pneumonia_classification_challenge_v1.ipynb

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       "  }\n",
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       "<div class=\"ansiout\"></div>"
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   "source": [
    "# Import necessary libraries \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import PIL\n",
    "import tensorflow as tf\n",
    "import pathlib\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
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    {
     "data": {
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       "<style scoped>\n",
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       "    display: block;\n",
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       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
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      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also autoinstruments tf.keras\n",
    "import mlflow.tensorflow\n",
    "mlflow.tensorflow.autolog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[5]: True</div>"
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       "data": "<div class=\"ansiout\">Out[5]: True</div>",
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   "source": [
    "# # Mount ADLS to read data source\n",
    "# application_id = \"PLEASE-ENTER-YOUR-OWNED-APPLICATION-ID-IN-AZURE-AD\"\n",
    "# directory_id = \"PLEASE-ENTER-YOUR-OWNED-AZURE-AD-TENANT-ID\"\n",
    "# scope_name = \"PLEASE-ENTER-YOUR-OWNED-AZURE-DATABRICKS-SECRET-SCOPE-NAME\"\n",
    "# key_name = \"PLEASE-ENTER-YOUR-OWNED-KEY-NAME-STORED-IN-SECRET-SCOPE\"\n",
    "# adls_container_name = \"PLEASE-ENTER-CONTAINER-NAME-IN-ADLS\"\n",
    "# adls_account_name = \"PLEASE-ENTER-ADLS-STORAGE-ACCOUNT-NAME\"\n",
    "# mount_point_name = \"PLEASE-ENTER-MOUNT-POINT-NAME-OF-ADLS\"\n",
    "\n",
    "# configs = {\"fs.azure.account.auth.type\": \"OAuth\",\n",
    "#            \"fs.azure.account.oauth.provider.type\": \"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider\",\n",
    "#            \"fs.azure.account.oauth2.client.id\": application_id,\n",
    "#            \"fs.azure.account.oauth2.client.secret\": dbutils.secrets.get(scope=scope_name,key=key_name),\n",
    "#            \"fs.azure.account.oauth2.client.endpoint\": \"https://login.microsoftonline.com/\"+directory_id+\"/oauth2/token\"}\n",
    "\n",
    "# dbutils.fs.mount(\n",
    "#   source = \"abfss://\"+adls_container_name+\"@\"+adls_account_name+\".dfs.core.windows.net/\",\n",
    "#   mount_point = \"/mnt/\"+mount_point_name,\n",
    "#   extra_configs = configs)"
   ]
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    {
     "data": {
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       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">/mnt/eason has been unmounted.\n",
       "Out[4]: True</div>"
      ]
     },
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       "data": "<div class=\"ansiout\">/mnt/eason has been unmounted.\nOut[4]: True</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unmount ADLS as needed\n",
    "# dbutils.fs.unmount(\"/mnt/eason\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
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       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[2]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/&#39;, name=&#39;models/&#39;, size=0),\n",
       " FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/&#39;, name=&#39;pneumonia_dataset/&#39;, size=0),\n",
       " FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/result_v1.csv&#39;, name=&#39;result_v1.csv&#39;, size=69970)]</div>"
      ]
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       "data": "<div class=\"ansiout\">Out[2]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/&#39;, name=&#39;models/&#39;, size=0),\n FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/&#39;, name=&#39;pneumonia_dataset/&#39;, size=0),\n FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/result_v1.csv&#39;, name=&#39;result_v1.csv&#39;, size=69970)]</div>",
       "datasetInfos": [],
       "metadata": {},
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       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List file in ADLS\n",
    "dbutils.fs.ls(\"/mnt/eason/pneumonia_classification_challenge/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Total = 2425\n",
       "Normal = 1280\n",
       "Pneumonia = 1145\n",
       "</div>"
      ]
     },
     "metadata": {
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       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Total = 2425\nNormal = 1280\nPneumonia = 1145\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check how many images in images folder\n",
    "image_data_dir = \"/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/train/\"\n",
    "image_data_dir = pathlib.Path(image_data_dir)\n",
    "image_count = len(list(image_data_dir.glob('*/*.png')))\n",
    "image_count_normal = len(list(image_data_dir.glob('normal/*.png')))\n",
    "image_count_pneumonia = len(list(image_data_dir.glob('pneumonia/*.png')))\n",
    "print(\"Total = \"+str(image_count))\n",
    "print(\"Normal = \"+str(image_count_normal))\n",
    "print(\"Pneumonia = \"+str(image_count_pneumonia))"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
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       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define some parameters for the loader:\n",
    "batch_size = 10\n",
    "img_height = 180\n",
    "img_width = 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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     "nuid": "79385a3c-40a2-4308-b40c-3617c494a3e4",
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    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
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       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Found 2425 files belonging to 2 classes.\n",
       "Using 1940 files for training.\n",
       "</div>"
      ]
     },
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       "arguments": {},
       "data": "<div class=\"ansiout\">Found 2425 files belonging to 2 classes.\nUsing 1940 files for training.\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use 80% of the images for training, and 20% for validation.\n",
    "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "  image_data_dir,\n",
    "  validation_split=0.2,\n",
    "  subset=\"training\",\n",
    "  seed=123,\n",
    "  image_size=(img_height, img_width),\n",
    "  batch_size=batch_size,\n",
    "  color_mode=\"grayscale\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Found 2425 files belonging to 2 classes.\n",
       "Using 485 files for validation.\n",
       "</div>"
      ]
     },
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       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Found 2425 files belonging to 2 classes.\nUsing 485 files for validation.\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's use 80% of the images for training, and 20% for validation.\n",
    "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "  image_data_dir,\n",
    "  validation_split=0.2,\n",
    "  subset=\"validation\",\n",
    "  seed=123,\n",
    "  image_size=(img_height, img_width),\n",
    "  batch_size=batch_size,\n",
    "  color_mode=\"grayscale\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 0,
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   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">[&#39;normal&#39;, &#39;pneumonia&#39;]\n",
       "</div>"
      ]
     },
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      "application/vnd.databricks.v1+output": {
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       "data": "<div class=\"ansiout\">[&#39;normal&#39;, &#39;pneumonia&#39;]\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# There are class names in the class_names attribute on these datasets. These correspond to the directory names in alphabetical order.\n",
    "class_names = train_ds.class_names\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
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       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
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       "datasetInfos": [],
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   "source": [
    "# Configure the dataset for performance\n",
    "# * Dataset.cache() keeps the images in memory after they're loaded off disk during the first epoch.\n",
    "#   This will ensure the dataset does not become a bottleneck while training your model.\n",
    "#   If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.\n",
    "# * Dataset.prefetch() overlaps data preprocessing and model execution while training.\n",
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
    "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
   ]
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       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the model\n",
    "num_classes = 2\n",
    "\n",
    "model = Sequential([\n",
    "  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 1)),\n",
    "  layers.Conv2D(32, 1, padding='same', activation='relu'),\n",
    "  layers.MaxPooling2D(),\n",
    "  layers.Conv2D(64, 1, padding='same', activation='relu'),\n",
    "  layers.MaxPooling2D(),\n",
    "  layers.Flatten(),\n",
    "  layers.Dense(128, activation='relu'),\n",
    "  layers.Dense(num_classes)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "db865952-638e-46e7-9056-914572bb5f89",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Model: &#34;sequential&#34;\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "rescaling (Rescaling)        (None, 180, 180, 1)       0         \n",
       "_________________________________________________________________\n",
       "conv2d (Conv2D)              (None, 180, 180, 32)      64        \n",
       "_________________________________________________________________\n",
       "max_pooling2d (MaxPooling2D) (None, 90, 90, 32)        0         \n",
       "_________________________________________________________________\n",
       "conv2d_1 (Conv2D)            (None, 90, 90, 64)        2112      \n",
       "_________________________________________________________________\n",
       "max_pooling2d_1 (MaxPooling2 (None, 45, 45, 64)        0         \n",
       "_________________________________________________________________\n",
       "flatten (Flatten)            (None, 129600)            0         \n",
       "_________________________________________________________________\n",
       "dense (Dense)                (None, 128)               16588928  \n",
       "_________________________________________________________________\n",
       "dense_1 (Dense)              (None, 2)                 258       \n",
       "=================================================================\n",
       "Total params: 16,591,362\n",
       "Trainable params: 16,591,362\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Model: &#34;sequential&#34;\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nrescaling (Rescaling)        (None, 180, 180, 1)       0         \n_________________________________________________________________\nconv2d (Conv2D)              (None, 180, 180, 32)      64        \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 90, 90, 32)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 90, 90, 64)        2112      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 45, 45, 64)        0         \n_________________________________________________________________\nflatten (Flatten)            (None, 129600)            0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               16588928  \n_________________________________________________________________\ndense_1 (Dense)              (None, 2)                 258       \n=================================================================\nTotal params: 16,591,362\nTrainable params: 16,591,362\nNon-trainable params: 0\n_________________________________________________________________\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "c5d93f33-ef53-4538-9097-3adeebd82888",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "319e6b21-c0e2-4d95-90ed-203babac304d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Epoch 1/20\n",
       "\r",
       "  1/194 [..............................] - ETA: 10:11:54 - loss: 0.6982 - accuracy: 0.4000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  2/194 [..............................] - ETA: 34s - loss: 1.8059 - accuracy: 0.4250     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  3/194 [..............................] - ETA: 34s - loss: 2.0029 - accuracy: 0.4167\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  4/194 [..............................] - ETA: 34s - loss: 1.9950 - accuracy: 0.4312\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  5/194 [..............................] - ETA: 35s - loss: 1.9584 - accuracy: 0.4410\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  6/194 [..............................] - ETA: 35s - loss: 1.9015 - accuracy: 0.4536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  7/194 [&gt;.............................] - ETA: 34s - loss: 1.8543 - accuracy: 0.4582\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  8/194 [&gt;.............................] - ETA: 33s - loss: 1.8035 - accuracy: 0.4650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  9/194 [&gt;.............................] - ETA: 33s - loss: 1.7539 - accuracy: 0.4726\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 10/194 [&gt;.............................] - ETA: 32s - loss: 1.7077 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 11/194 [&gt;.............................] - ETA: 32s - loss: 1.6653 - accuracy: 0.4794\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 12/194 [&gt;.............................] - ETA: 31s - loss: 1.6258 - accuracy: 0.4825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 13/194 [=&gt;............................] - ETA: 31s - loss: 1.5901 - accuracy: 0.4850\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 14/194 [=&gt;............................] - ETA: 31s - loss: 1.5574 - accuracy: 0.4871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 15/194 [=&gt;............................] - ETA: 31s - loss: 1.5273 - accuracy: 0.4889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 16/194 [=&gt;............................] - ETA: 31s - loss: 1.4991 - accuracy: 0.4911\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 17/194 [=&gt;............................] - ETA: 31s - loss: 1.4728 - accuracy: 0.4934\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 18/194 [=&gt;............................] - ETA: 31s - loss: 1.4483 - accuracy: 0.4956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 19/194 [=&gt;............................] - ETA: 30s - loss: 1.4252 - accuracy: 0.4980\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 20/194 [==&gt;...........................] - ETA: 30s - loss: 1.4037 - accuracy: 0.5004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 21/194 [==&gt;...........................] - ETA: 30s - loss: 1.3834 - accuracy: 0.5024\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 22/194 [==&gt;...........................] - ETA: 29s - loss: 1.3645 - accuracy: 0.5042\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 23/194 [==&gt;...........................] - ETA: 29s - loss: 1.3466 - accuracy: 0.5059\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 24/194 [==&gt;...........................] - ETA: 29s - loss: 1.3298 - accuracy: 0.5074\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 25/194 [==&gt;...........................] - ETA: 29s - loss: 1.3139 - accuracy: 0.5088\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 26/194 [===&gt;..........................] - ETA: 29s - loss: 1.2988 - accuracy: 0.5103\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 27/194 [===&gt;..........................] - ETA: 28s - loss: 1.2844 - accuracy: 0.5119\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 28/194 [===&gt;..........................] - ETA: 28s - loss: 1.2706 - accuracy: 0.5137\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 29/194 [===&gt;..........................] - ETA: 28s - loss: 1.2576 - accuracy: 0.5151\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 30/194 [===&gt;..........................] - ETA: 28s - loss: 1.2452 - accuracy: 0.5166\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 31/194 [===&gt;..........................] - ETA: 27s - loss: 1.2332 - accuracy: 0.5182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 32/194 [===&gt;..........................] - ETA: 27s - loss: 1.2218 - accuracy: 0.5196\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 33/194 [====&gt;.........................] - ETA: 27s - loss: 1.2112 - accuracy: 0.5207\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 34/194 [====&gt;.........................] - ETA: 27s - loss: 1.2011 - accuracy: 0.5217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 35/194 [====&gt;.........................] - ETA: 27s - loss: 1.1914 - accuracy: 0.5227\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 36/194 [====&gt;.........................] - ETA: 27s - loss: 1.1821 - accuracy: 0.5237\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 37/194 [====&gt;.........................] - ETA: 26s - loss: 1.1731 - accuracy: 0.5245\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 38/194 [====&gt;.........................] - ETA: 26s - loss: 1.1645 - accuracy: 0.5255\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 39/194 [=====&gt;........................] - ETA: 26s - loss: 1.1562 - accuracy: 0.5262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 40/194 [=====&gt;........................] - ETA: 26s - loss: 1.1483 - accuracy: 0.5271\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 41/194 [=====&gt;........................] - ETA: 26s - loss: 1.1406 - accuracy: 0.5280\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 42/194 [=====&gt;........................] - ETA: 25s - loss: 1.1333 - accuracy: 0.5287\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 43/194 [=====&gt;........................] - ETA: 25s - loss: 1.1262 - accuracy: 0.5294\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 44/194 [=====&gt;........................] - ETA: 25s - loss: 1.1193 - accuracy: 0.5302\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 45/194 [=====&gt;........................] - ETA: 25s - loss: 1.1127 - accuracy: 0.5309\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 46/194 [======&gt;.......................] - ETA: 25s - loss: 1.1063 - accuracy: 0.5316\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 47/194 [======&gt;.......................] - ETA: 25s - loss: 1.1000 - accuracy: 0.5324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 48/194 [======&gt;.......................] - ETA: 24s - loss: 1.0940 - accuracy: 0.5330\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 49/194 [======&gt;.......................] - ETA: 24s - loss: 1.0881 - accuracy: 0.5337\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 50/194 [======&gt;.......................] - ETA: 24s - loss: 1.0823 - accuracy: 0.5345\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 51/194 [======&gt;.......................] - ETA: 24s - loss: 1.0767 - accuracy: 0.5354\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 52/194 [=======&gt;......................] - ETA: 24s - loss: 1.0713 - accuracy: 0.5361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 53/194 [=======&gt;......................] - ETA: 24s - loss: 1.0661 - accuracy: 0.5368\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 54/194 [=======&gt;......................] - ETA: 23s - loss: 1.0610 - accuracy: 0.5374\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 55/194 [=======&gt;......................] - ETA: 23s - loss: 1.0561 - accuracy: 0.5381\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 56/194 [=======&gt;......................] - ETA: 23s - loss: 1.0513 - accuracy: 0.5387\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 57/194 [=======&gt;......................] - ETA: 23s - loss: 1.0466 - accuracy: 0.5392\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 58/194 [=======&gt;......................] - ETA: 23s - loss: 1.0421 - accuracy: 0.5398\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 59/194 [========&gt;.....................] - ETA: 22s - loss: 1.0377 - accuracy: 0.5404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 60/194 [========&gt;.....................] - ETA: 22s - loss: 1.0333 - accuracy: 0.5411\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 61/194 [========&gt;.....................] - ETA: 22s - loss: 1.0291 - accuracy: 0.5417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 62/194 [========&gt;.....................] - ETA: 22s - loss: 1.0250 - accuracy: 0.5424\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 63/194 [========&gt;.....................] - ETA: 22s - loss: 1.0209 - accuracy: 0.5431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 64/194 [========&gt;.....................] - ETA: 22s - loss: 1.0170 - accuracy: 0.5437\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 65/194 [=========&gt;....................] - ETA: 21s - loss: 1.0132 - accuracy: 0.5443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 66/194 [=========&gt;....................] - ETA: 21s - loss: 1.0095 - accuracy: 0.5449\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 67/194 [=========&gt;....................] - ETA: 21s - loss: 1.0059 - accuracy: 0.5454\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 68/194 [=========&gt;....................] - ETA: 21s - loss: 1.0024 - accuracy: 0.5459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 69/194 [=========&gt;....................] - ETA: 21s - loss: 0.9990 - accuracy: 0.5464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 70/194 [=========&gt;....................] - ETA: 21s - loss: 0.9957 - accuracy: 0.5468\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 71/194 [=========&gt;....................] - ETA: 20s - loss: 0.9924 - accuracy: 0.5473\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 72/194 [==========&gt;...................] - ETA: 20s - loss: 0.9892 - accuracy: 0.5477\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 73/194 [==========&gt;...................] - ETA: 20s - loss: 0.9861 - accuracy: 0.5481\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 74/194 [==========&gt;...................] - ETA: 20s - loss: 0.9831 - accuracy: 0.5484\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 75/194 [==========&gt;...................] - ETA: 20s - loss: 0.9802 - accuracy: 0.5487\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 76/194 [==========&gt;...................] - ETA: 20s - loss: 0.9773 - accuracy: 0.5491\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 77/194 [==========&gt;...................] - ETA: 19s - loss: 0.9744 - accuracy: 0.5495\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 78/194 [===========&gt;..................] - ETA: 19s - loss: 0.9716 - accuracy: 0.5499\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 79/194 [===========&gt;..................] - ETA: 19s - loss: 0.9688 - accuracy: 0.5503\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 80/194 [===========&gt;..................] - ETA: 19s - loss: 0.9661 - accuracy: 0.5507\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 81/194 [===========&gt;..................] - ETA: 19s - loss: 0.9635 - accuracy: 0.5511\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 82/194 [===========&gt;..................] - ETA: 18s - loss: 0.9609 - accuracy: 0.5515\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 83/194 [===========&gt;..................] - ETA: 18s - loss: 0.9583 - accuracy: 0.5519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 84/194 [===========&gt;..................] - ETA: 18s - loss: 0.9558 - accuracy: 0.5522\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 85/194 [============&gt;.................] - ETA: 18s - loss: 0.9533 - accuracy: 0.5526\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 86/194 [============&gt;.................] - ETA: 18s - loss: 0.9510 - accuracy: 0.5529\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 87/194 [============&gt;.................] - ETA: 18s - loss: 0.9486 - accuracy: 0.5533\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 88/194 [============&gt;.................] - ETA: 17s - loss: 0.9463 - accuracy: 0.5536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 89/194 [============&gt;.................] - ETA: 17s - loss: 0.9440 - accuracy: 0.5540\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 90/194 [============&gt;.................] - ETA: 17s - loss: 0.9418 - accuracy: 0.5544\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 91/194 [=============&gt;................] - ETA: 17s - loss: 0.9396 - accuracy: 0.5547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 92/194 [=============&gt;................] - ETA: 17s - loss: 0.9374 - accuracy: 0.5550\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 93/194 [=============&gt;................] - ETA: 17s - loss: 0.9353 - accuracy: 0.5553\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 94/194 [=============&gt;................] - ETA: 16s - loss: 0.9332 - accuracy: 0.5557\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 95/194 [=============&gt;................] - ETA: 16s - loss: 0.9312 - accuracy: 0.5560\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 96/194 [=============&gt;................] - ETA: 16s - loss: 0.9292 - accuracy: 0.5563\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 97/194 [==============&gt;...............] - ETA: 16s - loss: 0.9272 - accuracy: 0.5566\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 98/194 [==============&gt;...............] - ETA: 16s - loss: 0.9253 - accuracy: 0.5569\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 99/194 [==============&gt;...............] - ETA: 16s - loss: 0.9234 - accuracy: 0.5572\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "100/194 [==============&gt;...............] - ETA: 15s - loss: 0.9215 - accuracy: 0.5575\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "101/194 [==============&gt;...............] - ETA: 15s - loss: 0.9197 - accuracy: 0.5578\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "102/194 [==============&gt;...............] - ETA: 15s - loss: 0.9179 - accuracy: 0.5581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "103/194 [==============&gt;...............] - ETA: 15s - loss: 0.9161 - accuracy: 0.5583\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "104/194 [===============&gt;..............] - ETA: 15s - loss: 0.9144 - accuracy: 0.5586\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "105/194 [===============&gt;..............] - ETA: 15s - loss: 0.9127 - accuracy: 0.5588\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "106/194 [===============&gt;..............] - ETA: 14s - loss: 0.9110 - accuracy: 0.5590\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "107/194 [===============&gt;..............] - ETA: 14s - loss: 0.9094 - accuracy: 0.5592\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "108/194 [===============&gt;..............] - ETA: 14s - loss: 0.9077 - accuracy: 0.5594\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "109/194 [===============&gt;..............] - ETA: 14s - loss: 0.9062 - accuracy: 0.5596\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "110/194 [================&gt;.............] - ETA: 14s - loss: 0.9046 - accuracy: 0.5598\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "111/194 [================&gt;.............] - ETA: 13s - loss: 0.9031 - accuracy: 0.5599\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "112/194 [================&gt;.............] - ETA: 13s - loss: 0.9015 - accuracy: 0.5601\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "113/194 [================&gt;.............] - ETA: 13s - loss: 0.9000 - accuracy: 0.5602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "114/194 [================&gt;.............] - ETA: 13s - loss: 0.8986 - accuracy: 0.5604\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "115/194 [================&gt;.............] - ETA: 13s - loss: 0.8971 - accuracy: 0.5605\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "116/194 [================&gt;.............] - ETA: 13s - loss: 0.8957 - accuracy: 0.5607\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "117/194 [=================&gt;............] - ETA: 12s - loss: 0.8943 - accuracy: 0.5608\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "118/194 [=================&gt;............] - ETA: 12s - loss: 0.8929 - accuracy: 0.5609\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "119/194 [=================&gt;............] - ETA: 12s - loss: 0.8915 - accuracy: 0.5610\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "120/194 [=================&gt;............] - ETA: 12s - loss: 0.8902 - accuracy: 0.5611\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "121/194 [=================&gt;............] - ETA: 12s - loss: 0.8888 - accuracy: 0.5612\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/194 [=================&gt;............] - ETA: 12s - loss: 0.8875 - accuracy: 0.5613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "123/194 [==================&gt;...........] - ETA: 11s - loss: 0.8862 - accuracy: 0.5614\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "124/194 [==================&gt;...........] - ETA: 11s - loss: 0.8849 - accuracy: 0.5616\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "125/194 [==================&gt;...........] - ETA: 11s - loss: 0.8837 - accuracy: 0.5617\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "126/194 [==================&gt;...........] - ETA: 11s - loss: 0.8824 - accuracy: 0.5618\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "127/194 [==================&gt;...........] - ETA: 11s - loss: 0.8812 - accuracy: 0.5619\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "128/194 [==================&gt;...........] - ETA: 11s - loss: 0.8800 - accuracy: 0.5620\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "129/194 [==================&gt;...........] - ETA: 10s - loss: 0.8787 - accuracy: 0.5622\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "130/194 [===================&gt;..........] - ETA: 10s - loss: 0.8775 - accuracy: 0.5623\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "131/194 [===================&gt;..........] - ETA: 10s - loss: 0.8764 - accuracy: 0.5625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "132/194 [===================&gt;..........] - ETA: 10s - loss: 0.8752 - accuracy: 0.5626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "133/194 [===================&gt;..........] - ETA: 10s - loss: 0.8740 - accuracy: 0.5627\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "134/194 [===================&gt;..........] - ETA: 10s - loss: 0.8729 - accuracy: 0.5629\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "135/194 [===================&gt;..........] - ETA: 9s - loss: 0.8718 - accuracy: 0.5630 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "136/194 [====================&gt;.........] - ETA: 9s - loss: 0.8707 - accuracy: 0.5631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "137/194 [====================&gt;.........] - ETA: 9s - loss: 0.8696 - accuracy: 0.5632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "138/194 [====================&gt;.........] - ETA: 9s - loss: 0.8685 - accuracy: 0.5634\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "139/194 [====================&gt;.........] - ETA: 9s - loss: 0.8674 - accuracy: 0.5635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "140/194 [====================&gt;.........] - ETA: 9s - loss: 0.8663 - accuracy: 0.5637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "141/194 [====================&gt;.........] - ETA: 8s - loss: 0.8653 - accuracy: 0.5638\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "142/194 [====================&gt;.........] - ETA: 8s - loss: 0.8643 - accuracy: 0.5639\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "143/194 [=====================&gt;........] - ETA: 8s - loss: 0.8632 - accuracy: 0.5641\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "144/194 [=====================&gt;........] - ETA: 8s - loss: 0.8622 - accuracy: 0.5642\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "145/194 [=====================&gt;........] - ETA: 8s - loss: 0.8612 - accuracy: 0.5643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "146/194 [=====================&gt;........] - ETA: 8s - loss: 0.8602 - accuracy: 0.5644\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "147/194 [=====================&gt;........] - ETA: 7s - loss: 0.8593 - accuracy: 0.5646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "148/194 [=====================&gt;........] - ETA: 7s - loss: 0.8583 - accuracy: 0.5647\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "149/194 [======================&gt;.......] - ETA: 7s - loss: 0.8573 - accuracy: 0.5648\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "150/194 [======================&gt;.......] - ETA: 7s - loss: 0.8564 - accuracy: 0.5650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "151/194 [======================&gt;.......] - ETA: 7s - loss: 0.8554 - accuracy: 0.5651\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "152/194 [======================&gt;.......] - ETA: 7s - loss: 0.8545 - accuracy: 0.5653\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "153/194 [======================&gt;.......] - ETA: 6s - loss: 0.8535 - accuracy: 0.5655\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "154/194 [======================&gt;.......] - ETA: 6s - loss: 0.8526 - accuracy: 0.5656\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "155/194 [======================&gt;.......] - ETA: 6s - loss: 0.8517 - accuracy: 0.5658\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "156/194 [=======================&gt;......] - ETA: 6s - loss: 0.8508 - accuracy: 0.5660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "157/194 [=======================&gt;......] - ETA: 6s - loss: 0.8499 - accuracy: 0.5661\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "158/194 [=======================&gt;......] - ETA: 6s - loss: 0.8490 - accuracy: 0.5663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "159/194 [=======================&gt;......] - ETA: 5s - loss: 0.8481 - accuracy: 0.5664\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "160/194 [=======================&gt;......] - ETA: 5s - loss: 0.8473 - accuracy: 0.5666\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "161/194 [=======================&gt;......] - ETA: 5s - loss: 0.8464 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "162/194 [========================&gt;.....] - ETA: 5s - loss: 0.8455 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "163/194 [========================&gt;.....] - ETA: 5s - loss: 0.8447 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "164/194 [========================&gt;.....] - ETA: 5s - loss: 0.8439 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "165/194 [========================&gt;.....] - ETA: 4s - loss: 0.8430 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "166/194 [========================&gt;.....] - ETA: 4s - loss: 0.8422 - accuracy: 0.5675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "167/194 [========================&gt;.....] - ETA: 4s - loss: 0.8414 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "168/194 [========================&gt;.....] - ETA: 4s - loss: 0.8406 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "169/194 [=========================&gt;....] - ETA: 4s - loss: 0.8398 - accuracy: 0.5680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "170/194 [=========================&gt;....] - ETA: 4s - loss: 0.8390 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "171/194 [=========================&gt;....] - ETA: 3s - loss: 0.8382 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "172/194 [=========================&gt;....] - ETA: 3s - loss: 0.8374 - accuracy: 0.5685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "173/194 [=========================&gt;....] - ETA: 3s - loss: 0.8366 - accuracy: 0.5687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "174/194 [=========================&gt;....] - ETA: 3s - loss: 0.8358 - accuracy: 0.5689\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "175/194 [==========================&gt;...] - ETA: 3s - loss: 0.8350 - accuracy: 0.5691\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "176/194 [==========================&gt;...] - ETA: 3s - loss: 0.8343 - accuracy: 0.5692\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "177/194 [==========================&gt;...] - ETA: 2s - loss: 0.8335 - accuracy: 0.5694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "178/194 [==========================&gt;...] - ETA: 2s - loss: 0.8328 - accuracy: 0.5696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "179/194 [==========================&gt;...] - ETA: 2s - loss: 0.8320 - accuracy: 0.5698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "180/194 [==========================&gt;...] - ETA: 2s - loss: 0.8313 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "181/194 [==========================&gt;...] - ETA: 2s - loss: 0.8305 - accuracy: 0.5701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "182/194 [===========================&gt;..] - ETA: 2s - loss: 0.8298 - accuracy: 0.5703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "183/194 [===========================&gt;..] - ETA: 1s - loss: 0.8291 - accuracy: 0.5705\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "184/194 [===========================&gt;..] - ETA: 1s - loss: 0.8284 - accuracy: 0.5706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "185/194 [===========================&gt;..] - ETA: 1s - loss: 0.8277 - accuracy: 0.5708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "186/194 [===========================&gt;..] - ETA: 1s - loss: 0.8270 - accuracy: 0.5710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "187/194 [===========================&gt;..] - ETA: 1s - loss: 0.8263 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "188/194 [============================&gt;.] - ETA: 1s - loss: 0.8256 - accuracy: 0.5714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "189/194 [============================&gt;.] - ETA: 0s - loss: 0.8249 - accuracy: 0.5715\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "190/194 [============================&gt;.] - ETA: 0s - loss: 0.8242 - accuracy: 0.5717\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "191/194 [============================&gt;.] - ETA: 0s - loss: 0.8236 - accuracy: 0.5719\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "192/194 [============================&gt;.] - ETA: 0s - loss: 0.8229 - accuracy: 0.5721\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "193/194 [============================&gt;.] - ETA: 0s - loss: 0.8222 - accuracy: 0.5722\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "194/194 [==============================] - ETA: 0s - loss: 0.8216 - accuracy: 0.5724\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "194/194 [==============================] - 275s 438ms/step - loss: 0.8209 - accuracy: 0.5726 - val_loss: 0.6330 - val_accuracy: 0.6495\n",
       "\n",
       "*** WARNING: skipped 622966 bytes of output ***\n",
       "\n",
       "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "150/194 [======================&gt;.......] - ETA: 7s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "151/194 [======================&gt;.......] - ETA: 7s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "152/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "153/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "154/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "155/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "156/194 [=======================&gt;......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "157/194 [=======================&gt;......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "158/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "159/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "160/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "161/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "162/194 [========================&gt;.....] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "163/194 [========================&gt;.....] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "164/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "165/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "166/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "167/194 [========================&gt;.....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "168/194 [========================&gt;.....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8213\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "169/194 [=========================&gt;....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8213\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "170/194 [=========================&gt;....] - ETA: 3s - loss: 0.3901 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "171/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "172/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "173/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8215\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "174/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8215\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "175/194 [==========================&gt;...] - ETA: 3s - loss: 0.3900 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "176/194 [==========================&gt;...] - ETA: 2s - loss: 0.3900 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "177/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "178/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "179/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "180/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "181/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "182/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "183/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "184/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8219\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "185/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8219\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "186/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8220\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "187/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8220\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "188/194 [============================&gt;.] - ETA: 0s - loss: 0.3896 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "189/194 [============================&gt;.] - ETA: 0s - loss: 0.3896 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "190/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "191/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "192/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "193/194 [============================&gt;.] - ETA: 0s - loss: 0.3894 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "194/194 [==============================] - ETA: 0s - loss: 0.3894 - accuracy: 0.8223\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "194/194 [==============================] - 34s 174ms/step - loss: 0.3894 - accuracy: 0.8223 - val_loss: 0.7644 - val_accuracy: 0.6536\n",
       "INFO:tensorflow:Assets written to: /tmp/tmpd8m3sk8_/model/data/model/assets\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Epoch 1/20\n\r  1/194 [..............................] - ETA: 10:11:54 - loss: 0.6982 - accuracy: 0.4000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  2/194 [..............................] - ETA: 34s - loss: 1.8059 - accuracy: 0.4250     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  3/194 [..............................] - ETA: 34s - loss: 2.0029 - accuracy: 0.4167\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  4/194 [..............................] - ETA: 34s - loss: 1.9950 - accuracy: 0.4312\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  5/194 [..............................] - ETA: 35s - loss: 1.9584 - accuracy: 0.4410\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  6/194 [..............................] - ETA: 35s - loss: 1.9015 - accuracy: 0.4536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  7/194 [&gt;.............................] - ETA: 34s - loss: 1.8543 - accuracy: 0.4582\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  8/194 [&gt;.............................] - ETA: 33s - loss: 1.8035 - accuracy: 0.4650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  9/194 [&gt;.............................] - ETA: 33s - loss: 1.7539 - accuracy: 0.4726\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 10/194 [&gt;.............................] - ETA: 32s - loss: 1.7077 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 11/194 [&gt;.............................] - ETA: 32s - loss: 1.6653 - accuracy: 0.4794\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 12/194 [&gt;.............................] - ETA: 31s - loss: 1.6258 - accuracy: 0.4825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 13/194 [=&gt;............................] - ETA: 31s - loss: 1.5901 - accuracy: 0.4850\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 14/194 [=&gt;............................] - ETA: 31s - loss: 1.5574 - accuracy: 0.4871\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 15/194 [=&gt;............................] - ETA: 31s - loss: 1.5273 - accuracy: 0.4889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 16/194 [=&gt;............................] - ETA: 31s - loss: 1.4991 - accuracy: 0.4911\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 17/194 [=&gt;............................] - ETA: 31s - loss: 1.4728 - accuracy: 0.4934\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 18/194 [=&gt;............................] - ETA: 31s - loss: 1.4483 - accuracy: 0.4956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 19/194 [=&gt;............................] - ETA: 30s - loss: 1.4252 - accuracy: 0.4980\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 20/194 [==&gt;...........................] - ETA: 30s - loss: 1.4037 - accuracy: 0.5004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 21/194 [==&gt;...........................] - ETA: 30s - loss: 1.3834 - accuracy: 0.5024\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 22/194 [==&gt;...........................] - ETA: 29s - loss: 1.3645 - accuracy: 0.5042\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 23/194 [==&gt;...........................] - ETA: 29s - loss: 1.3466 - accuracy: 0.5059\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 24/194 [==&gt;...........................] - ETA: 29s - loss: 1.3298 - accuracy: 0.5074\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 25/194 [==&gt;...........................] - ETA: 29s - loss: 1.3139 - accuracy: 0.5088\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 26/194 [===&gt;..........................] - ETA: 29s - loss: 1.2988 - accuracy: 0.5103\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 27/194 [===&gt;..........................] - ETA: 28s - loss: 1.2844 - accuracy: 0.5119\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 28/194 [===&gt;..........................] - ETA: 28s - loss: 1.2706 - accuracy: 0.5137\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 29/194 [===&gt;..........................] - ETA: 28s - loss: 1.2576 - accuracy: 0.5151\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 30/194 [===&gt;..........................] - ETA: 28s - loss: 1.2452 - accuracy: 0.5166\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 31/194 [===&gt;..........................] - ETA: 27s - loss: 1.2332 - accuracy: 0.5182\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 32/194 [===&gt;..........................] - ETA: 27s - loss: 1.2218 - accuracy: 0.5196\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 33/194 [====&gt;.........................] - ETA: 27s - loss: 1.2112 - accuracy: 0.5207\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 34/194 [====&gt;.........................] - ETA: 27s - loss: 1.2011 - accuracy: 0.5217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 35/194 [====&gt;.........................] - ETA: 27s - loss: 1.1914 - accuracy: 0.5227\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 36/194 [====&gt;.........................] - ETA: 27s - loss: 1.1821 - accuracy: 0.5237\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 37/194 [====&gt;.........................] - ETA: 26s - loss: 1.1731 - accuracy: 0.5245\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 38/194 [====&gt;.........................] - ETA: 26s - loss: 1.1645 - accuracy: 0.5255\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 39/194 [=====&gt;........................] - ETA: 26s - loss: 1.1562 - accuracy: 0.5262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 40/194 [=====&gt;........................] - ETA: 26s - loss: 1.1483 - accuracy: 0.5271\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 41/194 [=====&gt;........................] - ETA: 26s - loss: 1.1406 - accuracy: 0.5280\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 42/194 [=====&gt;........................] - ETA: 25s - loss: 1.1333 - accuracy: 0.5287\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 43/194 [=====&gt;........................] - ETA: 25s - loss: 1.1262 - accuracy: 0.5294\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 44/194 [=====&gt;........................] - ETA: 25s - loss: 1.1193 - accuracy: 0.5302\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 45/194 [=====&gt;........................] - ETA: 25s - loss: 1.1127 - accuracy: 0.5309\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 46/194 [======&gt;.......................] - ETA: 25s - loss: 1.1063 - accuracy: 0.5316\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 47/194 [======&gt;.......................] - ETA: 25s - loss: 1.1000 - accuracy: 0.5324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 48/194 [======&gt;.......................] - ETA: 24s - loss: 1.0940 - accuracy: 0.5330\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 49/194 [======&gt;.......................] - ETA: 24s - loss: 1.0881 - accuracy: 0.5337\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 50/194 [======&gt;.......................] - ETA: 24s - loss: 1.0823 - accuracy: 0.5345\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 51/194 [======&gt;.......................] - ETA: 24s - loss: 1.0767 - accuracy: 0.5354\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 52/194 [=======&gt;......................] - ETA: 24s - loss: 1.0713 - accuracy: 0.5361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 53/194 [=======&gt;......................] - ETA: 24s - loss: 1.0661 - accuracy: 0.5368\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 54/194 [=======&gt;......................] - ETA: 23s - loss: 1.0610 - accuracy: 0.5374\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 55/194 [=======&gt;......................] - ETA: 23s - loss: 1.0561 - accuracy: 0.5381\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 56/194 [=======&gt;......................] - ETA: 23s - loss: 1.0513 - accuracy: 0.5387\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 57/194 [=======&gt;......................] - ETA: 23s - loss: 1.0466 - accuracy: 0.5392\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 58/194 [=======&gt;......................] - ETA: 23s - loss: 1.0421 - accuracy: 0.5398\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 59/194 [========&gt;.....................] - ETA: 22s - loss: 1.0377 - accuracy: 0.5404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 60/194 [========&gt;.....................] - ETA: 22s - loss: 1.0333 - accuracy: 0.5411\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 61/194 [========&gt;.....................] - ETA: 22s - loss: 1.0291 - accuracy: 0.5417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 62/194 [========&gt;.....................] - ETA: 22s - loss: 1.0250 - accuracy: 0.5424\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 63/194 [========&gt;.....................] - ETA: 22s - loss: 1.0209 - accuracy: 0.5431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 64/194 [========&gt;.....................] - ETA: 22s - loss: 1.0170 - accuracy: 0.5437\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 65/194 [=========&gt;....................] - ETA: 21s - loss: 1.0132 - accuracy: 0.5443\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 66/194 [=========&gt;....................] - ETA: 21s - loss: 1.0095 - accuracy: 0.5449\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 67/194 [=========&gt;....................] - ETA: 21s - loss: 1.0059 - accuracy: 0.5454\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 68/194 [=========&gt;....................] - ETA: 21s - loss: 1.0024 - accuracy: 0.5459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 69/194 [=========&gt;....................] - ETA: 21s - loss: 0.9990 - accuracy: 0.5464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 70/194 [=========&gt;....................] - ETA: 21s - loss: 0.9957 - accuracy: 0.5468\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 71/194 [=========&gt;....................] - ETA: 20s - loss: 0.9924 - accuracy: 0.5473\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 72/194 [==========&gt;...................] - ETA: 20s - loss: 0.9892 - accuracy: 0.5477\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 73/194 [==========&gt;...................] - ETA: 20s - loss: 0.9861 - accuracy: 0.5481\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 74/194 [==========&gt;...................] - ETA: 20s - loss: 0.9831 - accuracy: 0.5484\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 75/194 [==========&gt;...................] - ETA: 20s - loss: 0.9802 - accuracy: 0.5487\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 76/194 [==========&gt;...................] - ETA: 20s - loss: 0.9773 - accuracy: 0.5491\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 77/194 [==========&gt;...................] - ETA: 19s - loss: 0.9744 - accuracy: 0.5495\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 78/194 [===========&gt;..................] - ETA: 19s - loss: 0.9716 - accuracy: 0.5499\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 79/194 [===========&gt;..................] - ETA: 19s - loss: 0.9688 - accuracy: 0.5503\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 80/194 [===========&gt;..................] - ETA: 19s - loss: 0.9661 - accuracy: 0.5507\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 81/194 [===========&gt;..................] - ETA: 19s - loss: 0.9635 - accuracy: 0.5511\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 82/194 [===========&gt;..................] - ETA: 18s - loss: 0.9609 - accuracy: 0.5515\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 83/194 [===========&gt;..................] - ETA: 18s - loss: 0.9583 - accuracy: 0.5519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 84/194 [===========&gt;..................] - ETA: 18s - loss: 0.9558 - accuracy: 0.5522\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 85/194 [============&gt;.................] - ETA: 18s - loss: 0.9533 - accuracy: 0.5526\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 86/194 [============&gt;.................] - ETA: 18s - loss: 0.9510 - accuracy: 0.5529\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 87/194 [============&gt;.................] - ETA: 18s - loss: 0.9486 - accuracy: 0.5533\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 88/194 [============&gt;.................] - ETA: 17s - loss: 0.9463 - accuracy: 0.5536\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 89/194 [============&gt;.................] - ETA: 17s - loss: 0.9440 - accuracy: 0.5540\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 90/194 [============&gt;.................] - ETA: 17s - loss: 0.9418 - accuracy: 0.5544\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 91/194 [=============&gt;................] - ETA: 17s - loss: 0.9396 - accuracy: 0.5547\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 92/194 [=============&gt;................] - ETA: 17s - loss: 0.9374 - accuracy: 0.5550\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 93/194 [=============&gt;................] - ETA: 17s - loss: 0.9353 - accuracy: 0.5553\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 94/194 [=============&gt;................] - ETA: 16s - loss: 0.9332 - accuracy: 0.5557\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 95/194 [=============&gt;................] - ETA: 16s - loss: 0.9312 - accuracy: 0.5560\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 96/194 [=============&gt;................] - ETA: 16s - loss: 0.9292 - accuracy: 0.5563\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 97/194 [==============&gt;...............] - ETA: 16s - loss: 0.9272 - accuracy: 0.5566\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 98/194 [==============&gt;...............] - ETA: 16s - loss: 0.9253 - accuracy: 0.5569\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 99/194 [==============&gt;...............] - ETA: 16s - loss: 0.9234 - accuracy: 0.5572\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r100/194 [==============&gt;...............] - ETA: 15s - loss: 0.9215 - accuracy: 0.5575\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r101/194 [==============&gt;...............] - ETA: 15s - loss: 0.9197 - accuracy: 0.5578\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r102/194 [==============&gt;...............] - ETA: 15s - loss: 0.9179 - accuracy: 0.5581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r103/194 [==============&gt;...............] - ETA: 15s - loss: 0.9161 - accuracy: 0.5583\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r104/194 [===============&gt;..............] - ETA: 15s - loss: 0.9144 - accuracy: 0.5586\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r105/194 [===============&gt;..............] - ETA: 15s - loss: 0.9127 - accuracy: 0.5588\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r106/194 [===============&gt;..............] - ETA: 14s - loss: 0.9110 - accuracy: 0.5590\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r107/194 [===============&gt;..............] - ETA: 14s - loss: 0.9094 - accuracy: 0.5592\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r108/194 [===============&gt;..............] - ETA: 14s - loss: 0.9077 - accuracy: 0.5594\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r109/194 [===============&gt;..............] - ETA: 14s - loss: 0.9062 - accuracy: 0.5596\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r110/194 [================&gt;.............] - ETA: 14s - loss: 0.9046 - accuracy: 0.5598\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r111/194 [================&gt;.............] - ETA: 13s - loss: 0.9031 - accuracy: 0.5599\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r112/194 [================&gt;.............] - ETA: 13s - loss: 0.9015 - accuracy: 0.5601\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r113/194 [================&gt;.............] - ETA: 13s - loss: 0.9000 - accuracy: 0.5602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r114/194 [================&gt;.............] - ETA: 13s - loss: 0.8986 - accuracy: 0.5604\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r115/194 [================&gt;.............] - ETA: 13s - loss: 0.8971 - accuracy: 0.5605\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r116/194 [================&gt;.............] - ETA: 13s - loss: 0.8957 - accuracy: 0.5607\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r117/194 [=================&gt;............] - ETA: 12s - loss: 0.8943 - accuracy: 0.5608\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r118/194 [=================&gt;............] - ETA: 12s - loss: 0.8929 - accuracy: 0.5609\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r119/194 [=================&gt;............] - ETA: 12s - loss: 0.8915 - accuracy: 0.5610\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r120/194 [=================&gt;............] - ETA: 12s - loss: 0.8902 - accuracy: 0.5611\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r121/194 [=================&gt;............] - ETA: 12s - loss: 0.8888 - accuracy: 0.5612\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/194 [=================&gt;............] - ETA: 12s - loss: 0.8875 - accuracy: 0.5613\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r123/194 [==================&gt;...........] - ETA: 11s - loss: 0.8862 - accuracy: 0.5614\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r124/194 [==================&gt;...........] - ETA: 11s - loss: 0.8849 - accuracy: 0.5616\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r125/194 [==================&gt;...........] - ETA: 11s - loss: 0.8837 - accuracy: 0.5617\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r126/194 [==================&gt;...........] - ETA: 11s - loss: 0.8824 - accuracy: 0.5618\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r127/194 [==================&gt;...........] - ETA: 11s - loss: 0.8812 - accuracy: 0.5619\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r128/194 [==================&gt;...........] - ETA: 11s - loss: 0.8800 - accuracy: 0.5620\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r129/194 [==================&gt;...........] - ETA: 10s - loss: 0.8787 - accuracy: 0.5622\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r130/194 [===================&gt;..........] - ETA: 10s - loss: 0.8775 - accuracy: 0.5623\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r131/194 [===================&gt;..........] - ETA: 10s - loss: 0.8764 - accuracy: 0.5625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r132/194 [===================&gt;..........] - ETA: 10s - loss: 0.8752 - accuracy: 0.5626\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r133/194 [===================&gt;..........] - ETA: 10s - loss: 0.8740 - accuracy: 0.5627\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r134/194 [===================&gt;..........] - ETA: 10s - loss: 0.8729 - accuracy: 0.5629\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r135/194 [===================&gt;..........] - ETA: 9s - loss: 0.8718 - accuracy: 0.5630 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r136/194 [====================&gt;.........] - ETA: 9s - loss: 0.8707 - accuracy: 0.5631\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r137/194 [====================&gt;.........] - ETA: 9s - loss: 0.8696 - accuracy: 0.5632\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r138/194 [====================&gt;.........] - ETA: 9s - loss: 0.8685 - accuracy: 0.5634\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r139/194 [====================&gt;.........] - ETA: 9s - loss: 0.8674 - accuracy: 0.5635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r140/194 [====================&gt;.........] - ETA: 9s - loss: 0.8663 - accuracy: 0.5637\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r141/194 [====================&gt;.........] - ETA: 8s - loss: 0.8653 - accuracy: 0.5638\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r142/194 [====================&gt;.........] - ETA: 8s - loss: 0.8643 - accuracy: 0.5639\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r143/194 [=====================&gt;........] - ETA: 8s - loss: 0.8632 - accuracy: 0.5641\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r144/194 [=====================&gt;........] - ETA: 8s - loss: 0.8622 - accuracy: 0.5642\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r145/194 [=====================&gt;........] - ETA: 8s - loss: 0.8612 - accuracy: 0.5643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r146/194 [=====================&gt;........] - ETA: 8s - loss: 0.8602 - accuracy: 0.5644\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r147/194 [=====================&gt;........] - ETA: 7s - loss: 0.8593 - accuracy: 0.5646\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r148/194 [=====================&gt;........] - ETA: 7s - loss: 0.8583 - accuracy: 0.5647\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r149/194 [======================&gt;.......] - ETA: 7s - loss: 0.8573 - accuracy: 0.5648\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/194 [======================&gt;.......] - ETA: 7s - loss: 0.8564 - accuracy: 0.5650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r151/194 [======================&gt;.......] - ETA: 7s - loss: 0.8554 - accuracy: 0.5651\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r152/194 [======================&gt;.......] - ETA: 7s - loss: 0.8545 - accuracy: 0.5653\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r153/194 [======================&gt;.......] - ETA: 6s - loss: 0.8535 - accuracy: 0.5655\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r154/194 [======================&gt;.......] - ETA: 6s - loss: 0.8526 - accuracy: 0.5656\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r155/194 [======================&gt;.......] - ETA: 6s - loss: 0.8517 - accuracy: 0.5658\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r156/194 [=======================&gt;......] - ETA: 6s - loss: 0.8508 - accuracy: 0.5660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r157/194 [=======================&gt;......] - ETA: 6s - loss: 0.8499 - accuracy: 0.5661\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r158/194 [=======================&gt;......] - ETA: 6s - loss: 0.8490 - accuracy: 0.5663\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r159/194 [=======================&gt;......] - ETA: 5s - loss: 0.8481 - accuracy: 0.5664\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/194 [=======================&gt;......] - ETA: 5s - loss: 0.8473 - accuracy: 0.5666\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r161/194 [=======================&gt;......] - ETA: 5s - loss: 0.8464 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r162/194 [========================&gt;.....] - ETA: 5s - loss: 0.8455 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r163/194 [========================&gt;.....] - ETA: 5s - loss: 0.8447 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r164/194 [========================&gt;.....] - ETA: 5s - loss: 0.8439 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r165/194 [========================&gt;.....] - ETA: 4s - loss: 0.8430 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r166/194 [========================&gt;.....] - ETA: 4s - loss: 0.8422 - accuracy: 0.5675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r167/194 [========================&gt;.....] - ETA: 4s - loss: 0.8414 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r168/194 [========================&gt;.....] - ETA: 4s - loss: 0.8406 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r169/194 [=========================&gt;....] - ETA: 4s - loss: 0.8398 - accuracy: 0.5680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r170/194 [=========================&gt;....] - ETA: 4s - loss: 0.8390 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r171/194 [=========================&gt;....] - ETA: 3s - loss: 0.8382 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r172/194 [=========================&gt;....] - ETA: 3s - loss: 0.8374 - accuracy: 0.5685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r173/194 [=========================&gt;....] - ETA: 3s - loss: 0.8366 - accuracy: 0.5687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r174/194 [=========================&gt;....] - ETA: 3s - loss: 0.8358 - accuracy: 0.5689\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r175/194 [==========================&gt;...] - ETA: 3s - loss: 0.8350 - accuracy: 0.5691\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r176/194 [==========================&gt;...] - ETA: 3s - loss: 0.8343 - accuracy: 0.5692\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r177/194 [==========================&gt;...] - ETA: 2s - loss: 0.8335 - accuracy: 0.5694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r178/194 [==========================&gt;...] - ETA: 2s - loss: 0.8328 - accuracy: 0.5696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r179/194 [==========================&gt;...] - ETA: 2s - loss: 0.8320 - accuracy: 0.5698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r180/194 [==========================&gt;...] - ETA: 2s - loss: 0.8313 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r181/194 [==========================&gt;...] - ETA: 2s - loss: 0.8305 - accuracy: 0.5701\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r182/194 [===========================&gt;..] - ETA: 2s - loss: 0.8298 - accuracy: 0.5703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r183/194 [===========================&gt;..] - ETA: 1s - loss: 0.8291 - accuracy: 0.5705\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r184/194 [===========================&gt;..] - ETA: 1s - loss: 0.8284 - accuracy: 0.5706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r185/194 [===========================&gt;..] - ETA: 1s - loss: 0.8277 - accuracy: 0.5708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r186/194 [===========================&gt;..] - ETA: 1s - loss: 0.8270 - accuracy: 0.5710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r187/194 [===========================&gt;..] - ETA: 1s - loss: 0.8263 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r188/194 [============================&gt;.] - ETA: 1s - loss: 0.8256 - accuracy: 0.5714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r189/194 [============================&gt;.] - ETA: 0s - loss: 0.8249 - accuracy: 0.5715\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r190/194 [============================&gt;.] - ETA: 0s - loss: 0.8242 - accuracy: 0.5717\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r191/194 [============================&gt;.] - ETA: 0s - loss: 0.8236 - accuracy: 0.5719\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r192/194 [============================&gt;.] - ETA: 0s - loss: 0.8229 - accuracy: 0.5721\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r193/194 [============================&gt;.] - ETA: 0s - loss: 0.8222 - accuracy: 0.5722\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r194/194 [==============================] - ETA: 0s - loss: 0.8216 - accuracy: 0.5724\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r194/194 [==============================] - 275s 438ms/step - loss: 0.8209 - accuracy: 0.5726 - val_loss: 0.6330 - val_accuracy: 0.6495\n\n*** WARNING: skipped 622966 bytes of output ***\n\n\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/194 [======================&gt;.......] - ETA: 7s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r151/194 [======================&gt;.......] - ETA: 7s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r152/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8208\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r153/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r154/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r155/194 [======================&gt;.......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r156/194 [=======================&gt;......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8209\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r157/194 [=======================&gt;......] - ETA: 6s - loss: 0.3904 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r158/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r159/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8210\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r160/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r161/194 [=======================&gt;......] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r162/194 [========================&gt;.....] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r163/194 [========================&gt;.....] - ETA: 5s - loss: 0.3903 - accuracy: 0.8211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r164/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r165/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r166/194 [========================&gt;.....] - ETA: 4s - loss: 0.3902 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r167/194 [========================&gt;.....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8212\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r168/194 [========================&gt;.....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8213\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r169/194 [=========================&gt;....] - ETA: 4s - loss: 0.3901 - accuracy: 0.8213\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r170/194 [=========================&gt;....] - ETA: 3s - loss: 0.3901 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r171/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r172/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8214\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r173/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8215\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r174/194 [=========================&gt;....] - ETA: 3s - loss: 0.3900 - accuracy: 0.8215\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r175/194 [==========================&gt;...] - ETA: 3s - loss: 0.3900 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r176/194 [==========================&gt;...] - ETA: 2s - loss: 0.3900 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r177/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8216\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r178/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r179/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r180/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8217\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r181/194 [==========================&gt;...] - ETA: 2s - loss: 0.3899 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r182/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r183/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8218\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r184/194 [===========================&gt;..] - ETA: 1s - loss: 0.3898 - accuracy: 0.8219\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r185/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8219\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r186/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8220\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r187/194 [===========================&gt;..] - ETA: 1s - loss: 0.3897 - accuracy: 0.8220\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r188/194 [============================&gt;.] - ETA: 0s - loss: 0.3896 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r189/194 [============================&gt;.] - ETA: 0s - loss: 0.3896 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r190/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r191/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r192/194 [============================&gt;.] - ETA: 0s - loss: 0.3895 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r193/194 [============================&gt;.] - ETA: 0s - loss: 0.3894 - accuracy: 0.8222\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r194/194 [==============================] - ETA: 0s - loss: 0.3894 - accuracy: 0.8223\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r194/194 [==============================] - 34s 174ms/step - loss: 0.3894 - accuracy: 0.8223 - val_loss: 0.7644 - val_accuracy: 0.6536\nINFO:tensorflow:Assets written to: /tmp/tmpd8m3sk8_/model/data/model/assets\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train the model\n",
    "with mlflow.start_run():\n",
    "  epochs=20\n",
    "  history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "61095ff8-af3c-4ef0-a997-64d3f5da0b1a",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/e5487d11-206e-4c61-a4b6-161534427539.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual the result\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "4a1f04f3-869e-4c79-b214-941dee39ec7b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">INFO:tensorflow:Assets written to: /dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/assets\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">INFO:tensorflow:Assets written to: /dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/assets\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save the model artifact.\n",
    "model.save('/dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "a7eea0ad-672c-41be-addc-001668bd9f60",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[16]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/&#39;, name=&#39;basic_neural_v1/&#39;, size=0)]</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[16]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/&#39;, name=&#39;basic_neural_v1/&#39;, size=0)]</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List file in ADLS\n",
    "dbutils.fs.ls(\"/mnt/eason/pneumonia_classification_challenge/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "29239990-52ad-437e-8f84-e7ae3cd157cc",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# data_augmentation = keras.Sequential(\n",
    "#   [\n",
    "#     layers.experimental.preprocessing.RandomFlip(\"horizontal\", \n",
    "#                                                  input_shape=(img_height, \n",
    "#                                                               img_width,\n",
    "#                                                               1)),\n",
    "#     layers.experimental.preprocessing.RandomRotation(0.1),\n",
    "#     layers.experimental.preprocessing.RandomZoom(0.1),\n",
    "#   ]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "798c12b7-34b0-478d-8d69-207019024e59",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Create the model\n",
    "# model = Sequential([\n",
    "#   data_augmentation,\n",
    "#   layers.experimental.preprocessing.Rescaling(1./255),\n",
    "#   layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
    "#   layers.MaxPooling2D(),\n",
    "#   layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "#   layers.MaxPooling2D(),\n",
    "#   layers.Dropout(0.2),\n",
    "#   layers.Flatten(),\n",
    "#   layers.Dense(128, activation='relu'),\n",
    "#   layers.Dense(num_classes)\n",
    "# ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "1ae37ba7-1ce7-4831-96d4-840224845c5d",
     "showTitle": false,
     "title": ""
    }
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
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       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Model: &#34;sequential_2&#34;\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
       "sequential_1 (Sequential)    (None, 500, 500, 1)       0         \n",
       "_________________________________________________________________\n",
       "rescaling_1 (Rescaling)      (None, 500, 500, 1)       0         \n",
       "_________________________________________________________________\n",
       "conv2d_2 (Conv2D)            (None, 500, 500, 32)      320       \n",
       "_________________________________________________________________\n",
       "max_pooling2d_2 (MaxPooling2 (None, 250, 250, 32)      0         \n",
       "_________________________________________________________________\n",
       "conv2d_3 (Conv2D)            (None, 250, 250, 64)      18496     \n",
       "_________________________________________________________________\n",
       "max_pooling2d_3 (MaxPooling2 (None, 125, 125, 64)      0         \n",
       "_________________________________________________________________\n",
       "dropout (Dropout)            (None, 125, 125, 64)      0         \n",
       "_________________________________________________________________\n",
       "flatten_1 (Flatten)          (None, 1000000)           0         \n",
       "_________________________________________________________________\n",
       "dense_2 (Dense)              (None, 128)               128000128 \n",
       "_________________________________________________________________\n",
       "dense_3 (Dense)              (None, 2)                 258       \n",
       "=================================================================\n",
       "Total params: 128,019,202\n",
       "Trainable params: 128,019,202\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
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       "arguments": {},
       "data": "<div class=\"ansiout\">Model: &#34;sequential_2&#34;\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nsequential_1 (Sequential)    (None, 500, 500, 1)       0         \n_________________________________________________________________\nrescaling_1 (Rescaling)      (None, 500, 500, 1)       0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 500, 500, 32)      320       \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 250, 250, 32)      0         \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 250, 250, 64)      18496     \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 125, 125, 64)      0         \n_________________________________________________________________\ndropout (Dropout)            (None, 125, 125, 64)      0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 1000000)           0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 128)               128000128 \n_________________________________________________________________\ndense_3 (Dense)              (None, 2)                 258       \n=================================================================\nTotal params: 128,019,202\nTrainable params: 128,019,202\nNon-trainable params: 0\n_________________________________________________________________\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Show the model summary\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "c3879a0d-ec2a-466f-8429-92fbb3732bca",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Compile the model\n",
    "# model.compile(optimizer='adam',\n",
    "#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "#               metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "118f6985-8df9-417e-8fae-13a64b6d04fd",
     "showTitle": false,
     "title": ""
    }
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Epoch 1/10\n",
       "\r",
       "  1/122 [..............................] - ETA: 41:35 - loss: 0.6875 - accuracy: 0.6250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  2/122 [..............................] - ETA: 6:57 - loss: 1.8578 - accuracy: 0.6406 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  3/122 [..............................] - ETA: 6:37 - loss: 8.7227 - accuracy: 0.6007\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  4/122 [..............................] - ETA: 6:22 - loss: 11.5495 - accuracy: 0.5716\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  5/122 [&gt;.............................] - ETA: 6:09 - loss: 13.0087 - accuracy: 0.5573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  6/122 [&gt;.............................] - ETA: 6:02 - loss: 14.0899 - accuracy: 0.5460\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  7/122 [&gt;.............................] - ETA: 5:55 - loss: 14.8244 - accuracy: 0.5369\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  8/122 [&gt;.............................] - ETA: 5:49 - loss: 15.2905 - accuracy: 0.5303\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  9/122 [=&gt;............................] - ETA: 5:45 - loss: 15.4621 - accuracy: 0.5277\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 10/122 [=&gt;............................] - ETA: 5:39 - loss: 15.4527 - accuracy: 0.5243\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 11/122 [=&gt;............................] - ETA: 5:37 - loss: 15.3303 - accuracy: 0.5211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 12/122 [=&gt;............................] - ETA: 5:33 - loss: 15.1537 - accuracy: 0.5163\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 13/122 [==&gt;...........................] - ETA: 5:30 - loss: 14.9413 - accuracy: 0.5121\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 14/122 [==&gt;...........................] - ETA: 5:26 - loss: 14.7063 - accuracy: 0.5083\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 15/122 [==&gt;...........................] - ETA: 5:22 - loss: 14.4564 - accuracy: 0.5053\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 16/122 [==&gt;...........................] - ETA: 5:19 - loss: 14.1981 - accuracy: 0.5025\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 17/122 [===&gt;..........................] - ETA: 5:15 - loss: 13.9370 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 18/122 [===&gt;..........................] - ETA: 5:12 - loss: 13.6771 - accuracy: 0.4971\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 19/122 [===&gt;..........................] - ETA: 5:09 - loss: 13.4208 - accuracy: 0.4947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 20/122 [===&gt;..........................] - ETA: 5:06 - loss: 13.1699 - accuracy: 0.4926\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 21/122 [====&gt;.........................] - ETA: 5:02 - loss: 12.9255 - accuracy: 0.4907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 22/122 [====&gt;.........................] - ETA: 4:59 - loss: 12.6881 - accuracy: 0.4889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 23/122 [====&gt;.........................] - ETA: 4:56 - loss: 12.4583 - accuracy: 0.4867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 24/122 [====&gt;.........................] - ETA: 4:53 - loss: 12.2360 - accuracy: 0.4846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 25/122 [=====&gt;........................] - ETA: 4:49 - loss: 12.0212 - accuracy: 0.4826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 26/122 [=====&gt;........................] - ETA: 4:46 - loss: 11.8138 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 27/122 [=====&gt;........................] - ETA: 4:43 - loss: 11.6136 - accuracy: 0.4793\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 28/122 [=====&gt;........................] - ETA: 4:40 - loss: 11.4205 - accuracy: 0.4781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 29/122 [======&gt;.......................] - ETA: 4:37 - loss: 11.2341 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 30/122 [======&gt;.......................] - ETA: 4:34 - loss: 11.0541 - accuracy: 0.4769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 31/122 [======&gt;.......................] - ETA: 4:30 - loss: 10.8805 - accuracy: 0.4768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 32/122 [======&gt;.......................] - ETA: 4:27 - loss: 10.7128 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 33/122 [=======&gt;......................] - ETA: 4:24 - loss: 10.5509 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 34/122 [=======&gt;......................] - ETA: 4:21 - loss: 10.3944 - accuracy: 0.4765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 35/122 [=======&gt;......................] - ETA: 4:18 - loss: 10.2432 - accuracy: 0.4764\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 36/122 [=======&gt;......................] - ETA: 4:15 - loss: 10.0970 - accuracy: 0.4763\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 37/122 [========&gt;.....................] - ETA: 4:12 - loss: 9.9555 - accuracy: 0.4761 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 38/122 [========&gt;.....................] - ETA: 4:10 - loss: 9.8187 - accuracy: 0.4760\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 39/122 [========&gt;.....................] - ETA: 4:07 - loss: 9.6861 - accuracy: 0.4760\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 40/122 [========&gt;.....................] - ETA: 4:04 - loss: 9.5577 - accuracy: 0.4761\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 41/122 [=========&gt;....................] - ETA: 4:01 - loss: 9.4333 - accuracy: 0.4762\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 42/122 [=========&gt;....................] - ETA: 3:58 - loss: 9.3127 - accuracy: 0.4763\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 43/122 [=========&gt;....................] - ETA: 3:54 - loss: 9.1956 - accuracy: 0.4764\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 44/122 [=========&gt;....................] - ETA: 3:51 - loss: 9.0821 - accuracy: 0.4765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 45/122 [==========&gt;...................] - ETA: 3:48 - loss: 8.9719 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 46/122 [==========&gt;...................] - ETA: 3:45 - loss: 8.8648 - accuracy: 0.4767\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 47/122 [==========&gt;...................] - ETA: 3:42 - loss: 8.7608 - accuracy: 0.4769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 48/122 [==========&gt;...................] - ETA: 3:39 - loss: 8.6597 - accuracy: 0.4770\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 49/122 [===========&gt;..................] - ETA: 3:36 - loss: 8.5613 - accuracy: 0.4772\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 50/122 [===========&gt;..................] - ETA: 3:33 - loss: 8.4657 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 51/122 [===========&gt;..................] - ETA: 3:30 - loss: 8.3726 - accuracy: 0.4775\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 52/122 [===========&gt;..................] - ETA: 3:27 - loss: 8.2819 - accuracy: 0.4777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 53/122 [============&gt;.................] - ETA: 3:24 - loss: 8.1937 - accuracy: 0.4779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 54/122 [============&gt;.................] - ETA: 3:21 - loss: 8.1077 - accuracy: 0.4782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 55/122 [============&gt;.................] - ETA: 3:18 - loss: 8.0238 - accuracy: 0.4784\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 56/122 [============&gt;.................] - ETA: 3:15 - loss: 7.9421 - accuracy: 0.4787\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 57/122 [=============&gt;................] - ETA: 3:12 - loss: 7.8624 - accuracy: 0.4789\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 58/122 [=============&gt;................] - ETA: 3:09 - loss: 7.7846 - accuracy: 0.4792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 59/122 [=============&gt;................] - ETA: 3:06 - loss: 7.7087 - accuracy: 0.4795\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 60/122 [=============&gt;................] - ETA: 3:03 - loss: 7.6347 - accuracy: 0.4798\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 61/122 [==============&gt;...............] - ETA: 3:00 - loss: 7.5625 - accuracy: 0.4801\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 62/122 [==============&gt;...............] - ETA: 2:57 - loss: 7.4919 - accuracy: 0.4804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 63/122 [==============&gt;...............] - ETA: 2:54 - loss: 7.4229 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 64/122 [==============&gt;...............] - ETA: 2:51 - loss: 7.3555 - accuracy: 0.4811\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 65/122 [==============&gt;...............] - ETA: 2:48 - loss: 7.2896 - accuracy: 0.4814\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 66/122 [===============&gt;..............] - ETA: 2:45 - loss: 7.2251 - accuracy: 0.4817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 67/122 [===============&gt;..............] - ETA: 2:42 - loss: 7.1620 - accuracy: 0.4820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 68/122 [===============&gt;..............] - ETA: 2:39 - loss: 7.1003 - accuracy: 0.4822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 69/122 [===============&gt;..............] - ETA: 2:36 - loss: 7.0399 - accuracy: 0.4825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 70/122 [================&gt;.............] - ETA: 2:33 - loss: 6.9808 - accuracy: 0.4828\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 71/122 [================&gt;.............] - ETA: 2:29 - loss: 6.9232 - accuracy: 0.4831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 72/122 [================&gt;.............] - ETA: 2:26 - loss: 6.8668 - accuracy: 0.4833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 73/122 [================&gt;.............] - ETA: 2:23 - loss: 6.8116 - accuracy: 0.4835\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 74/122 [=================&gt;............] - ETA: 2:20 - loss: 6.7574 - accuracy: 0.4838\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 75/122 [=================&gt;............] - ETA: 2:17 - loss: 6.7043 - accuracy: 0.4840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 76/122 [=================&gt;............] - ETA: 2:14 - loss: 6.6522 - accuracy: 0.4843\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 77/122 [=================&gt;............] - ETA: 2:11 - loss: 6.6012 - accuracy: 0.4846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 78/122 [==================&gt;...........] - ETA: 2:08 - loss: 6.5511 - accuracy: 0.4848\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 79/122 [==================&gt;...........] - ETA: 2:05 - loss: 6.5019 - accuracy: 0.4851\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 80/122 [==================&gt;...........] - ETA: 2:02 - loss: 6.4537 - accuracy: 0.4853\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 81/122 [==================&gt;...........] - ETA: 1:59 - loss: 6.4064 - accuracy: 0.4856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 82/122 [===================&gt;..........] - ETA: 1:56 - loss: 6.3599 - accuracy: 0.4858\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 83/122 [===================&gt;..........] - ETA: 1:53 - loss: 6.3143 - accuracy: 0.4861\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 84/122 [===================&gt;..........] - ETA: 1:51 - loss: 6.2695 - accuracy: 0.4863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 85/122 [===================&gt;..........] - ETA: 1:48 - loss: 6.2254 - accuracy: 0.4866\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 86/122 [====================&gt;.........] - ETA: 1:45 - loss: 6.1822 - accuracy: 0.4869\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 87/122 [====================&gt;.........] - ETA: 1:42 - loss: 6.1397 - accuracy: 0.4872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 88/122 [====================&gt;.........] - ETA: 1:39 - loss: 6.0979 - accuracy: 0.4875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 89/122 [====================&gt;.........] - ETA: 1:36 - loss: 6.0569 - accuracy: 0.4878\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 90/122 [=====================&gt;........] - ETA: 1:33 - loss: 6.0165 - accuracy: 0.4880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 91/122 [=====================&gt;........] - ETA: 1:30 - loss: 5.9769 - accuracy: 0.4883\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 92/122 [=====================&gt;........] - ETA: 1:27 - loss: 5.9378 - accuracy: 0.4885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 93/122 [=====================&gt;........] - ETA: 1:24 - loss: 5.8994 - accuracy: 0.4888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 94/122 [======================&gt;.......] - ETA: 1:21 - loss: 5.8617 - accuracy: 0.4890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 95/122 [======================&gt;.......] - ETA: 1:18 - loss: 5.8245 - accuracy: 0.4892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 96/122 [======================&gt;.......] - ETA: 1:15 - loss: 5.7880 - accuracy: 0.4895\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 97/122 [======================&gt;.......] - ETA: 1:12 - loss: 5.7520 - accuracy: 0.4897\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 98/122 [=======================&gt;......] - ETA: 1:09 - loss: 5.7166 - accuracy: 0.4899\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 99/122 [=======================&gt;......] - ETA: 1:06 - loss: 5.6817 - accuracy: 0.4901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "100/122 [=======================&gt;......] - ETA: 1:04 - loss: 5.6474 - accuracy: 0.4903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "101/122 [=======================&gt;......] - ETA: 1:01 - loss: 5.6136 - accuracy: 0.4905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "102/122 [========================&gt;.....] - ETA: 58s - loss: 5.5803 - accuracy: 0.4908 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "103/122 [========================&gt;.....] - ETA: 55s - loss: 5.5476 - accuracy: 0.4910\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "104/122 [========================&gt;.....] - ETA: 52s - loss: 5.5153 - accuracy: 0.4912\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "105/122 [========================&gt;.....] - ETA: 49s - loss: 5.4835 - accuracy: 0.4914\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "106/122 [=========================&gt;....] - ETA: 46s - loss: 5.4521 - accuracy: 0.4916\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "107/122 [=========================&gt;....] - ETA: 43s - loss: 5.4212 - accuracy: 0.4917\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "108/122 [=========================&gt;....] - ETA: 40s - loss: 5.3908 - accuracy: 0.4919\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "109/122 [=========================&gt;....] - ETA: 37s - loss: 5.3608 - accuracy: 0.4921\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "110/122 [==========================&gt;...] - ETA: 34s - loss: 5.3312 - accuracy: 0.4923\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "111/122 [==========================&gt;...] - ETA: 32s - loss: 5.3020 - accuracy: 0.4925\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "112/122 [==========================&gt;...] - ETA: 29s - loss: 5.2733 - accuracy: 0.4927\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "113/122 [==========================&gt;...] - ETA: 26s - loss: 5.2449 - accuracy: 0.4929\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "114/122 [===========================&gt;..] - ETA: 23s - loss: 5.2169 - accuracy: 0.4931\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "115/122 [===========================&gt;..] - ETA: 20s - loss: 5.1893 - accuracy: 0.4933\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "116/122 [===========================&gt;..] - ETA: 17s - loss: 5.1621 - accuracy: 0.4935\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "117/122 [===========================&gt;..] - ETA: 14s - loss: 5.1353 - accuracy: 0.4937\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "118/122 [============================&gt;.] - ETA: 11s - loss: 5.1088 - accuracy: 0.4939\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "119/122 [============================&gt;.] - ETA: 8s - loss: 5.0826 - accuracy: 0.4941 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "120/122 [============================&gt;.] - ETA: 5s - loss: 5.0568 - accuracy: 0.4943\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "121/122 [============================&gt;.] - ETA: 2s - loss: 5.0314 - accuracy: 0.4945\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - ETA: 0s - loss: 5.0063 - accuracy: 0.4947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - 395s 3s/step - loss: 4.9815 - accuracy: 0.4948 - val_loss: 0.6729 - val_accuracy: 0.5691\n",
       "Epoch 2/10\n",
       "\r",
       "  1/122 [..............................] - ETA: 30:46 - loss: 0.7115 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  2/122 [..............................] - ETA: 6:10 - loss: 0.7063 - accuracy: 0.5312 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  3/122 [..............................] - ETA: 5:55 - loss: 0.7037 - accuracy: 0.5278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  4/122 [..............................] - ETA: 5:48 - loss: 0.6997 - accuracy: 0.5365\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  5/122 [&gt;.............................] - ETA: 5:45 - loss: 0.6977 - accuracy: 0.5342\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  6/122 [&gt;.............................] - ETA: 5:41 - loss: 0.6964 - accuracy: 0.5319\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  7/122 [&gt;.............................] - ETA: 5:40 - loss: 0.6956 - accuracy: 0.5299\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  8/122 [&gt;.............................] - ETA: 5:36 - loss: 0.6951 - accuracy: 0.5281\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  9/122 [=&gt;............................] - ETA: 5:32 - loss: 0.6945 - accuracy: 0.5304\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 10/122 [=&gt;............................] - ETA: 5:29 - loss: 0.6938 - accuracy: 0.5324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 11/122 [=&gt;............................] - ETA: 5:25 - loss: 0.6931 - accuracy: 0.5361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 12/122 [=&gt;............................] - ETA: 5:22 - loss: 0.6925 - accuracy: 0.5392\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 13/122 [==&gt;...........................] - ETA: 5:18 - loss: 0.6918 - accuracy: 0.5429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 14/122 [==&gt;...........................] - ETA: 5:14 - loss: 0.6913 - accuracy: 0.5459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 15/122 [==&gt;...........................] - ETA: 5:10 - loss: 0.6908 - accuracy: 0.5489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 16/122 [==&gt;...........................] - ETA: 5:08 - loss: 0.6903 - accuracy: 0.5520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 17/122 [===&gt;..........................] - ETA: 5:04 - loss: 0.6897 - accuracy: 0.5554\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 18/122 [===&gt;..........................] - ETA: 5:01 - loss: 0.6892 - accuracy: 0.5581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 19/122 [===&gt;..........................] - ETA: 4:58 - loss: 0.6888 - accuracy: 0.5606\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 20/122 [===&gt;..........................] - ETA: 4:55 - loss: 0.6884 - accuracy: 0.5629\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 21/122 [====&gt;.........................] - ETA: 4:52 - loss: 0.6881 - accuracy: 0.5643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 22/122 [====&gt;.........................] - ETA: 4:50 - loss: 0.6879 - accuracy: 0.5653\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 23/122 [====&gt;.........................] - ETA: 4:47 - loss: 0.6878 - accuracy: 0.5664\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 24/122 [====&gt;.........................] - ETA: 4:44 - loss: 0.6876 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 25/122 [=====&gt;........................] - ETA: 4:41 - loss: 0.6875 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 26/122 [=====&gt;........................] - ETA: 4:38 - loss: 0.6874 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 27/122 [=====&gt;........................] - ETA: 4:35 - loss: 0.6872 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 28/122 [=====&gt;........................] - ETA: 4:32 - loss: 0.6871 - accuracy: 0.5690\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 29/122 [======&gt;.......................] - ETA: 4:29 - loss: 0.6869 - accuracy: 0.5693\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 30/122 [======&gt;.......................] - ETA: 4:26 - loss: 0.6868 - accuracy: 0.5696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 31/122 [======&gt;.......................] - ETA: 4:23 - loss: 0.6866 - accuracy: 0.5698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 32/122 [======&gt;.......................] - ETA: 4:20 - loss: 0.6865 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 33/122 [=======&gt;......................] - ETA: 4:18 - loss: 0.6863 - accuracy: 0.5700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 34/122 [=======&gt;......................] - ETA: 4:15 - loss: 0.6862 - accuracy: 0.5702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 35/122 [=======&gt;......................] - ETA: 4:12 - loss: 0.6861 - accuracy: 0.5703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 36/122 [=======&gt;......................] - ETA: 4:09 - loss: 0.6860 - accuracy: 0.5705\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 37/122 [========&gt;.....................] - ETA: 4:06 - loss: 0.6859 - accuracy: 0.5707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 38/122 [========&gt;.....................] - ETA: 4:03 - loss: 0.6857 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 39/122 [========&gt;.....................] - ETA: 4:00 - loss: 0.6856 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 40/122 [========&gt;.....................] - ETA: 3:57 - loss: 0.6855 - accuracy: 0.5713\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 41/122 [=========&gt;....................] - ETA: 3:54 - loss: 0.6855 - accuracy: 0.5713\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 42/122 [=========&gt;....................] - ETA: 3:52 - loss: 0.6855 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 43/122 [=========&gt;....................] - ETA: 3:49 - loss: 0.6854 - accuracy: 0.5711\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 44/122 [=========&gt;....................] - ETA: 3:46 - loss: 0.6854 - accuracy: 0.5710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 45/122 [==========&gt;...................] - ETA: 3:43 - loss: 0.6854 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 46/122 [==========&gt;...................] - ETA: 3:40 - loss: 0.6854 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 47/122 [==========&gt;...................] - ETA: 3:37 - loss: 0.6854 - accuracy: 0.5708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 48/122 [==========&gt;...................] - ETA: 3:34 - loss: 0.6853 - accuracy: 0.5707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 49/122 [===========&gt;..................] - ETA: 3:31 - loss: 0.6853 - accuracy: 0.5706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 50/122 [===========&gt;..................] - ETA: 3:28 - loss: 0.6853 - accuracy: 0.5704\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 51/122 [===========&gt;..................] - ETA: 3:25 - loss: 0.6853 - accuracy: 0.5702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 52/122 [===========&gt;..................] - ETA: 3:22 - loss: 0.6853 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 53/122 [============&gt;.................] - ETA: 3:19 - loss: 0.6853 - accuracy: 0.5697\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 54/122 [============&gt;.................] - ETA: 3:16 - loss: 0.6853 - accuracy: 0.5694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 55/122 [============&gt;.................] - ETA: 3:13 - loss: 0.6853 - accuracy: 0.5692\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 56/122 [============&gt;.................] - ETA: 3:11 - loss: 0.6854 - accuracy: 0.5690\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 57/122 [=============&gt;................] - ETA: 3:08 - loss: 0.6854 - accuracy: 0.5688\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 58/122 [=============&gt;................] - ETA: 3:05 - loss: 0.6854 - accuracy: 0.5687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 59/122 [=============&gt;................] - ETA: 3:02 - loss: 0.6854 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 60/122 [=============&gt;................] - ETA: 2:59 - loss: 0.6854 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 61/122 [==============&gt;...............] - ETA: 2:56 - loss: 0.6854 - accuracy: 0.5685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 62/122 [==============&gt;...............] - ETA: 2:53 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 63/122 [==============&gt;...............] - ETA: 2:50 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 64/122 [==============&gt;...............] - ETA: 2:47 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 65/122 [==============&gt;...............] - ETA: 2:43 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 66/122 [===============&gt;..............] - ETA: 2:40 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 67/122 [===============&gt;..............] - ETA: 2:37 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 68/122 [===============&gt;..............] - ETA: 2:34 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 69/122 [===============&gt;..............] - ETA: 2:31 - loss: 0.6854 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 70/122 [================&gt;.............] - ETA: 2:28 - loss: 0.6853 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 71/122 [================&gt;.............] - ETA: 2:26 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 72/122 [================&gt;.............] - ETA: 2:23 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 73/122 [================&gt;.............] - ETA: 2:20 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 74/122 [=================&gt;............] - ETA: 2:17 - loss: 0.6852 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 75/122 [=================&gt;............] - ETA: 2:14 - loss: 0.6852 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 76/122 [=================&gt;............] - ETA: 2:11 - loss: 0.6852 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 77/122 [=================&gt;............] - ETA: 2:08 - loss: 0.6851 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 78/122 [==================&gt;...........] - ETA: 2:06 - loss: 0.6851 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 79/122 [==================&gt;...........] - ETA: 2:03 - loss: 0.6851 - accuracy: 0.5680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 80/122 [==================&gt;...........] - ETA: 2:00 - loss: 0.6850 - accuracy: 0.5679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 81/122 [==================&gt;...........] - ETA: 1:57 - loss: 0.6850 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 82/122 [===================&gt;..........] - ETA: 1:54 - loss: 0.6850 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 83/122 [===================&gt;..........] - ETA: 1:51 - loss: 0.6850 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 84/122 [===================&gt;..........] - ETA: 1:48 - loss: 0.6850 - accuracy: 0.5676\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 85/122 [===================&gt;..........] - ETA: 1:45 - loss: 0.6850 - accuracy: 0.5675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 86/122 [====================&gt;.........] - ETA: 1:43 - loss: 0.6850 - accuracy: 0.5674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 87/122 [====================&gt;.........] - ETA: 1:40 - loss: 0.6850 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 88/122 [====================&gt;.........] - ETA: 1:37 - loss: 0.6849 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 89/122 [====================&gt;.........] - ETA: 1:34 - loss: 0.6849 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 90/122 [=====================&gt;........] - ETA: 1:31 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 91/122 [=====================&gt;........] - ETA: 1:28 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 92/122 [=====================&gt;........] - ETA: 1:25 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 93/122 [=====================&gt;........] - ETA: 1:22 - loss: 0.6849 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 94/122 [======================&gt;.......] - ETA: 1:19 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 95/122 [======================&gt;.......] - ETA: 1:17 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 96/122 [======================&gt;.......] - ETA: 1:14 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 97/122 [======================&gt;.......] - ETA: 1:11 - loss: 0.6848 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 98/122 [=======================&gt;......] - ETA: 1:08 - loss: 0.6847 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 99/122 [=======================&gt;......] - ETA: 1:05 - loss: 0.6847 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "100/122 [=======================&gt;......] - ETA: 1:02 - loss: 0.6847 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "101/122 [=======================&gt;......] - ETA: 59s - loss: 0.6847 - accuracy: 0.5668 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "102/122 [========================&gt;.....] - ETA: 57s - loss: 0.6847 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "103/122 [========================&gt;.....] - ETA: 54s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "104/122 [========================&gt;.....] - ETA: 51s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "105/122 [========================&gt;.....] - ETA: 48s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "106/122 [=========================&gt;....] - ETA: 45s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "107/122 [=========================&gt;....] - ETA: 42s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "108/122 [=========================&gt;....] - ETA: 39s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "109/122 [=========================&gt;....] - ETA: 37s - loss: 0.6845 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "110/122 [==========================&gt;...] - ETA: 34s - loss: 0.6845 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "111/122 [==========================&gt;...] - ETA: 31s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "112/122 [==========================&gt;...] - ETA: 28s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "113/122 [==========================&gt;...] - ETA: 25s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "114/122 [===========================&gt;..] - ETA: 22s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "115/122 [===========================&gt;..] - ETA: 19s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "116/122 [===========================&gt;..] - ETA: 17s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "117/122 [===========================&gt;..] - ETA: 14s - loss: 0.6843 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "118/122 [============================&gt;.] - ETA: 11s - loss: 0.6843 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "119/122 [============================&gt;.] - ETA: 8s - loss: 0.6842 - accuracy: 0.5672 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "120/122 [============================&gt;.] - ETA: 5s - loss: 0.6842 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "121/122 [============================&gt;.] - ETA: 2s - loss: 0.6842 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - ETA: 0s - loss: 0.6841 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - 380s 3s/step - loss: 0.6841 - accuracy: 0.5674 - val_loss: 0.6560 - val_accuracy: 0.6062\n",
       "\n",
       "*** WARNING: skipped 148412 bytes of output ***\n",
       "\n",
       "Epoch 10/10\n",
       "\r",
       "  1/122 [..............................] - ETA: 5:49 - loss: 0.5531 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  2/122 [..............................] - ETA: 2:15 - loss: 0.5380 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  3/122 [..............................] - ETA: 3:58 - loss: 0.5192 - accuracy: 0.8037\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  4/122 [..............................] - ETA: 4:31 - loss: 0.5153 - accuracy: 0.8095\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  5/122 [&gt;.............................] - ETA: 4:44 - loss: 0.5122 - accuracy: 0.8123\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  6/122 [&gt;.............................] - ETA: 4:54 - loss: 0.5126 - accuracy: 0.8118\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  7/122 [&gt;.............................] - ETA: 5:00 - loss: 0.5129 - accuracy: 0.8116\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  8/122 [&gt;.............................] - ETA: 5:03 - loss: 0.5149 - accuracy: 0.8104\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "  9/122 [=&gt;............................] - ETA: 5:04 - loss: 0.5171 - accuracy: 0.8079\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 10/122 [=&gt;............................] - ETA: 5:04 - loss: 0.5197 - accuracy: 0.8054\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 11/122 [=&gt;............................] - ETA: 5:02 - loss: 0.5220 - accuracy: 0.8026\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 12/122 [=&gt;............................] - ETA: 5:10 - loss: 0.5229 - accuracy: 0.8006\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 13/122 [==&gt;...........................] - ETA: 5:10 - loss: 0.5254 - accuracy: 0.7974\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 14/122 [==&gt;...........................] - ETA: 5:08 - loss: 0.5280 - accuracy: 0.7947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 15/122 [==&gt;...........................] - ETA: 5:06 - loss: 0.5303 - accuracy: 0.7920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 16/122 [==&gt;...........................] - ETA: 5:03 - loss: 0.5328 - accuracy: 0.7892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 17/122 [===&gt;..........................] - ETA: 5:00 - loss: 0.5352 - accuracy: 0.7864\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 18/122 [===&gt;..........................] - ETA: 4:59 - loss: 0.5378 - accuracy: 0.7834\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 19/122 [===&gt;..........................] - ETA: 4:56 - loss: 0.5403 - accuracy: 0.7804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 20/122 [===&gt;..........................] - ETA: 4:53 - loss: 0.5423 - accuracy: 0.7779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 21/122 [====&gt;.........................] - ETA: 4:50 - loss: 0.5445 - accuracy: 0.7752\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 22/122 [====&gt;.........................] - ETA: 4:48 - loss: 0.5467 - accuracy: 0.7729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 23/122 [====&gt;.........................] - ETA: 4:46 - loss: 0.5487 - accuracy: 0.7707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 24/122 [====&gt;.........................] - ETA: 4:42 - loss: 0.5504 - accuracy: 0.7687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 25/122 [=====&gt;........................] - ETA: 4:40 - loss: 0.5520 - accuracy: 0.7668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 26/122 [=====&gt;........................] - ETA: 4:37 - loss: 0.5536 - accuracy: 0.7650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 27/122 [=====&gt;........................] - ETA: 4:34 - loss: 0.5550 - accuracy: 0.7635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 28/122 [=====&gt;........................] - ETA: 4:50 - loss: 0.5564 - accuracy: 0.7619\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 29/122 [======&gt;.......................] - ETA: 4:47 - loss: 0.5577 - accuracy: 0.7604\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 30/122 [======&gt;.......................] - ETA: 4:43 - loss: 0.5591 - accuracy: 0.7588\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 31/122 [======&gt;.......................] - ETA: 4:39 - loss: 0.5605 - accuracy: 0.7573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 32/122 [======&gt;.......................] - ETA: 4:36 - loss: 0.5618 - accuracy: 0.7557\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 33/122 [=======&gt;......................] - ETA: 4:32 - loss: 0.5631 - accuracy: 0.7542\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 34/122 [=======&gt;......................] - ETA: 4:29 - loss: 0.5642 - accuracy: 0.7527\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 35/122 [=======&gt;......................] - ETA: 4:25 - loss: 0.5654 - accuracy: 0.7512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 36/122 [=======&gt;......................] - ETA: 4:22 - loss: 0.5665 - accuracy: 0.7498\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 37/122 [========&gt;.....................] - ETA: 4:18 - loss: 0.5676 - accuracy: 0.7485\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 38/122 [========&gt;.....................] - ETA: 4:15 - loss: 0.5687 - accuracy: 0.7472\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 39/122 [========&gt;.....................] - ETA: 4:12 - loss: 0.5696 - accuracy: 0.7459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 40/122 [========&gt;.....................] - ETA: 4:08 - loss: 0.5707 - accuracy: 0.7445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 41/122 [=========&gt;....................] - ETA: 4:05 - loss: 0.5718 - accuracy: 0.7431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 42/122 [=========&gt;....................] - ETA: 4:01 - loss: 0.5728 - accuracy: 0.7418\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 43/122 [=========&gt;....................] - ETA: 3:58 - loss: 0.5739 - accuracy: 0.7405\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 44/122 [=========&gt;....................] - ETA: 3:55 - loss: 0.5748 - accuracy: 0.7393\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 45/122 [==========&gt;...................] - ETA: 3:51 - loss: 0.5757 - accuracy: 0.7383\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 46/122 [==========&gt;...................] - ETA: 3:48 - loss: 0.5766 - accuracy: 0.7372\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 47/122 [==========&gt;...................] - ETA: 3:45 - loss: 0.5774 - accuracy: 0.7362\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 48/122 [==========&gt;...................] - ETA: 3:42 - loss: 0.5783 - accuracy: 0.7351\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 49/122 [===========&gt;..................] - ETA: 3:38 - loss: 0.5792 - accuracy: 0.7340\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 50/122 [===========&gt;..................] - ETA: 3:35 - loss: 0.5801 - accuracy: 0.7328\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 51/122 [===========&gt;..................] - ETA: 3:32 - loss: 0.5810 - accuracy: 0.7316\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 52/122 [===========&gt;..................] - ETA: 3:29 - loss: 0.5819 - accuracy: 0.7305\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 53/122 [============&gt;.................] - ETA: 3:26 - loss: 0.5827 - accuracy: 0.7295\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 54/122 [============&gt;.................] - ETA: 3:23 - loss: 0.5836 - accuracy: 0.7284\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 55/122 [============&gt;.................] - ETA: 3:19 - loss: 0.5844 - accuracy: 0.7273\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 56/122 [============&gt;.................] - ETA: 3:16 - loss: 0.5852 - accuracy: 0.7262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 57/122 [=============&gt;................] - ETA: 3:13 - loss: 0.5860 - accuracy: 0.7252\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 58/122 [=============&gt;................] - ETA: 3:10 - loss: 0.5868 - accuracy: 0.7242\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 59/122 [=============&gt;................] - ETA: 3:07 - loss: 0.5876 - accuracy: 0.7231\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 60/122 [=============&gt;................] - ETA: 3:04 - loss: 0.5883 - accuracy: 0.7221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 61/122 [==============&gt;...............] - ETA: 3:01 - loss: 0.5890 - accuracy: 0.7211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 62/122 [==============&gt;...............] - ETA: 2:58 - loss: 0.5898 - accuracy: 0.7200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 63/122 [==============&gt;...............] - ETA: 2:55 - loss: 0.5905 - accuracy: 0.7190\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 64/122 [==============&gt;...............] - ETA: 2:52 - loss: 0.5911 - accuracy: 0.7180\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 65/122 [==============&gt;...............] - ETA: 2:49 - loss: 0.5918 - accuracy: 0.7171\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 66/122 [===============&gt;..............] - ETA: 2:45 - loss: 0.5924 - accuracy: 0.7161\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 67/122 [===============&gt;..............] - ETA: 2:42 - loss: 0.5930 - accuracy: 0.7153\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 68/122 [===============&gt;..............] - ETA: 2:39 - loss: 0.5936 - accuracy: 0.7144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 69/122 [===============&gt;..............] - ETA: 2:36 - loss: 0.5941 - accuracy: 0.7136\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 70/122 [================&gt;.............] - ETA: 2:33 - loss: 0.5947 - accuracy: 0.7128\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 71/122 [================&gt;.............] - ETA: 2:30 - loss: 0.5952 - accuracy: 0.7121\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 72/122 [================&gt;.............] - ETA: 2:27 - loss: 0.5956 - accuracy: 0.7113\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 73/122 [================&gt;.............] - ETA: 2:24 - loss: 0.5961 - accuracy: 0.7106\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 74/122 [=================&gt;............] - ETA: 2:21 - loss: 0.5966 - accuracy: 0.7099\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 75/122 [=================&gt;............] - ETA: 2:18 - loss: 0.5970 - accuracy: 0.7093\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 76/122 [=================&gt;............] - ETA: 2:15 - loss: 0.5975 - accuracy: 0.7086\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 77/122 [=================&gt;............] - ETA: 2:12 - loss: 0.5979 - accuracy: 0.7080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 78/122 [==================&gt;...........] - ETA: 2:09 - loss: 0.5983 - accuracy: 0.7074\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 79/122 [==================&gt;...........] - ETA: 2:06 - loss: 0.5987 - accuracy: 0.7068\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 80/122 [==================&gt;...........] - ETA: 2:03 - loss: 0.5991 - accuracy: 0.7062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 81/122 [==================&gt;...........] - ETA: 2:00 - loss: 0.5995 - accuracy: 0.7057\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 82/122 [===================&gt;..........] - ETA: 1:57 - loss: 0.5999 - accuracy: 0.7051\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 83/122 [===================&gt;..........] - ETA: 1:54 - loss: 0.6003 - accuracy: 0.7045\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 84/122 [===================&gt;..........] - ETA: 1:51 - loss: 0.6007 - accuracy: 0.7040\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 85/122 [===================&gt;..........] - ETA: 1:48 - loss: 0.6011 - accuracy: 0.7033\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 86/122 [====================&gt;.........] - ETA: 1:45 - loss: 0.6015 - accuracy: 0.7027\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 87/122 [====================&gt;.........] - ETA: 1:42 - loss: 0.6019 - accuracy: 0.7021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 88/122 [====================&gt;.........] - ETA: 1:39 - loss: 0.6022 - accuracy: 0.7015\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 89/122 [====================&gt;.........] - ETA: 1:36 - loss: 0.6026 - accuracy: 0.7010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 90/122 [=====================&gt;........] - ETA: 1:33 - loss: 0.6030 - accuracy: 0.7004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 91/122 [=====================&gt;........] - ETA: 1:30 - loss: 0.6033 - accuracy: 0.6999\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 92/122 [=====================&gt;........] - ETA: 1:28 - loss: 0.6037 - accuracy: 0.6993\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 93/122 [=====================&gt;........] - ETA: 1:25 - loss: 0.6040 - accuracy: 0.6988\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 94/122 [======================&gt;.......] - ETA: 1:22 - loss: 0.6044 - accuracy: 0.6983\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 95/122 [======================&gt;.......] - ETA: 1:19 - loss: 0.6047 - accuracy: 0.6978\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 96/122 [======================&gt;.......] - ETA: 1:16 - loss: 0.6050 - accuracy: 0.6973\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 97/122 [======================&gt;.......] - ETA: 1:13 - loss: 0.6053 - accuracy: 0.6969\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 98/122 [=======================&gt;......] - ETA: 1:10 - loss: 0.6056 - accuracy: 0.6964\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       " 99/122 [=======================&gt;......] - ETA: 1:07 - loss: 0.6059 - accuracy: 0.6960\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "100/122 [=======================&gt;......] - ETA: 1:04 - loss: 0.6062 - accuracy: 0.6956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "101/122 [=======================&gt;......] - ETA: 1:01 - loss: 0.6065 - accuracy: 0.6952\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "102/122 [========================&gt;.....] - ETA: 58s - loss: 0.6067 - accuracy: 0.6948 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "103/122 [========================&gt;.....] - ETA: 55s - loss: 0.6070 - accuracy: 0.6944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "104/122 [========================&gt;.....] - ETA: 52s - loss: 0.6072 - accuracy: 0.6941\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "105/122 [========================&gt;.....] - ETA: 49s - loss: 0.6074 - accuracy: 0.6937\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "106/122 [=========================&gt;....] - ETA: 46s - loss: 0.6077 - accuracy: 0.6933\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "107/122 [=========================&gt;....] - ETA: 43s - loss: 0.6079 - accuracy: 0.6930\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "108/122 [=========================&gt;....] - ETA: 40s - loss: 0.6081 - accuracy: 0.6927\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "109/122 [=========================&gt;....] - ETA: 38s - loss: 0.6083 - accuracy: 0.6923\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "110/122 [==========================&gt;...] - ETA: 35s - loss: 0.6085 - accuracy: 0.6920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "111/122 [==========================&gt;...] - ETA: 32s - loss: 0.6087 - accuracy: 0.6917\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "112/122 [==========================&gt;...] - ETA: 29s - loss: 0.6089 - accuracy: 0.6915\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "113/122 [==========================&gt;...] - ETA: 26s - loss: 0.6091 - accuracy: 0.6912\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "114/122 [===========================&gt;..] - ETA: 23s - loss: 0.6093 - accuracy: 0.6909\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "115/122 [===========================&gt;..] - ETA: 20s - loss: 0.6094 - accuracy: 0.6906\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "116/122 [===========================&gt;..] - ETA: 17s - loss: 0.6096 - accuracy: 0.6904\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "117/122 [===========================&gt;..] - ETA: 14s - loss: 0.6098 - accuracy: 0.6901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "118/122 [============================&gt;.] - ETA: 11s - loss: 0.6100 - accuracy: 0.6898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "119/122 [============================&gt;.] - ETA: 8s - loss: 0.6102 - accuracy: 0.6896 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "120/122 [============================&gt;.] - ETA: 5s - loss: 0.6104 - accuracy: 0.6893\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "121/122 [============================&gt;.] - ETA: 2s - loss: 0.6105 - accuracy: 0.6890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - ETA: 0s - loss: 0.6107 - accuracy: 0.6888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
       "122/122 [==============================] - 376s 3s/step - loss: 0.6109 - accuracy: 0.6885 - val_loss: 0.6035 - val_accuracy: 0.6825\n",
       "INFO:tensorflow:Assets written to: /tmp/tmpgm6a7ubk/model/data/model/assets\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Epoch 1/10\n\r  1/122 [..............................] - ETA: 41:35 - loss: 0.6875 - accuracy: 0.6250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  2/122 [..............................] - ETA: 6:57 - loss: 1.8578 - accuracy: 0.6406 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  3/122 [..............................] - ETA: 6:37 - loss: 8.7227 - accuracy: 0.6007\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  4/122 [..............................] - ETA: 6:22 - loss: 11.5495 - accuracy: 0.5716\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  5/122 [&gt;.............................] - ETA: 6:09 - loss: 13.0087 - accuracy: 0.5573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  6/122 [&gt;.............................] - ETA: 6:02 - loss: 14.0899 - accuracy: 0.5460\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  7/122 [&gt;.............................] - ETA: 5:55 - loss: 14.8244 - accuracy: 0.5369\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  8/122 [&gt;.............................] - ETA: 5:49 - loss: 15.2905 - accuracy: 0.5303\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  9/122 [=&gt;............................] - ETA: 5:45 - loss: 15.4621 - accuracy: 0.5277\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 10/122 [=&gt;............................] - ETA: 5:39 - loss: 15.4527 - accuracy: 0.5243\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 11/122 [=&gt;............................] - ETA: 5:37 - loss: 15.3303 - accuracy: 0.5211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 12/122 [=&gt;............................] - ETA: 5:33 - loss: 15.1537 - accuracy: 0.5163\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 13/122 [==&gt;...........................] - ETA: 5:30 - loss: 14.9413 - accuracy: 0.5121\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 14/122 [==&gt;...........................] - ETA: 5:26 - loss: 14.7063 - accuracy: 0.5083\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 15/122 [==&gt;...........................] - ETA: 5:22 - loss: 14.4564 - accuracy: 0.5053\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 16/122 [==&gt;...........................] - ETA: 5:19 - loss: 14.1981 - accuracy: 0.5025\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 17/122 [===&gt;..........................] - ETA: 5:15 - loss: 13.9370 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 18/122 [===&gt;..........................] - ETA: 5:12 - loss: 13.6771 - accuracy: 0.4971\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 19/122 [===&gt;..........................] - ETA: 5:09 - loss: 13.4208 - accuracy: 0.4947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 20/122 [===&gt;..........................] - ETA: 5:06 - loss: 13.1699 - accuracy: 0.4926\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 21/122 [====&gt;.........................] - ETA: 5:02 - loss: 12.9255 - accuracy: 0.4907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 22/122 [====&gt;.........................] - ETA: 4:59 - loss: 12.6881 - accuracy: 0.4889\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 23/122 [====&gt;.........................] - ETA: 4:56 - loss: 12.4583 - accuracy: 0.4867\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 24/122 [====&gt;.........................] - ETA: 4:53 - loss: 12.2360 - accuracy: 0.4846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 25/122 [=====&gt;........................] - ETA: 4:49 - loss: 12.0212 - accuracy: 0.4826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 26/122 [=====&gt;........................] - ETA: 4:46 - loss: 11.8138 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 27/122 [=====&gt;........................] - ETA: 4:43 - loss: 11.6136 - accuracy: 0.4793\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 28/122 [=====&gt;........................] - ETA: 4:40 - loss: 11.4205 - accuracy: 0.4781\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 29/122 [======&gt;.......................] - ETA: 4:37 - loss: 11.2341 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 30/122 [======&gt;.......................] - ETA: 4:34 - loss: 11.0541 - accuracy: 0.4769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 31/122 [======&gt;.......................] - ETA: 4:30 - loss: 10.8805 - accuracy: 0.4768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 32/122 [======&gt;.......................] - ETA: 4:27 - loss: 10.7128 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 33/122 [=======&gt;......................] - ETA: 4:24 - loss: 10.5509 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 34/122 [=======&gt;......................] - ETA: 4:21 - loss: 10.3944 - accuracy: 0.4765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 35/122 [=======&gt;......................] - ETA: 4:18 - loss: 10.2432 - accuracy: 0.4764\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 36/122 [=======&gt;......................] - ETA: 4:15 - loss: 10.0970 - accuracy: 0.4763\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 37/122 [========&gt;.....................] - ETA: 4:12 - loss: 9.9555 - accuracy: 0.4761 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 38/122 [========&gt;.....................] - ETA: 4:10 - loss: 9.8187 - accuracy: 0.4760\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 39/122 [========&gt;.....................] - ETA: 4:07 - loss: 9.6861 - accuracy: 0.4760\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 40/122 [========&gt;.....................] - ETA: 4:04 - loss: 9.5577 - accuracy: 0.4761\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 41/122 [=========&gt;....................] - ETA: 4:01 - loss: 9.4333 - accuracy: 0.4762\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 42/122 [=========&gt;....................] - ETA: 3:58 - loss: 9.3127 - accuracy: 0.4763\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 43/122 [=========&gt;....................] - ETA: 3:54 - loss: 9.1956 - accuracy: 0.4764\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 44/122 [=========&gt;....................] - ETA: 3:51 - loss: 9.0821 - accuracy: 0.4765\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 45/122 [==========&gt;...................] - ETA: 3:48 - loss: 8.9719 - accuracy: 0.4766\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 46/122 [==========&gt;...................] - ETA: 3:45 - loss: 8.8648 - accuracy: 0.4767\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 47/122 [==========&gt;...................] - ETA: 3:42 - loss: 8.7608 - accuracy: 0.4769\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 48/122 [==========&gt;...................] - ETA: 3:39 - loss: 8.6597 - accuracy: 0.4770\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 49/122 [===========&gt;..................] - ETA: 3:36 - loss: 8.5613 - accuracy: 0.4772\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 50/122 [===========&gt;..................] - ETA: 3:33 - loss: 8.4657 - accuracy: 0.4773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 51/122 [===========&gt;..................] - ETA: 3:30 - loss: 8.3726 - accuracy: 0.4775\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 52/122 [===========&gt;..................] - ETA: 3:27 - loss: 8.2819 - accuracy: 0.4777\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 53/122 [============&gt;.................] - ETA: 3:24 - loss: 8.1937 - accuracy: 0.4779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 54/122 [============&gt;.................] - ETA: 3:21 - loss: 8.1077 - accuracy: 0.4782\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 55/122 [============&gt;.................] - ETA: 3:18 - loss: 8.0238 - accuracy: 0.4784\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 56/122 [============&gt;.................] - ETA: 3:15 - loss: 7.9421 - accuracy: 0.4787\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 57/122 [=============&gt;................] - ETA: 3:12 - loss: 7.8624 - accuracy: 0.4789\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 58/122 [=============&gt;................] - ETA: 3:09 - loss: 7.7846 - accuracy: 0.4792\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 59/122 [=============&gt;................] - ETA: 3:06 - loss: 7.7087 - accuracy: 0.4795\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 60/122 [=============&gt;................] - ETA: 3:03 - loss: 7.6347 - accuracy: 0.4798\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 61/122 [==============&gt;...............] - ETA: 3:00 - loss: 7.5625 - accuracy: 0.4801\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 62/122 [==============&gt;...............] - ETA: 2:57 - loss: 7.4919 - accuracy: 0.4804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 63/122 [==============&gt;...............] - ETA: 2:54 - loss: 7.4229 - accuracy: 0.4808\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 64/122 [==============&gt;...............] - ETA: 2:51 - loss: 7.3555 - accuracy: 0.4811\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 65/122 [==============&gt;...............] - ETA: 2:48 - loss: 7.2896 - accuracy: 0.4814\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 66/122 [===============&gt;..............] - ETA: 2:45 - loss: 7.2251 - accuracy: 0.4817\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 67/122 [===============&gt;..............] - ETA: 2:42 - loss: 7.1620 - accuracy: 0.4820\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 68/122 [===============&gt;..............] - ETA: 2:39 - loss: 7.1003 - accuracy: 0.4822\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 69/122 [===============&gt;..............] - ETA: 2:36 - loss: 7.0399 - accuracy: 0.4825\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 70/122 [================&gt;.............] - ETA: 2:33 - loss: 6.9808 - accuracy: 0.4828\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 71/122 [================&gt;.............] - ETA: 2:29 - loss: 6.9232 - accuracy: 0.4831\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 72/122 [================&gt;.............] - ETA: 2:26 - loss: 6.8668 - accuracy: 0.4833\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 73/122 [================&gt;.............] - ETA: 2:23 - loss: 6.8116 - accuracy: 0.4835\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 74/122 [=================&gt;............] - ETA: 2:20 - loss: 6.7574 - accuracy: 0.4838\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 75/122 [=================&gt;............] - ETA: 2:17 - loss: 6.7043 - accuracy: 0.4840\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 76/122 [=================&gt;............] - ETA: 2:14 - loss: 6.6522 - accuracy: 0.4843\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 77/122 [=================&gt;............] - ETA: 2:11 - loss: 6.6012 - accuracy: 0.4846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 78/122 [==================&gt;...........] - ETA: 2:08 - loss: 6.5511 - accuracy: 0.4848\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 79/122 [==================&gt;...........] - ETA: 2:05 - loss: 6.5019 - accuracy: 0.4851\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 80/122 [==================&gt;...........] - ETA: 2:02 - loss: 6.4537 - accuracy: 0.4853\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 81/122 [==================&gt;...........] - ETA: 1:59 - loss: 6.4064 - accuracy: 0.4856\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 82/122 [===================&gt;..........] - ETA: 1:56 - loss: 6.3599 - accuracy: 0.4858\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 83/122 [===================&gt;..........] - ETA: 1:53 - loss: 6.3143 - accuracy: 0.4861\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 84/122 [===================&gt;..........] - ETA: 1:51 - loss: 6.2695 - accuracy: 0.4863\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 85/122 [===================&gt;..........] - ETA: 1:48 - loss: 6.2254 - accuracy: 0.4866\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 86/122 [====================&gt;.........] - ETA: 1:45 - loss: 6.1822 - accuracy: 0.4869\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 87/122 [====================&gt;.........] - ETA: 1:42 - loss: 6.1397 - accuracy: 0.4872\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 88/122 [====================&gt;.........] - ETA: 1:39 - loss: 6.0979 - accuracy: 0.4875\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 89/122 [====================&gt;.........] - ETA: 1:36 - loss: 6.0569 - accuracy: 0.4878\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 90/122 [=====================&gt;........] - ETA: 1:33 - loss: 6.0165 - accuracy: 0.4880\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 91/122 [=====================&gt;........] - ETA: 1:30 - loss: 5.9769 - accuracy: 0.4883\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 92/122 [=====================&gt;........] - ETA: 1:27 - loss: 5.9378 - accuracy: 0.4885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 93/122 [=====================&gt;........] - ETA: 1:24 - loss: 5.8994 - accuracy: 0.4888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 94/122 [======================&gt;.......] - ETA: 1:21 - loss: 5.8617 - accuracy: 0.4890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 95/122 [======================&gt;.......] - ETA: 1:18 - loss: 5.8245 - accuracy: 0.4892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 96/122 [======================&gt;.......] - ETA: 1:15 - loss: 5.7880 - accuracy: 0.4895\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 97/122 [======================&gt;.......] - ETA: 1:12 - loss: 5.7520 - accuracy: 0.4897\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 98/122 [=======================&gt;......] - ETA: 1:09 - loss: 5.7166 - accuracy: 0.4899\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 99/122 [=======================&gt;......] - ETA: 1:06 - loss: 5.6817 - accuracy: 0.4901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r100/122 [=======================&gt;......] - ETA: 1:04 - loss: 5.6474 - accuracy: 0.4903\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r101/122 [=======================&gt;......] - ETA: 1:01 - loss: 5.6136 - accuracy: 0.4905\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r102/122 [========================&gt;.....] - ETA: 58s - loss: 5.5803 - accuracy: 0.4908 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r103/122 [========================&gt;.....] - ETA: 55s - loss: 5.5476 - accuracy: 0.4910\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r104/122 [========================&gt;.....] - ETA: 52s - loss: 5.5153 - accuracy: 0.4912\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r105/122 [========================&gt;.....] - ETA: 49s - loss: 5.4835 - accuracy: 0.4914\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r106/122 [=========================&gt;....] - ETA: 46s - loss: 5.4521 - accuracy: 0.4916\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r107/122 [=========================&gt;....] - ETA: 43s - loss: 5.4212 - accuracy: 0.4917\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r108/122 [=========================&gt;....] - ETA: 40s - loss: 5.3908 - accuracy: 0.4919\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r109/122 [=========================&gt;....] - ETA: 37s - loss: 5.3608 - accuracy: 0.4921\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r110/122 [==========================&gt;...] - ETA: 34s - loss: 5.3312 - accuracy: 0.4923\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r111/122 [==========================&gt;...] - ETA: 32s - loss: 5.3020 - accuracy: 0.4925\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r112/122 [==========================&gt;...] - ETA: 29s - loss: 5.2733 - accuracy: 0.4927\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r113/122 [==========================&gt;...] - ETA: 26s - loss: 5.2449 - accuracy: 0.4929\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r114/122 [===========================&gt;..] - ETA: 23s - loss: 5.2169 - accuracy: 0.4931\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r115/122 [===========================&gt;..] - ETA: 20s - loss: 5.1893 - accuracy: 0.4933\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r116/122 [===========================&gt;..] - ETA: 17s - loss: 5.1621 - accuracy: 0.4935\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r117/122 [===========================&gt;..] - ETA: 14s - loss: 5.1353 - accuracy: 0.4937\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r118/122 [============================&gt;.] - ETA: 11s - loss: 5.1088 - accuracy: 0.4939\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r119/122 [============================&gt;.] - ETA: 8s - loss: 5.0826 - accuracy: 0.4941 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r120/122 [============================&gt;.] - ETA: 5s - loss: 5.0568 - accuracy: 0.4943\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r121/122 [============================&gt;.] - ETA: 2s - loss: 5.0314 - accuracy: 0.4945\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - ETA: 0s - loss: 5.0063 - accuracy: 0.4947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - 395s 3s/step - loss: 4.9815 - accuracy: 0.4948 - val_loss: 0.6729 - val_accuracy: 0.5691\nEpoch 2/10\n\r  1/122 [..............................] - ETA: 30:46 - loss: 0.7115 - accuracy: 0.5000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  2/122 [..............................] - ETA: 6:10 - loss: 0.7063 - accuracy: 0.5312 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  3/122 [..............................] - ETA: 5:55 - loss: 0.7037 - accuracy: 0.5278\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  4/122 [..............................] - ETA: 5:48 - loss: 0.6997 - accuracy: 0.5365\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  5/122 [&gt;.............................] - ETA: 5:45 - loss: 0.6977 - accuracy: 0.5342\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  6/122 [&gt;.............................] - ETA: 5:41 - loss: 0.6964 - accuracy: 0.5319\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  7/122 [&gt;.............................] - ETA: 5:40 - loss: 0.6956 - accuracy: 0.5299\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  8/122 [&gt;.............................] - ETA: 5:36 - loss: 0.6951 - accuracy: 0.5281\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  9/122 [=&gt;............................] - ETA: 5:32 - loss: 0.6945 - accuracy: 0.5304\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 10/122 [=&gt;............................] - ETA: 5:29 - loss: 0.6938 - accuracy: 0.5324\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 11/122 [=&gt;............................] - ETA: 5:25 - loss: 0.6931 - accuracy: 0.5361\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 12/122 [=&gt;............................] - ETA: 5:22 - loss: 0.6925 - accuracy: 0.5392\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 13/122 [==&gt;...........................] - ETA: 5:18 - loss: 0.6918 - accuracy: 0.5429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 14/122 [==&gt;...........................] - ETA: 5:14 - loss: 0.6913 - accuracy: 0.5459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 15/122 [==&gt;...........................] - ETA: 5:10 - loss: 0.6908 - accuracy: 0.5489\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 16/122 [==&gt;...........................] - ETA: 5:08 - loss: 0.6903 - accuracy: 0.5520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 17/122 [===&gt;..........................] - ETA: 5:04 - loss: 0.6897 - accuracy: 0.5554\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 18/122 [===&gt;..........................] - ETA: 5:01 - loss: 0.6892 - accuracy: 0.5581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 19/122 [===&gt;..........................] - ETA: 4:58 - loss: 0.6888 - accuracy: 0.5606\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 20/122 [===&gt;..........................] - ETA: 4:55 - loss: 0.6884 - accuracy: 0.5629\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 21/122 [====&gt;.........................] - ETA: 4:52 - loss: 0.6881 - accuracy: 0.5643\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 22/122 [====&gt;.........................] - ETA: 4:50 - loss: 0.6879 - accuracy: 0.5653\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 23/122 [====&gt;.........................] - ETA: 4:47 - loss: 0.6878 - accuracy: 0.5664\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 24/122 [====&gt;.........................] - ETA: 4:44 - loss: 0.6876 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 25/122 [=====&gt;........................] - ETA: 4:41 - loss: 0.6875 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 26/122 [=====&gt;........................] - ETA: 4:38 - loss: 0.6874 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 27/122 [=====&gt;........................] - ETA: 4:35 - loss: 0.6872 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 28/122 [=====&gt;........................] - ETA: 4:32 - loss: 0.6871 - accuracy: 0.5690\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 29/122 [======&gt;.......................] - ETA: 4:29 - loss: 0.6869 - accuracy: 0.5693\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 30/122 [======&gt;.......................] - ETA: 4:26 - loss: 0.6868 - accuracy: 0.5696\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 31/122 [======&gt;.......................] - ETA: 4:23 - loss: 0.6866 - accuracy: 0.5698\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 32/122 [======&gt;.......................] - ETA: 4:20 - loss: 0.6865 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 33/122 [=======&gt;......................] - ETA: 4:18 - loss: 0.6863 - accuracy: 0.5700\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 34/122 [=======&gt;......................] - ETA: 4:15 - loss: 0.6862 - accuracy: 0.5702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 35/122 [=======&gt;......................] - ETA: 4:12 - loss: 0.6861 - accuracy: 0.5703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 36/122 [=======&gt;......................] - ETA: 4:09 - loss: 0.6860 - accuracy: 0.5705\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 37/122 [========&gt;.....................] - ETA: 4:06 - loss: 0.6859 - accuracy: 0.5707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 38/122 [========&gt;.....................] - ETA: 4:03 - loss: 0.6857 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 39/122 [========&gt;.....................] - ETA: 4:00 - loss: 0.6856 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 40/122 [========&gt;.....................] - ETA: 3:57 - loss: 0.6855 - accuracy: 0.5713\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 41/122 [=========&gt;....................] - ETA: 3:54 - loss: 0.6855 - accuracy: 0.5713\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 42/122 [=========&gt;....................] - ETA: 3:52 - loss: 0.6855 - accuracy: 0.5712\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 43/122 [=========&gt;....................] - ETA: 3:49 - loss: 0.6854 - accuracy: 0.5711\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 44/122 [=========&gt;....................] - ETA: 3:46 - loss: 0.6854 - accuracy: 0.5710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 45/122 [==========&gt;...................] - ETA: 3:43 - loss: 0.6854 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 46/122 [==========&gt;...................] - ETA: 3:40 - loss: 0.6854 - accuracy: 0.5709\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 47/122 [==========&gt;...................] - ETA: 3:37 - loss: 0.6854 - accuracy: 0.5708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 48/122 [==========&gt;...................] - ETA: 3:34 - loss: 0.6853 - accuracy: 0.5707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 49/122 [===========&gt;..................] - ETA: 3:31 - loss: 0.6853 - accuracy: 0.5706\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 50/122 [===========&gt;..................] - ETA: 3:28 - loss: 0.6853 - accuracy: 0.5704\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 51/122 [===========&gt;..................] - ETA: 3:25 - loss: 0.6853 - accuracy: 0.5702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 52/122 [===========&gt;..................] - ETA: 3:22 - loss: 0.6853 - accuracy: 0.5699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 53/122 [============&gt;.................] - ETA: 3:19 - loss: 0.6853 - accuracy: 0.5697\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 54/122 [============&gt;.................] - ETA: 3:16 - loss: 0.6853 - accuracy: 0.5694\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 55/122 [============&gt;.................] - ETA: 3:13 - loss: 0.6853 - accuracy: 0.5692\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 56/122 [============&gt;.................] - ETA: 3:11 - loss: 0.6854 - accuracy: 0.5690\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 57/122 [=============&gt;................] - ETA: 3:08 - loss: 0.6854 - accuracy: 0.5688\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 58/122 [=============&gt;................] - ETA: 3:05 - loss: 0.6854 - accuracy: 0.5687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 59/122 [=============&gt;................] - ETA: 3:02 - loss: 0.6854 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 60/122 [=============&gt;................] - ETA: 2:59 - loss: 0.6854 - accuracy: 0.5686\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 61/122 [==============&gt;...............] - ETA: 2:56 - loss: 0.6854 - accuracy: 0.5685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 62/122 [==============&gt;...............] - ETA: 2:53 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 63/122 [==============&gt;...............] - ETA: 2:50 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 64/122 [==============&gt;...............] - ETA: 2:47 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 65/122 [==============&gt;...............] - ETA: 2:43 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 66/122 [===============&gt;..............] - ETA: 2:40 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 67/122 [===============&gt;..............] - ETA: 2:37 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 68/122 [===============&gt;..............] - ETA: 2:34 - loss: 0.6854 - accuracy: 0.5684\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 69/122 [===============&gt;..............] - ETA: 2:31 - loss: 0.6854 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 70/122 [================&gt;.............] - ETA: 2:28 - loss: 0.6853 - accuracy: 0.5683\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 71/122 [================&gt;.............] - ETA: 2:26 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 72/122 [================&gt;.............] - ETA: 2:23 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 73/122 [================&gt;.............] - ETA: 2:20 - loss: 0.6853 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 74/122 [=================&gt;............] - ETA: 2:17 - loss: 0.6852 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 75/122 [=================&gt;............] - ETA: 2:14 - loss: 0.6852 - accuracy: 0.5682\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 76/122 [=================&gt;............] - ETA: 2:11 - loss: 0.6852 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 77/122 [=================&gt;............] - ETA: 2:08 - loss: 0.6851 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 78/122 [==================&gt;...........] - ETA: 2:06 - loss: 0.6851 - accuracy: 0.5681\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 79/122 [==================&gt;...........] - ETA: 2:03 - loss: 0.6851 - accuracy: 0.5680\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 80/122 [==================&gt;...........] - ETA: 2:00 - loss: 0.6850 - accuracy: 0.5679\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 81/122 [==================&gt;...........] - ETA: 1:57 - loss: 0.6850 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 82/122 [===================&gt;..........] - ETA: 1:54 - loss: 0.6850 - accuracy: 0.5678\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 83/122 [===================&gt;..........] - ETA: 1:51 - loss: 0.6850 - accuracy: 0.5677\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 84/122 [===================&gt;..........] - ETA: 1:48 - loss: 0.6850 - accuracy: 0.5676\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 85/122 [===================&gt;..........] - ETA: 1:45 - loss: 0.6850 - accuracy: 0.5675\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 86/122 [====================&gt;.........] - ETA: 1:43 - loss: 0.6850 - accuracy: 0.5674\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 87/122 [====================&gt;.........] - ETA: 1:40 - loss: 0.6850 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 88/122 [====================&gt;.........] - ETA: 1:37 - loss: 0.6849 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 89/122 [====================&gt;.........] - ETA: 1:34 - loss: 0.6849 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 90/122 [=====================&gt;........] - ETA: 1:31 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 91/122 [=====================&gt;........] - ETA: 1:28 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 92/122 [=====================&gt;........] - ETA: 1:25 - loss: 0.6849 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 93/122 [=====================&gt;........] - ETA: 1:22 - loss: 0.6849 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 94/122 [======================&gt;.......] - ETA: 1:19 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 95/122 [======================&gt;.......] - ETA: 1:17 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 96/122 [======================&gt;.......] - ETA: 1:14 - loss: 0.6848 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 97/122 [======================&gt;.......] - ETA: 1:11 - loss: 0.6848 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 98/122 [=======================&gt;......] - ETA: 1:08 - loss: 0.6847 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 99/122 [=======================&gt;......] - ETA: 1:05 - loss: 0.6847 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r100/122 [=======================&gt;......] - ETA: 1:02 - loss: 0.6847 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r101/122 [=======================&gt;......] - ETA: 59s - loss: 0.6847 - accuracy: 0.5668 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r102/122 [========================&gt;.....] - ETA: 57s - loss: 0.6847 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r103/122 [========================&gt;.....] - ETA: 54s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r104/122 [========================&gt;.....] - ETA: 51s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r105/122 [========================&gt;.....] - ETA: 48s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r106/122 [=========================&gt;....] - ETA: 45s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r107/122 [=========================&gt;....] - ETA: 42s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r108/122 [=========================&gt;....] - ETA: 39s - loss: 0.6846 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r109/122 [=========================&gt;....] - ETA: 37s - loss: 0.6845 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r110/122 [==========================&gt;...] - ETA: 34s - loss: 0.6845 - accuracy: 0.5668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r111/122 [==========================&gt;...] - ETA: 31s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r112/122 [==========================&gt;...] - ETA: 28s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r113/122 [==========================&gt;...] - ETA: 25s - loss: 0.6845 - accuracy: 0.5669\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r114/122 [===========================&gt;..] - ETA: 22s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r115/122 [===========================&gt;..] - ETA: 19s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r116/122 [===========================&gt;..] - ETA: 17s - loss: 0.6844 - accuracy: 0.5670\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r117/122 [===========================&gt;..] - ETA: 14s - loss: 0.6843 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r118/122 [============================&gt;.] - ETA: 11s - loss: 0.6843 - accuracy: 0.5671\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r119/122 [============================&gt;.] - ETA: 8s - loss: 0.6842 - accuracy: 0.5672 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r120/122 [============================&gt;.] - ETA: 5s - loss: 0.6842 - accuracy: 0.5672\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r121/122 [============================&gt;.] - ETA: 2s - loss: 0.6842 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - ETA: 0s - loss: 0.6841 - accuracy: 0.5673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - 380s 3s/step - loss: 0.6841 - accuracy: 0.5674 - val_loss: 0.6560 - val_accuracy: 0.6062\n\n*** WARNING: skipped 148412 bytes of output ***\n\nEpoch 10/10\n\r  1/122 [..............................] - ETA: 5:49 - loss: 0.5531 - accuracy: 0.7500\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  2/122 [..............................] - ETA: 2:15 - loss: 0.5380 - accuracy: 0.7750\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  3/122 [..............................] - ETA: 3:58 - loss: 0.5192 - accuracy: 0.8037\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  4/122 [..............................] - ETA: 4:31 - loss: 0.5153 - accuracy: 0.8095\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  5/122 [&gt;.............................] - ETA: 4:44 - loss: 0.5122 - accuracy: 0.8123\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  6/122 [&gt;.............................] - ETA: 4:54 - loss: 0.5126 - accuracy: 0.8118\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  7/122 [&gt;.............................] - ETA: 5:00 - loss: 0.5129 - accuracy: 0.8116\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  8/122 [&gt;.............................] - ETA: 5:03 - loss: 0.5149 - accuracy: 0.8104\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r  9/122 [=&gt;............................] - ETA: 5:04 - loss: 0.5171 - accuracy: 0.8079\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 10/122 [=&gt;............................] - ETA: 5:04 - loss: 0.5197 - accuracy: 0.8054\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 11/122 [=&gt;............................] - ETA: 5:02 - loss: 0.5220 - accuracy: 0.8026\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 12/122 [=&gt;............................] - ETA: 5:10 - loss: 0.5229 - accuracy: 0.8006\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 13/122 [==&gt;...........................] - ETA: 5:10 - loss: 0.5254 - accuracy: 0.7974\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 14/122 [==&gt;...........................] - ETA: 5:08 - loss: 0.5280 - accuracy: 0.7947\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 15/122 [==&gt;...........................] - ETA: 5:06 - loss: 0.5303 - accuracy: 0.7920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 16/122 [==&gt;...........................] - ETA: 5:03 - loss: 0.5328 - accuracy: 0.7892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 17/122 [===&gt;..........................] - ETA: 5:00 - loss: 0.5352 - accuracy: 0.7864\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 18/122 [===&gt;..........................] - ETA: 4:59 - loss: 0.5378 - accuracy: 0.7834\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 19/122 [===&gt;..........................] - ETA: 4:56 - loss: 0.5403 - accuracy: 0.7804\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 20/122 [===&gt;..........................] - ETA: 4:53 - loss: 0.5423 - accuracy: 0.7779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 21/122 [====&gt;.........................] - ETA: 4:50 - loss: 0.5445 - accuracy: 0.7752\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 22/122 [====&gt;.........................] - ETA: 4:48 - loss: 0.5467 - accuracy: 0.7729\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 23/122 [====&gt;.........................] - ETA: 4:46 - loss: 0.5487 - accuracy: 0.7707\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 24/122 [====&gt;.........................] - ETA: 4:42 - loss: 0.5504 - accuracy: 0.7687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 25/122 [=====&gt;........................] - ETA: 4:40 - loss: 0.5520 - accuracy: 0.7668\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 26/122 [=====&gt;........................] - ETA: 4:37 - loss: 0.5536 - accuracy: 0.7650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 27/122 [=====&gt;........................] - ETA: 4:34 - loss: 0.5550 - accuracy: 0.7635\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 28/122 [=====&gt;........................] - ETA: 4:50 - loss: 0.5564 - accuracy: 0.7619\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 29/122 [======&gt;.......................] - ETA: 4:47 - loss: 0.5577 - accuracy: 0.7604\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 30/122 [======&gt;.......................] - ETA: 4:43 - loss: 0.5591 - accuracy: 0.7588\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 31/122 [======&gt;.......................] - ETA: 4:39 - loss: 0.5605 - accuracy: 0.7573\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 32/122 [======&gt;.......................] - ETA: 4:36 - loss: 0.5618 - accuracy: 0.7557\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 33/122 [=======&gt;......................] - ETA: 4:32 - loss: 0.5631 - accuracy: 0.7542\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 34/122 [=======&gt;......................] - ETA: 4:29 - loss: 0.5642 - accuracy: 0.7527\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 35/122 [=======&gt;......................] - ETA: 4:25 - loss: 0.5654 - accuracy: 0.7512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 36/122 [=======&gt;......................] - ETA: 4:22 - loss: 0.5665 - accuracy: 0.7498\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 37/122 [========&gt;.....................] - ETA: 4:18 - loss: 0.5676 - accuracy: 0.7485\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 38/122 [========&gt;.....................] - ETA: 4:15 - loss: 0.5687 - accuracy: 0.7472\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 39/122 [========&gt;.....................] - ETA: 4:12 - loss: 0.5696 - accuracy: 0.7459\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 40/122 [========&gt;.....................] - ETA: 4:08 - loss: 0.5707 - accuracy: 0.7445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 41/122 [=========&gt;....................] - ETA: 4:05 - loss: 0.5718 - accuracy: 0.7431\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 42/122 [=========&gt;....................] - ETA: 4:01 - loss: 0.5728 - accuracy: 0.7418\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 43/122 [=========&gt;....................] - ETA: 3:58 - loss: 0.5739 - accuracy: 0.7405\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 44/122 [=========&gt;....................] - ETA: 3:55 - loss: 0.5748 - accuracy: 0.7393\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 45/122 [==========&gt;...................] - ETA: 3:51 - loss: 0.5757 - accuracy: 0.7383\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 46/122 [==========&gt;...................] - ETA: 3:48 - loss: 0.5766 - accuracy: 0.7372\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 47/122 [==========&gt;...................] - ETA: 3:45 - loss: 0.5774 - accuracy: 0.7362\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 48/122 [==========&gt;...................] - ETA: 3:42 - loss: 0.5783 - accuracy: 0.7351\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 49/122 [===========&gt;..................] - ETA: 3:38 - loss: 0.5792 - accuracy: 0.7340\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 50/122 [===========&gt;..................] - ETA: 3:35 - loss: 0.5801 - accuracy: 0.7328\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 51/122 [===========&gt;..................] - ETA: 3:32 - loss: 0.5810 - accuracy: 0.7316\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 52/122 [===========&gt;..................] - ETA: 3:29 - loss: 0.5819 - accuracy: 0.7305\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 53/122 [============&gt;.................] - ETA: 3:26 - loss: 0.5827 - accuracy: 0.7295\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 54/122 [============&gt;.................] - ETA: 3:23 - loss: 0.5836 - accuracy: 0.7284\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 55/122 [============&gt;.................] - ETA: 3:19 - loss: 0.5844 - accuracy: 0.7273\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 56/122 [============&gt;.................] - ETA: 3:16 - loss: 0.5852 - accuracy: 0.7262\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 57/122 [=============&gt;................] - ETA: 3:13 - loss: 0.5860 - accuracy: 0.7252\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 58/122 [=============&gt;................] - ETA: 3:10 - loss: 0.5868 - accuracy: 0.7242\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 59/122 [=============&gt;................] - ETA: 3:07 - loss: 0.5876 - accuracy: 0.7231\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 60/122 [=============&gt;................] - ETA: 3:04 - loss: 0.5883 - accuracy: 0.7221\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 61/122 [==============&gt;...............] - ETA: 3:01 - loss: 0.5890 - accuracy: 0.7211\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 62/122 [==============&gt;...............] - ETA: 2:58 - loss: 0.5898 - accuracy: 0.7200\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 63/122 [==============&gt;...............] - ETA: 2:55 - loss: 0.5905 - accuracy: 0.7190\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 64/122 [==============&gt;...............] - ETA: 2:52 - loss: 0.5911 - accuracy: 0.7180\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 65/122 [==============&gt;...............] - ETA: 2:49 - loss: 0.5918 - accuracy: 0.7171\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 66/122 [===============&gt;..............] - ETA: 2:45 - loss: 0.5924 - accuracy: 0.7161\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 67/122 [===============&gt;..............] - ETA: 2:42 - loss: 0.5930 - accuracy: 0.7153\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 68/122 [===============&gt;..............] - ETA: 2:39 - loss: 0.5936 - accuracy: 0.7144\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 69/122 [===============&gt;..............] - ETA: 2:36 - loss: 0.5941 - accuracy: 0.7136\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 70/122 [================&gt;.............] - ETA: 2:33 - loss: 0.5947 - accuracy: 0.7128\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 71/122 [================&gt;.............] - ETA: 2:30 - loss: 0.5952 - accuracy: 0.7121\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 72/122 [================&gt;.............] - ETA: 2:27 - loss: 0.5956 - accuracy: 0.7113\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 73/122 [================&gt;.............] - ETA: 2:24 - loss: 0.5961 - accuracy: 0.7106\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 74/122 [=================&gt;............] - ETA: 2:21 - loss: 0.5966 - accuracy: 0.7099\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 75/122 [=================&gt;............] - ETA: 2:18 - loss: 0.5970 - accuracy: 0.7093\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 76/122 [=================&gt;............] - ETA: 2:15 - loss: 0.5975 - accuracy: 0.7086\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 77/122 [=================&gt;............] - ETA: 2:12 - loss: 0.5979 - accuracy: 0.7080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 78/122 [==================&gt;...........] - ETA: 2:09 - loss: 0.5983 - accuracy: 0.7074\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 79/122 [==================&gt;...........] - ETA: 2:06 - loss: 0.5987 - accuracy: 0.7068\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 80/122 [==================&gt;...........] - ETA: 2:03 - loss: 0.5991 - accuracy: 0.7062\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 81/122 [==================&gt;...........] - ETA: 2:00 - loss: 0.5995 - accuracy: 0.7057\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 82/122 [===================&gt;..........] - ETA: 1:57 - loss: 0.5999 - accuracy: 0.7051\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 83/122 [===================&gt;..........] - ETA: 1:54 - loss: 0.6003 - accuracy: 0.7045\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 84/122 [===================&gt;..........] - ETA: 1:51 - loss: 0.6007 - accuracy: 0.7040\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 85/122 [===================&gt;..........] - ETA: 1:48 - loss: 0.6011 - accuracy: 0.7033\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 86/122 [====================&gt;.........] - ETA: 1:45 - loss: 0.6015 - accuracy: 0.7027\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 87/122 [====================&gt;.........] - ETA: 1:42 - loss: 0.6019 - accuracy: 0.7021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 88/122 [====================&gt;.........] - ETA: 1:39 - loss: 0.6022 - accuracy: 0.7015\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 89/122 [====================&gt;.........] - ETA: 1:36 - loss: 0.6026 - accuracy: 0.7010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 90/122 [=====================&gt;........] - ETA: 1:33 - loss: 0.6030 - accuracy: 0.7004\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 91/122 [=====================&gt;........] - ETA: 1:30 - loss: 0.6033 - accuracy: 0.6999\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 92/122 [=====================&gt;........] - ETA: 1:28 - loss: 0.6037 - accuracy: 0.6993\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 93/122 [=====================&gt;........] - ETA: 1:25 - loss: 0.6040 - accuracy: 0.6988\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 94/122 [======================&gt;.......] - ETA: 1:22 - loss: 0.6044 - accuracy: 0.6983\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 95/122 [======================&gt;.......] - ETA: 1:19 - loss: 0.6047 - accuracy: 0.6978\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 96/122 [======================&gt;.......] - ETA: 1:16 - loss: 0.6050 - accuracy: 0.6973\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 97/122 [======================&gt;.......] - ETA: 1:13 - loss: 0.6053 - accuracy: 0.6969\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 98/122 [=======================&gt;......] - ETA: 1:10 - loss: 0.6056 - accuracy: 0.6964\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 99/122 [=======================&gt;......] - ETA: 1:07 - loss: 0.6059 - accuracy: 0.6960\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r100/122 [=======================&gt;......] - ETA: 1:04 - loss: 0.6062 - accuracy: 0.6956\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r101/122 [=======================&gt;......] - ETA: 1:01 - loss: 0.6065 - accuracy: 0.6952\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r102/122 [========================&gt;.....] - ETA: 58s - loss: 0.6067 - accuracy: 0.6948 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r103/122 [========================&gt;.....] - ETA: 55s - loss: 0.6070 - accuracy: 0.6944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r104/122 [========================&gt;.....] - ETA: 52s - loss: 0.6072 - accuracy: 0.6941\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r105/122 [========================&gt;.....] - ETA: 49s - loss: 0.6074 - accuracy: 0.6937\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r106/122 [=========================&gt;....] - ETA: 46s - loss: 0.6077 - accuracy: 0.6933\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r107/122 [=========================&gt;....] - ETA: 43s - loss: 0.6079 - accuracy: 0.6930\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r108/122 [=========================&gt;....] - ETA: 40s - loss: 0.6081 - accuracy: 0.6927\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r109/122 [=========================&gt;....] - ETA: 38s - loss: 0.6083 - accuracy: 0.6923\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r110/122 [==========================&gt;...] - ETA: 35s - loss: 0.6085 - accuracy: 0.6920\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r111/122 [==========================&gt;...] - ETA: 32s - loss: 0.6087 - accuracy: 0.6917\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r112/122 [==========================&gt;...] - ETA: 29s - loss: 0.6089 - accuracy: 0.6915\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r113/122 [==========================&gt;...] - ETA: 26s - loss: 0.6091 - accuracy: 0.6912\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r114/122 [===========================&gt;..] - ETA: 23s - loss: 0.6093 - accuracy: 0.6909\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r115/122 [===========================&gt;..] - ETA: 20s - loss: 0.6094 - accuracy: 0.6906\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r116/122 [===========================&gt;..] - ETA: 17s - loss: 0.6096 - accuracy: 0.6904\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r117/122 [===========================&gt;..] - ETA: 14s - loss: 0.6098 - accuracy: 0.6901\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r118/122 [============================&gt;.] - ETA: 11s - loss: 0.6100 - accuracy: 0.6898\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r119/122 [============================&gt;.] - ETA: 8s - loss: 0.6102 - accuracy: 0.6896 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r120/122 [============================&gt;.] - ETA: 5s - loss: 0.6104 - accuracy: 0.6893\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r121/122 [============================&gt;.] - ETA: 2s - loss: 0.6105 - accuracy: 0.6890\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - ETA: 0s - loss: 0.6107 - accuracy: 0.6888\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r122/122 [==============================] - 376s 3s/step - loss: 0.6109 - accuracy: 0.6885 - val_loss: 0.6035 - val_accuracy: 0.6825\nINFO:tensorflow:Assets written to: /tmp/tmpgm6a7ubk/model/data/model/assets\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Train the model\n",
    "# with mlflow.start_run():\n",
    "#   epochs = 10\n",
    "#   history = model.fit(\n",
    "#     train_ds,\n",
    "#     validation_data=val_ds,\n",
    "#     epochs=epochs\n",
    "#   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "a88ba62e-bd98-43aa-bbd9-0ff5766567dc",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "/plots/98f40999-5559-47df-a39d-d7e9567e2843.png",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "image"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Visual the result\n",
    "# acc = history.history['accuracy']\n",
    "# val_acc = history.history['val_accuracy']\n",
    "\n",
    "# loss = history.history['loss']\n",
    "# val_loss = history.history['val_loss']\n",
    "\n",
    "# epochs_range = range(epochs)\n",
    "\n",
    "# plt.figure(figsize=(8, 8))\n",
    "# plt.subplot(1, 2, 1)\n",
    "# plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "# plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "# plt.legend(loc='lower right')\n",
    "# plt.title('Training and Validation Accuracy')\n",
    "\n",
    "# plt.subplot(1, 2, 2)\n",
    "# plt.plot(epochs_range, loss, label='Training Loss')\n",
    "# plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "# plt.legend(loc='upper right')\n",
    "# plt.title('Training and Validation Loss')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "6906bbba-ef19-4be4-b400-caaf63ed982d",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">INFO:tensorflow:Assets written to: /dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1a/assets\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">INFO:tensorflow:Assets written to: /dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1a/assets\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# Save the model artifact.\n",
    "# model.save('/dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "49a69939-18fb-483d-9e97-4c3a5de1e519",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[33]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/&#39;, name=&#39;basic_neural_v1/&#39;, size=0),\n",
       " FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1/&#39;, name=&#39;basic_neural_v1_1/&#39;, size=0),\n",
       " FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1a/&#39;, name=&#39;basic_neural_v1_1a/&#39;, size=0)]</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[33]: [FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/&#39;, name=&#39;basic_neural_v1/&#39;, size=0),\n FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1/&#39;, name=&#39;basic_neural_v1_1/&#39;, size=0),\n FileInfo(path=&#39;dbfs:/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1_1a/&#39;, name=&#39;basic_neural_v1_1a/&#39;, size=0)]</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data augmentation and dropout\n",
    "# List file in ADLS\n",
    "# dbutils.fs.ls(\"/mnt/eason/pneumonia_classification_challenge/models/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "1d589f96-efbd-4772-a6d2-43308f53e050",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reload trained model\n",
    "model = tf.keras.models.load_model(\"/dbfs/mnt/eason/pneumonia_classification_challenge/models/basic_neural_v1/\")\n",
    "class_names = ['normal', 'pneumonia']\n",
    "img_height = 180\n",
    "img_width = 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "9acc9322-d247-4f37-8cc2-5a3a9812e235",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[58]: </div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[58]: </div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CXR_test_519.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CXR_test_578.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CXR_test_359.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CXR_test_573.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CXR_test_471.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>filename</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CXR_test_519.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CXR_test_578.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CXR_test_359.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CXR_test_573.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CXR_test_471.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "datasetInfos": [],
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       "textData": null,
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      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading the order of the image's name that has been provided\n",
    "test_image_order = pd.read_csv(\"/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test.csv\")\n",
    "test_image_order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "10de1c34-afbb-4125-b877-9ce15a9cf91b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
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       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
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       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct file path of images to score.\n",
    "file_paths = [[fname, '/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/' + fname] for fname in test_image_order['filename']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "f74b0ccb-a02a-429e-a764-9faf4054c880",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
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       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Number of image names i.e.  606 matches the number of file paths i.e.  606\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Number of image names i.e.  606 matches the number of file paths i.e.  606\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confirm if number of images is same as number of labels given.\n",
    "if len(test_image_order) == len(file_paths):\n",
    "    print('Number of image names i.e. ', len(test_image_order), 'matches the number of file paths i.e. ', len(file_paths))\n",
    "else:\n",
    "    print('Number of image names does not match the number of filepaths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "d6a2df5d-6318-4385-9bf5-1302411182eb",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
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       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[61]: </div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[61]: </div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>filepaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CXR_test_519.png</td>\n",
       "      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CXR_test_578.png</td>\n",
       "      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CXR_test_359.png</td>\n",
       "      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CXR_test_573.png</td>\n",
       "      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CXR_test_471.png</td>\n",
       "      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>filename</th>\n      <th>filepaths</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CXR_test_519.png</td>\n      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CXR_test_578.png</td>\n      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CXR_test_359.png</td>\n      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CXR_test_573.png</td>\n      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CXR_test_471.png</td>\n      <td>/dbfs/mnt/eason/pneumonia_classifica...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "textData": null,
       "type": "htmlSandbox"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct full list of file paths to score in dataframe.\n",
    "test_images = pd.DataFrame(file_paths, columns=['filename', 'filepaths'])\n",
    "test_images.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "86139e13-4f56-4b7c-9a26-2dedff1c65bf",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
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       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">This image most likely belongs to normal with a 75.34 percent confidence.\n",
       "</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">This image most likely belongs to normal with a 75.34 percent confidence.\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Randomly pick one sample image to score.\n",
    "img_height = 180\n",
    "img_width = 180\n",
    "\n",
    "test_file_path = \"/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_15.png\"\n",
    "\n",
    "img = keras.preprocessing.image.load_img(\n",
    "    test_file_path, target_size=(img_height, img_width), color_mode=\"grayscale\"\n",
    ")\n",
    "img_array = keras.preprocessing.image.img_to_array(img)\n",
    "img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
    "\n",
    "predictions = model.predict(img_array)\n",
    "score = tf.nn.softmax(predictions[0])\n",
    "\n",
    "print(\n",
    "    \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n",
    "    .format(class_names[np.argmax(score)], 100 * np.max(score))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "ad1357a1-9809-43be-800a-e3d4c6f1ab5f",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\"></div>",
       "datasetInfos": [],
       "metadata": {},
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       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making variable from full list of file paths.\n",
    "test_image_file_paths = test_images['filepaths']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "6151ea01-2a69-4552-b53f-7931f7f043e6",
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     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_519.png pneumonia 67.68\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_578.png pneumonia 50.65\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_359.png normal 68.83\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_573.png pneumonia 62.3\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_471.png normal 73.73\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_418.png normal 52.01\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_597.png pneumonia 51.15\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_372.png normal 71.98\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_264.png normal 57.1\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_262.png normal 80.5\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_54.png normal 63.96\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_521.png normal 71.6\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_500.png pneumonia 52.14\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_585.png pneumonia 61.85\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_216.png normal 61.54\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_445.png normal 68.86\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_63.png pneumonia 65.29\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_195.png normal 58.81\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_84.png pneumonia 61.42\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_274.png normal 73.56\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_346.png normal 62.89\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_385.png normal 50.94\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_181.png normal 79.31\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_453.png pneumonia 55.26\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_407.png normal 67.08\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_560.png pneumonia 63.48\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_448.png pneumonia 65.17\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_390.png normal 67.61\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_243.png pneumonia 54.78\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_130.png normal 74.22\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_143.png normal 80.29\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_309.png normal 61.96\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_293.png normal 77.07\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_23.png normal 85.41\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_57.png pneumonia 60.29\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_357.png normal 67.6\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_161.png normal 70.62\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_6.png pneumonia 53.56\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_556.png pneumonia 52.24\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_473.png normal 51.08\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_56.png normal 65.49\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_488.png normal 77.34\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_494.png normal 65.18\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_434.png normal 70.72\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_85.png pneumonia 52.64\n",
       "/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_225.png pneumonia 64.85\n",
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       "\n",
       "*** WARNING: skipped 19342 bytes of output ***\n",
       "\n",
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68.65\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_109.png normal 56.91\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_485.png normal 81.97\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_105.png normal 75.1\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_422.png normal 54.08\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_191.png pneumonia 68.45\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_362.png normal 61.25\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_474.png pneumonia 61.95\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_603.png normal 60.06\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_388.png normal 59.94\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_281.png normal 54.28\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_417.png normal 73.8\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_120.png normal 70.63\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_555.png normal 61.5\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_366.png normal 62.14\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_440.png normal 67.15\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_258.png normal 59.87\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_126.png pneumonia 58.92\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_39.png normal 57.82\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_260.png normal 51.62\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_416.png normal 73.25\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_222.png normal 54.59\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_328.png pneumonia 68.22\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_58.png normal 69.58\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_248.png normal 67.75\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_409.png pneumonia 64.34\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_301.png normal 69.75\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_601.png pneumonia 56.46\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_182.png pneumonia 64.82\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_443.png normal 78.15\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_310.png normal 69.14\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_162.png normal 78.98\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_526.png normal 54.95\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_300.png normal 72.28\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_326.png pneumonia 70.68\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_231.png pneumonia 59.15\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_568.png normal 61.98\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_410.png normal 62.55\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_317.png normal 50.46\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_18.png normal 84.82\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_141.png normal 51.72\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_86.png pneumonia 60.67\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_423.png normal 68.41\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_278.png normal 78.76\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_405.png pneumonia 51.98\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_96.png normal 66.97\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_358.png normal 86.73\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_193.png normal 78.58\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_543.png normal 79.56\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_2.png normal 75.91\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_41.png pneumonia 66.11\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_170.png normal 66.12\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_131.png pneumonia 54.56\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_61.png normal 75.24\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_207.png normal 54.33\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_221.png normal 54.03\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_542.png normal 80.14\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_398.png normal 65.86\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_43.png normal 65.83\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_344.png normal 57.69\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_220.png normal 73.41\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_3.png normal 56.06\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_511.png pneumonia 57.13\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_295.png normal 86.73\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_546.png normal 53.13\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_179.png pneumonia 51.02\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_498.png pneumonia 52.8\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_374.png pneumonia 72.41\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_268.png normal 69.13\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_571.png normal 86.38\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_192.png pneumonia 69.96\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_149.png normal 70.82\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_297.png pneumonia 76.92\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_343.png normal 79.0\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_489.png normal 66.23\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_394.png normal 55.63\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_425.png normal 55.67\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_24.png normal 55.98\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_491.png pneumonia 50.65\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_311.png normal 65.33\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_461.png normal 72.07\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_108.png normal 66.42\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_574.png normal 65.76\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_187.png normal 73.92\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_255.png normal 81.0\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_294.png normal 59.07\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_330.png normal 57.19\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_279.png pneumonia 59.49\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_321.png normal 52.42\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_254.png normal 72.18\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_495.png normal 80.79\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_186.png normal 76.25\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_210.png normal 88.49\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_320.png normal 51.31\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_570.png normal 66.15\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_336.png pneumonia 58.86\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_547.png pneumonia 64.39\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_441.png normal 69.57\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_40.png pneumonia 61.45\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_153.png normal 78.07\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_50.png normal 59.27\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_90.png normal 74.7\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_205.png normal 62.98\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_163.png normal 56.64\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_516.png normal 71.39\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_211.png normal 69.29\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_49.png normal 52.73\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_275.png normal 60.19\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_197.png normal 53.76\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_196.png pneumonia 63.53\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_180.png normal 63.0\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_95.png pneumonia 58.04\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_28.png pneumonia 63.45\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_496.png normal 76.5\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_318.png normal 54.86\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_395.png normal 65.53\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_386.png normal 74.96\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_15.png normal 75.34\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_510.png normal 64.4\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_144.png pneumonia 71.3\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_544.png normal 50.85\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_403.png normal 69.47\n/dbfs/mnt/eason/pneumonia_classification_challenge/pneumonia_dataset/test/CXR_test_404.png pneumonia 60.33\n</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Score all files and save the result.\n",
    "img_height = 180\n",
    "img_width = 180\n",
    "\n",
    "for test_image in test_image_file_paths:\n",
    "    img = keras.preprocessing.image.load_img(\n",
    "        test_image, target_size=(img_height, img_width), color_mode=\"grayscale\"\n",
    "    )\n",
    "    img_array = keras.preprocessing.image.img_to_array(img)\n",
    "    img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
    "\n",
    "    predictions = model.predict(img_array)\n",
    "    score = tf.nn.softmax(predictions[0])\n",
    "    predict_class = class_names[np.argmax(score)]\n",
    "    predict_confident = round(100 * np.max(score),2)\n",
    "    print(test_image+\" \"+str(predict_class)+\" \"+str(predict_confident))\n",
    "    #print(predict_class)\n",
    "    #print(predict_confident)\n",
    "    result_file_path = \"/tmp/result_v1.csv\"\n",
    "    with open(result_file_path, 'a', newline='') as file:\n",
    "        writer = csv.writer(file)\n",
    "        writer.writerow([test_image, predict_class, predict_confident])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "23e45181-4082-4d2b-b244-027bb12d6f67",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[98]: True</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[98]: True</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Move temporary save result file in tmp to ADLS.\n",
    "dbutils.fs.mv(\"file:/tmp/result_v1.csv\",\"dbfs:/mnt/eason/pneumonia_classification_challenge/result_v1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "inputWidgets": {},
     "nuid": "054344d1-b07b-4a9f-97ef-717e0ecc9cd7",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
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       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Out[99]: [FileInfo(path=&#39;dbfs:/tmp/hive/&#39;, name=&#39;hive/&#39;, size=0)]</div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "arguments": {},
       "data": "<div class=\"ansiout\">Out[99]: [FileInfo(path=&#39;dbfs:/tmp/hive/&#39;, name=&#39;hive/&#39;, size=0)]</div>",
       "datasetInfos": [],
       "metadata": {},
       "removedWidgets": [],
       "type": "html"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List tmp folder.\n",
    "dbutils.fs.ls(\"/tmp\")"
   ]
  }
 ],
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   "dashboards": [],
   "language": "python",
   "notebookMetadata": {
    "experimentId": "2836853134911347",
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   "notebookName": "pneumonia_classification_challenge_v1",
   "notebookOrigID": 2836853134911347,
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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