model_from_directory.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:32:02.834854: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-09 17:32:06.080388: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/sac0380/miniconda3/lib/python3.10/site-packages/cv2/../../lib64:\n",
      "2023-05-09 17:32:06.080454: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/sac0380/miniconda3/lib/python3.10/site-packages/cv2/../../lib64:\n",
      "2023-05-09 17:32:06.080461: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import cv2\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import shutil\n",
    "from decimal import Decimal, getcontext\n",
    "\n",
    "from tensorflow.keras.layers import Input, Dense, Flatten, BatchNormalization\n",
    "from tensorflow.keras.applications.resnet_v2 import ResNet50V2\n",
    "# from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "# from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size = [224, 224] + [3,]\n",
    "dir = 'images/'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>AccSpeciesName</th>\n",
       "      <th>mean_14</th>\n",
       "      <th>mean_3117</th>\n",
       "      <th>mean_3106</th>\n",
       "      <th>mean_4</th>\n",
       "      <th>mean_3110</th>\n",
       "      <th>mean_26</th>\n",
       "      <th>stddev_14</th>\n",
       "      <th>stddev_3117</th>\n",
       "      <th>stddev_3106</th>\n",
       "      <th>stddev_4</th>\n",
       "      <th>stddev_3110</th>\n",
       "      <th>stddev_26</th>\n",
       "      <th>PlantGrowthForm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abarema adenophora</td>\n",
       "      <td>24.422045</td>\n",
       "      <td>11.820481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.663459</td>\n",
       "      <td>56861.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.593152</td>\n",
       "      <td>1.030987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.132959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Abarema macradenia</td>\n",
       "      <td>23.394909</td>\n",
       "      <td>8.319237</td>\n",
       "      <td>31.670558</td>\n",
       "      <td>0.438332</td>\n",
       "      <td>16535.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.129421</td>\n",
       "      <td>0.679612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Abies alba</td>\n",
       "      <td>13.095739</td>\n",
       "      <td>7.846480</td>\n",
       "      <td>47.975610</td>\n",
       "      <td>0.499512</td>\n",
       "      <td>18.921429</td>\n",
       "      <td>70.544591</td>\n",
       "      <td>2.731756</td>\n",
       "      <td>2.738197</td>\n",
       "      <td>15.637276</td>\n",
       "      <td>0.076731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.875742</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Abies balsamea</td>\n",
       "      <td>14.619919</td>\n",
       "      <td>16.226192</td>\n",
       "      <td>4.047670</td>\n",
       "      <td>0.457618</td>\n",
       "      <td>34.202755</td>\n",
       "      <td>7.473453</td>\n",
       "      <td>3.278028</td>\n",
       "      <td>6.272770</td>\n",
       "      <td>6.972198</td>\n",
       "      <td>0.093733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.054439</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Abies fraseri</td>\n",
       "      <td>18.077778</td>\n",
       "      <td>5.566903</td>\n",
       "      <td>20.138400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.801836</td>\n",
       "      <td>7.202712</td>\n",
       "      <td>3.111181</td>\n",
       "      <td>0.451749</td>\n",
       "      <td>5.774298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.928773</td>\n",
       "      <td>tree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AccSpeciesName    mean_14  mean_3117  mean_3106    mean_4  \\\n",
       "3   Abarema adenophora  24.422045  11.820481        NaN  0.663459   \n",
       "11  Abarema macradenia  23.394909   8.319237  31.670558  0.438332   \n",
       "29          Abies alba  13.095739   7.846480  47.975610  0.499512   \n",
       "31      Abies balsamea  14.619919  16.226192   4.047670  0.457618   \n",
       "48       Abies fraseri  18.077778   5.566903  20.138400       NaN   \n",
       "\n",
       "       mean_3110    mean_26  stddev_14  stddev_3117  stddev_3106  stddev_4  \\\n",
       "3   56861.990000        NaN   1.593152     1.030987          NaN  0.132959   \n",
       "11  16535.000000  90.000000   0.000000     0.129421     0.679612  0.000000   \n",
       "29     18.921429  70.544591   2.731756     2.738197    15.637276  0.076731   \n",
       "31     34.202755   7.473453   3.278028     6.272770     6.972198  0.093733   \n",
       "48     22.801836   7.202712   3.111181     0.451749     5.774298       NaN   \n",
       "\n",
       "    stddev_3110  stddev_26 PlantGrowthForm  \n",
       "3           NaN        NaN            tree  \n",
       "11          0.0   0.000000            tree  \n",
       "29          NaN  26.875742            tree  \n",
       "31          NaN   3.054439            tree  \n",
       "48          NaN   0.928773            tree  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('TRY_final.txt', sep=',', encoding='latin-1')\n",
    "data = data[data['mean_3110'].notna()]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('unique_species_names.txt', 'r')\n",
    "Lines = file.readlines()\n",
    "unique_species = [i.strip() for i in Lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('leaf.pkl', 'rb') as fp:\n",
    "    y_mean = pickle.load(fp)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividing Data into Folders for Tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12782/12782 [02:32<00:00, 83.96it/s]  \n"
     ]
    }
   ],
   "source": [
    "y_dict = dict()\n",
    "y_mean, y_std = [], []\n",
    "for image in tqdm(glob.glob(\"images/*\")):\n",
    "    try:\n",
    "        val = data.loc[data['AccSpeciesName'] == image[7:re.search(r\"\\d\", image).start()], 'mean_3110'].values[0]\n",
    "        y_mean.append(val)\n",
    "        if not os.path.exists('/home/sac0380/plant_trait_identification/training/'+str(val)):\n",
    "            os.mkdir('/home/sac0380/plant_trait_identification/training/'+str(val))\n",
    "        shutil.copy(image, '/home/sac0380/plant_trait_identification/training/'+str(val))\n",
    "        \n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11270 files belonging to 1497 classes.\n",
      "Using 9016 files for training.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:32:21.624013: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:21.645975: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:21.646125: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:21.646541: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-09 17:32:21.646843: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:21.646957: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:21.647060: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:22.005282: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:22.005431: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:22.005542: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-05-09 17:32:22.005626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6535 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2060 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11270 files belonging to 1497 classes.\n",
      "Using 2254 files for validation.\n"
     ]
    }
   ],
   "source": [
    "train_dataset = tf.keras.utils.image_dataset_from_directory('/home/sac0380/plant_trait_identification/segmented_images/', labels=y_mean, batch_size=32, label_mode='int', image_size=(224, 224), subset='training', validation_split = 0.2, seed=123)\n",
    "validation_dataset = tf.keras.utils.image_dataset_from_directory('/home/sac0380/plant_trait_identification/segmented_images/', labels=y_mean, batch_size=32, label_mode='int', image_size=(224, 224), subset='validation', validation_split = 0.2, seed=123)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(736051.0, 2.49833333333333)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxx = np.amax(y_mean)\n",
    "minn = np.amin(y_mean)\n",
    "maxx, minn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(image, label):\n",
    "    global maxx\n",
    "    global minn\n",
    "    label = ((2*(label - minn)) / (maxx - minn)) - 1\n",
    "    print(label)\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"sub_1:0\", shape=(None,), dtype=float64)\n",
      "Tensor(\"sub_1:0\", shape=(None,), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "train_dataset = train_dataset.map(norm)\n",
    "validation_dataset = validation_dataset.map(norm)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = ResNet50V2(input_shape=[224, 224] + [3,],include_top=False, weights='imagenet')\n",
    "base_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape=(224, 224, 3))\n",
    "x = tf.keras.applications.resnet_v2.preprocess_input(inputs)\n",
    "x = base_model(x, training=False)\n",
    "x = tf.keras.layers.GlobalAveragePooling2D(name='avg_pool')(x)\n",
    "x = BatchNormalization()(x)\n",
    "#x = Dense(8, activation = 'relu')(x)\n",
    "output = Dense(1, activation='tanh')(x)\n",
    "model = tf.keras.Model(inputs, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " tf.math.truediv (TFOpLambda  (None, 224, 224, 3)      0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " tf.math.subtract (TFOpLambd  (None, 224, 224, 3)      0         \n",
      " a)                                                              \n",
      "                                                                 \n",
      " resnet50v2 (Functional)     (None, 7, 7, 2048)        23564800  \n",
      "                                                                 \n",
      " avg_pool (GlobalAveragePool  (None, 2048)             0         \n",
      " ing2D)                                                          \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 2048)             8192      \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 2049      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,575,041\n",
      "Trainable params: 6,145\n",
      "Non-trainable params: 23,568,896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'mean_absolute_error' , optimizer = Adam(learning_rate=0.00001) , metrics = ['mse', 'mae'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Fine-Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:32:43.495932: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8901\n",
      "2023-05-09 17:32:47.746689: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x7f36d739d9b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-05-09 17:32:47.746715: I tensorflow/compiler/xla/service/service.cc:181]   StreamExecutor device (0): NVIDIA GeForce RTX 2060 SUPER, Compute Capability 7.5\n",
      "2023-05-09 17:32:48.130763: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1/282 [..............................] - ETA: 59:57 - loss: 0.9619 - mse: 1.3950 - mae: 0.9619"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:32:49.518572: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "282/282 [==============================] - 60s 168ms/step - loss: 0.8844 - mse: 1.2385 - mae: 0.8844 - val_loss: 0.8443 - val_mse: 1.1011 - val_mae: 0.8443\n",
      "Epoch 2/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.7770 - mse: 1.1046 - mae: 0.7770 - val_loss: 0.6965 - val_mse: 0.9528 - val_mae: 0.6965\n",
      "Epoch 3/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.7042 - mse: 1.0202 - mae: 0.7042 - val_loss: 0.6223 - val_mse: 0.8674 - val_mae: 0.6223\n",
      "Epoch 4/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.6413 - mse: 0.9312 - mae: 0.6413 - val_loss: 0.5634 - val_mse: 0.7826 - val_mae: 0.5634\n",
      "Epoch 5/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.5844 - mse: 0.8332 - mae: 0.5844 - val_loss: 0.5031 - val_mse: 0.6838 - val_mae: 0.5031\n",
      "Epoch 6/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.5163 - mse: 0.7081 - mae: 0.5163 - val_loss: 0.4419 - val_mse: 0.5783 - val_mae: 0.4419\n",
      "Epoch 7/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.4479 - mse: 0.5786 - mae: 0.4479 - val_loss: 0.3776 - val_mse: 0.4649 - val_mae: 0.3776\n",
      "Epoch 8/15\n",
      "282/282 [==============================] - 20s 70ms/step - loss: 0.3718 - mse: 0.4357 - mae: 0.3718 - val_loss: 0.3119 - val_mse: 0.3525 - val_mae: 0.3119\n",
      "Epoch 9/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.2963 - mse: 0.3025 - mae: 0.2963 - val_loss: 0.2533 - val_mse: 0.2601 - val_mae: 0.2533\n",
      "Epoch 10/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.2298 - mse: 0.1987 - mae: 0.2298 - val_loss: 0.2024 - val_mse: 0.1917 - val_mae: 0.2024\n",
      "Epoch 11/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.1753 - mse: 0.1313 - mae: 0.1753 - val_loss: 0.1631 - val_mse: 0.1460 - val_mae: 0.1631\n",
      "Epoch 12/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.1372 - mse: 0.0901 - mae: 0.1372 - val_loss: 0.1320 - val_mse: 0.1142 - val_mae: 0.1320\n",
      "Epoch 13/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.1055 - mse: 0.0617 - mae: 0.1055 - val_loss: 0.1095 - val_mse: 0.0930 - val_mae: 0.1095\n",
      "Epoch 14/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.0843 - mse: 0.0442 - mae: 0.0843 - val_loss: 0.0912 - val_mse: 0.0758 - val_mae: 0.0912\n",
      "Epoch 15/15\n",
      "282/282 [==============================] - 20s 71ms/step - loss: 0.0674 - mse: 0.0313 - mae: 0.0674 - val_loss: 0.0772 - val_mse: 0.0629 - val_mae: 0.0772\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_dataset, epochs=15, batch_size=32, validation_data=validation_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 6), dpi=300)\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "ax.plot(history.history[\"mse\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "ax.plot(history.history[\"val_mse\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"MSE\")\n",
    "ax.legend()\n",
    "ax.grid(True, ls=\"--\")\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "ax.plot(history.history[\"loss\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "ax.plot(history.history[\"val_loss\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"Loss\")\n",
    "ax.legend()\n",
    "ax.grid(True, ls=\"--\")\n",
    "\n",
    "# ax = fig.add_subplot(223)\n",
    "# ax.plot(history.history[\"auc\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "# ax.plot(history.history[\"val_auc\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "# ax.set_xlabel(\"Epoch\")\n",
    "# ax.set_ylabel(\"AUC\")\n",
    "# ax.legend()\n",
    "# ax.grid(True, ls=\"--\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:38:26.790579: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.53GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:38:26.790610: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.53GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:38:26.807497: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.66GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:38:26.807532: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.66GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "281/282 [============================>.] - ETA: 0s - loss: 0.0391 - mse: 0.0111 - mae: 0.0391"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-09 17:39:25.861169: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.52GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:39:25.861236: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.52GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:39:25.874572: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.63GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-05-09 17:39:25.874612: W tensorflow/tsl/framework/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.63GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "282/282 [==============================] - 74s 198ms/step - loss: 0.0391 - mse: 0.0112 - mae: 0.0391 - val_loss: 0.0336 - val_mse: 0.0129 - val_mae: 0.0336\n",
      "Epoch 2/3\n",
      "282/282 [==============================] - 55s 193ms/step - loss: 0.0200 - mse: 0.0032 - mae: 0.0200 - val_loss: 0.0207 - val_mse: 0.0076 - val_mae: 0.0207\n",
      "Epoch 3/3\n",
      "282/282 [==============================] - 55s 194ms/step - loss: 0.0140 - mse: 0.0023 - mae: 0.0140 - val_loss: 0.0177 - val_mse: 0.0097 - val_mae: 0.0177\n"
     ]
    }
   ],
   "source": [
    "base_model.trainable = True\n",
    "model.compile(loss = 'mean_absolute_error' , optimizer = Adam(learning_rate=0.000007) , metrics = ['mse', 'mae'])\n",
    "history = model.fit(train_dataset, epochs=3, batch_size=32, validation_data=validation_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 6), dpi=300)\n",
    "\n",
    "ax = fig.add_subplot(221)\n",
    "ax.plot(history.history[\"mse\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "ax.plot(history.history[\"val_mse\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"MSE\")\n",
    "ax.legend()\n",
    "ax.grid(True, ls=\"--\")\n",
    "\n",
    "ax = fig.add_subplot(222)\n",
    "ax.plot(history.history[\"loss\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "ax.plot(history.history[\"val_loss\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"Loss\")\n",
    "ax.legend()\n",
    "ax.grid(True, ls=\"--\")\n",
    "\n",
    "# ax = fig.add_subplot(223)\n",
    "# ax.plot(history.history[\"auc\"], marker=\"^\", label = \"Train\", color=\"firebrick\")\n",
    "# ax.plot(history.history[\"val_auc\"], marker=\"o\", label = \"Test\", color=\"royalblue\")\n",
    "# ax.set_xlabel(\"Epoch\")\n",
    "# ax.set_ylabel(\"AUC\")\n",
    "# ax.legend()\n",
    "# ax.grid(True, ls=\"--\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"leaf_area_segmented.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('leaf.pkl', 'wb') as fp:\n",
    "    pickle.dump(y_mean, fp)"
   ]
  }
 ],
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