Machine_Learning/code_examples/tf_lite_conversion_examples.ipynb

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      "name": "tf-lite_conversion_examples.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOOOZdu8adBq",
        "colab_type": "text"
      },
      "source": [
        "# TensorFlow Lite conversion examples\n",
        "These are conversion examples according to\n",
        "[Introduction to TensorFlow Lite](https://www.udacity.com/course/intro-to-tensorflow-lite--ud190)\n",
        "video course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_RgJ_bWazIB",
        "colab_type": "text"
      },
      "source": [
        "## Example 1. Convert saved model into TF Lite model\n",
        "Convert TensorFlow saved model to TensorFlow Lite model.\n",
        "\n",
        "Save the equation `y=2x-1` model and convert it to TF Lite."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FAtlW19YEhJ",
        "colab_type": "text"
      },
      "source": [
        "### Get TF Lite file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcy6DZOtaL6m",
        "colab_type": "code",
        "outputId": "78d73eb7-811e-436a-ac37-37a9b8c667a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# As I can see video course is for TF version 2.0 (stable)\n",
        "!pip -q install tensorflow-gpu==2.0\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTV-YDnAXEgN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import tensorflow as tf\n",
        "\n",
        "import pathlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-g-LeW7Jigar",
        "colab_type": "code",
        "outputId": "76f2af94-ba1a-4192-f5ed-5be7179ae31f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Store data for x and y\n",
        "x = [-1, 0, 1, 2, 3, 4]\n",
        "y = [-3, -1, 1, 3, 5, 7]\n",
        "\n",
        "# Create a simple Keras model with 1 dense layer and 1 neuron\n",
        "# Train for 500 epochs using stocastic gradient descent (sgd) as an optimizer\n",
        "# and setting loss function to be the mean squared error.\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])])\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "model.fit(x, y, epochs=500, verbose=0)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f71bd2d43c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYI60mtNc_a1",
        "colab_type": "code",
        "outputId": "4274ce2e-eb96-4c0d-ef13-dd8647a04fc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "import os\n",
        "import pathlib\n",
        "\n",
        "# Export the SavedModel\n",
        "home_dir = '/content/sample_data'\n",
        "export_dir = os.path.join(home_dir, 'tmp')\n",
        "filename = os.path.join(home_dir, 'foo1.tflite')\n",
        "\n",
        "tf.saved_model.save(model, export_dir)\n",
        "\n",
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "# Option target_ops is for TF version < 2.0\n",
        "# converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "tf_lite_model_file = pathlib.Path(filename)\n",
        "tf_lite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/sample_data/tmp/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "792"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55ASuQcMiI1E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "32534a06-c518-48a7-f0f3-f8d73292d200"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 792 Oct 22 12:09 /content/sample_data/foo1.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsDQjgzxX12z",
        "colab_type": "text"
      },
      "source": [
        "### Initialize the TFLite interpreter to try it out"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqZ7moDFX7gZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1_MjxSjYOLZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test the TF Lite model on random input data\n",
        "input_shape = input_details[0]['shape']\n",
        "inputs, outputs1, outputs2 = [], [], []\n",
        "for _ in range(100):\n",
        "    input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n",
        "    interpreter.set_tensor(input_details[0]['index'], input_data)\n",
        "\n",
        "    interpreter.invoke()\n",
        "    tflite_results = interpreter.get_tensor(output_details[0]['index'])\n",
        "    output_data = np.array(tflite_results)\n",
        "\n",
        "    inputs.append(input_data[0][0])\n",
        "    outputs1.append(output_data[0][0])\n",
        "\n",
        "    # Test the TF model on random input data\n",
        "    tf_results = model(tf.constant(input_data))\n",
        "    output_data = np.array(tf_results)\n",
        "\n",
        "    outputs2.append(output_data[0][0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL6N75TWZdaq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "264ec7c6-f77b-436c-aa08-71ea34b1b130"
      },
      "source": [
        "# Visualize the model\n",
        "plt.plot(inputs, outputs2, 'b')\n",
        "plt.plot(inputs, outputs1, 'r')\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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qMcsQJwbrPBXuuxx79jolmNXIicE6S5XzEiqMmlkVbgzWGXyimlnDeFeSZZ+bgllDOTFY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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2fMSAi7Z2cb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Download the TFLite model file\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download(filename)\n",
        "except Exception as err:\n",
        "    print(err)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0loVvF2ru1w",
        "colab_type": "text"
      },
      "source": [
        "## Example 2. Convert TF Keras model into TF Lite model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xcET3Duq3GY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "68af1005-aeae-4ba0-a492-0d2a2bdbbff3"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pathlib\n",
        "\n",
        "# Load the MobileNet tf.keras pre-trained model\n",
        "model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))\n",
        "\n",
        "# Save model into HDF5 file\n",
        "model.save(os.path.join(home_dir, 'model.h5'))\n",
        "\n",
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "home_dir = '/content/sample_data'\n",
        "filename = os.path.join(home_dir, 'foo2.tflite')\n",
        "tflite_model_file = pathlib.Path(filename)\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13978384"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11DEHXhZ1h8x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "41eefbe5-2904-4e01-b718-d305b108b552"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 14M Oct 22 09:16 /content/sample_data/foo2.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x68b83Y_zSAD",
        "colab_type": "text"
      },
      "source": [
        "## Example 3. Convert concrete function into TF Lite model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVw9r7UctnTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "de63b5bd-e98d-48ae-d05f-321b75b49dc1"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Load the MobileNet tf.keras pre-trained model\n",
        "model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))\n",
        "\n",
        "# Get the concrete function from the Keras model\n",
        "# Create callable TF graph of the model by passing a model's forward path to the tf.function.\n",
        "run_model = tf.function(lambda x: model(x))\n",
        "\n",
        "# Save the concrete function\n",
        "concrete_func = run_model.get_concrete_function(tf.TensorSpec(model.inputs[0].shape,\n",
        "                                                              model.inputs[0].dtype))\n",
        "\n",
        "# Save the model\n",
        "converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "home_dir = '/content/sample_data'\n",
        "filename = os.path.join(home_dir, 'foo3.tflite')\n",
        "tflite_model_file = pathlib.Path(filename)\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13978380"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wQvqIqC01khq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a626f66d-039a-4d0b-a7fd-34a300fb630c"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 14M Oct 22 09:40 /content/sample_data/foo3.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gfggp5gk1wnD",
        "colab_type": "text"
      },
      "source": [
        "## Example 4. Command-line usage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MikEs0db1nWS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eb362d23-500c-49e9-d443-0ba48809566f"
      },
      "source": [
        "#!/usr/bin/env bash\n",
        "\n",
        "home_dir = '/content/sample_data'\n",
        "export_dir = os.path.join(home_dir, 'tmp')\n",
        "filename = os.path.join(home_dir, 'foo4.tflite')\n",
        "\n",
        "# Saving with the command-line from a SavedModel\n",
        "!tflite_convert --output_file=\"{filename}\" \\\n",
        "                --saved_model_dir=\"{export_dir}\""
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-10-22 09:44:39.958145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2019-10-22 09:44:39.961201: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:39.961918: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:44:39.962194: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:44:39.963605: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:44:39.965282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:44:39.965612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:44:39.967230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:44:39.968837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:44:39.975842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:44:39.975983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:39.976758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:39.977510: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:44:39.977850: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2019-10-22 09:44:39.983267: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2019-10-22 09:44:39.983534: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2a43640 executing computations on platform Host. Devices:\n",
            "2019-10-22 09:44:39.983570: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version\n",
            "2019-10-22 09:44:40.031354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.032204: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6894300 executing computations on platform CUDA. Devices:\n",
            "2019-10-22 09:44:40.032237: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2019-10-22 09:44:40.032390: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.033150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:44:40.033202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:44:40.033226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:44:40.033244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:44:40.033263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:44:40.033282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:44:40.033301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:44:40.033321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:44:40.033399: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.034200: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.034799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:44:40.034849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:44:40.036139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:44:40.036174: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:44:40.036188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:44:40.036308: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.037039: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.037708: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-10-22 09:44:40.037748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10608 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "2019-10-22 09:44:40.590340: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.591142: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2019-10-22 09:44:40.591257: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2019-10-22 09:44:40.592054: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.592722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:44:40.592789: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:44:40.592837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:44:40.592871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:44:40.592929: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:44:40.592968: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:44:40.593001: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:44:40.593031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:44:40.593121: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.593873: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.595963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:44:40.596022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:44:40.596046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:44:40.596065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:44:40.596238: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.597255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.598115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10608 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "2019-10-22 09:44:40.603513: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:716] Optimization results for grappler item: graph_to_optimize\n",
            "2019-10-22 09:44:40.603558: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   function_optimizer: Graph size after: 15 nodes (10), 23 edges (18), time = 0.82ms.\n",
            "2019-10-22 09:44:40.603582: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   function_optimizer: function_optimizer did nothing. time = 0.012ms.\n",
            "2019-10-22 09:44:40.631833: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.632529: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2019-10-22 09:44:40.632633: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2019-10-22 09:44:40.633209: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.633884: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:44:40.633969: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:44:40.634006: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:44:40.634038: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:44:40.634071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:44:40.634102: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:44:40.634131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:44:40.634160: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:44:40.634236: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.635001: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.635653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:44:40.635689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:44:40.635714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:44:40.635751: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:44:40.635858: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.636605: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:44:40.637362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10608 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "2019-10-22 09:44:40.639735: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:716] Optimization results for grappler item: graph_to_optimize\n",
            "2019-10-22 09:44:40.639769: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   constant folding: Graph size after: 13 nodes (-2), 19 edges (-4), time = 0.92ms.\n",
            "2019-10-22 09:44:40.639790: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   constant folding: Graph size after: 13 nodes (0), 19 edges (0), time = 0.226ms.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0y2iWCn14Zj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "59ea1ee9-272c-4229-a577-34e5be5e6db3"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 780 Oct 22 09:44 /content/sample_data/foo4.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pj0Nthdi23_c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a07a05f7-b852-4219-bf9f-59eb36d13238"
      },
      "source": [
        "#!/usr/bin/env bash\n",
        "\n",
        "home_dir = '/content/sample_data'\n",
        "filename = os.path.join(home_dir, 'foo5.tflite')\n",
        "model_file = os.path.join(home_dir, 'model.h5')\n",
        "\n",
        "# Saving with the command-line from a Keras model\n",
        "!tflite_convert --output_file=\"{filename}\" \\\n",
        "                --keras_model_file=\"{model_file}\""
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-10-22 09:56:15.099704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2019-10-22 09:56:15.102794: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.103518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:56:15.103752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:56:15.106159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:56:15.108187: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:56:15.108632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:56:15.112572: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:56:15.117110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:56:15.125297: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:56:15.125392: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.126132: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.126761: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:56:15.127086: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2019-10-22 09:56:15.132857: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2019-10-22 09:56:15.133147: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24c5800 executing computations on platform Host. Devices:\n",
            "2019-10-22 09:56:15.133180: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version\n",
            "2019-10-22 09:56:15.185890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.187254: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x63a61c0 executing computations on platform CUDA. Devices:\n",
            "2019-10-22 09:56:15.187298: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
            "2019-10-22 09:56:15.187499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.188258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:56:15.188349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:56:15.188375: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:56:15.188396: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:56:15.188423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:56:15.188442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:56:15.188462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:56:15.188483: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:56:15.188541: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.189267: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.189958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:56:15.190014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:56:15.191336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:56:15.191369: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:56:15.191382: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:56:15.191487: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.192204: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:15.192925: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2019-10-22 09:56:15.192974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10547 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "W1022 09:56:18.565565 140047226632064 hdf5_format.py:177] No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "2019-10-22 09:56:19.392833: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.393575: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2019-10-22 09:56:19.393703: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2019-10-22 09:56:19.394524: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.395266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:56:19.395343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:56:19.395377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:56:19.395417: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:56:19.395453: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:56:19.395498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:56:19.395530: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:56:19.395563: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:56:19.395638: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.396364: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.397034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:56:19.397082: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:56:19.397104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:56:19.397123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:56:19.397221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.398021: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:19.398685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10547 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "2019-10-22 09:56:19.408641: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:716] Optimization results for grappler item: graph_to_optimize\n",
            "2019-10-22 09:56:19.408677: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   function_optimizer: function_optimizer did nothing. time = 0.003ms.\n",
            "2019-10-22 09:56:19.408691: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   function_optimizer: function_optimizer did nothing. time = 0ms.\n",
            "2019-10-22 09:56:20.336658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.337520: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2019-10-22 09:56:20.337637: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2019-10-22 09:56:20.338217: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.338991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
            "pciBusID: 0000:00:04.0\n",
            "2019-10-22 09:56:20.339068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2019-10-22 09:56:20.339101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2019-10-22 09:56:20.339147: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2019-10-22 09:56:20.339180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2019-10-22 09:56:20.339216: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2019-10-22 09:56:20.339244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2019-10-22 09:56:20.339274: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2019-10-22 09:56:20.339348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.340070: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.340709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2019-10-22 09:56:20.340758: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2019-10-22 09:56:20.340777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2019-10-22 09:56:20.340795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2019-10-22 09:56:20.340984: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.341707: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] 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",
            "2019-10-22 09:56:20.342453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10547 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
            "2019-10-22 09:56:20.493050: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:716] Optimization results for grappler item: graph_to_optimize\n",
            "2019-10-22 09:56:20.493135: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   constant folding: Graph size after: 428 nodes (-262), 699 edges (-262), time = 85.239ms.\n",
            "2019-10-22 09:56:20.493158: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:718]   constant folding: Graph size after: 428 nodes (0), 699 edges (0), time = 34.423ms.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxL9ZJ6S4Bxa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "34dfc56b-dc88-4675-e87a-5232ca1e59d6"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 14M Oct 22 09:56 /content/sample_data/foo5.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNVmVuw6B0Re",
        "colab_type": "text"
      },
      "source": [
        "## Post-training quantizations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXtHi3cP4KWE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7b01ea81-c231-4bff-d483-5d6c43958ce7"
      },
      "source": [
        "import os\n",
        "import pathlib\n",
        "\n",
        "# Export the SavedModel\n",
        "home_dir = '/content/sample_data'\n",
        "export_dir = os.path.join(home_dir, 'tmp')\n",
        "filename = os.path.join(home_dir, 'foo_quant1.tflite')\n",
        "\n",
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "\n",
        "# Post-training quantization\n",
        "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "\n",
        "tflite_quant_model_1 = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "tf_lite_model_file = pathlib.Path(filename)\n",
        "tf_lite_model_file.write_bytes(tflite_quant_model_1)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3584424"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Du3fCAsCbbO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "716328a8-77a6-4f36-c8fe-8607058b0b2a"
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 3.5M Oct 22 11:46 /content/sample_data/foo_quant1.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bpz65OvkDuAe",
        "colab_type": "text"
      },
      "source": [
        "## Post-training integer quantization\n",
        "Quantize both the weights and the activation outputs of the model by using the DEFAULT option.\n",
        "\n",
        "In other to quantize the activations TF Lite needs to **gather calibration data** by running inferences on a small set of inputs. So as to determine the right scaling parameters needed to convert model to an integer quantized model.\n",
        "\n",
        "In other words TF Lite needs to **measure the dynamic range** of the activations and inputs with a representative dataset in other to quaqntize the activations.\n",
        "\n",
        "Post-Training Decision Tree:\n",
        "\n",
        "![TF Lite optimizations](https://raw.githubusercontent.com/foobar167/articles/master/Machine_Learning/data/2019.10.22_tf-lite_optimization.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YveIybNxCe1X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tensorflow-datasets\n",
        "\n",
        "import os\n",
        "import pathlib\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "\n",
        "# Export the SavedModel\n",
        "home_dir = '/content/sample_data'\n",
        "export_dir = os.path.join(home_dir, 'tmp')\n",
        "filename = os.path.join(home_dir, 'foo_quant2.tflite')\n",
        "model_file = os.path.join(home_dir, 'model.h5')\n",
        "\n",
        "# NOT FINISHED! You must set tfds.load(...) function.\n",
        "\n",
        "# Define the generator to generate samples from our dataset\n",
        "def generator():\n",
        "    data = tfds.load(...)\n",
        "    for _ in range(num_calibration_stps):\n",
        "        image, = data.take(1)\n",
        "        yield [image]\n",
        "\n",
        "\n",
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "\n",
        "# Set the optimization mode to DEFAULT\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "\n",
        "# Pass the representative dataset to the converter\n",
        "converter.representative_dataset = tf.lite.RepresentativeDataset(generator)\n",
        "\n",
        "# Restricting supported target ops specification to INT8\n",
        "# In other words, make it all INT8 without floating point, so the model can be\n",
        "# executed ENTIRELY on integer only hardware accelerators such as Edge TPU.\n",
        "# However, if the converter comes across the operation, that cannot be\n",
        "# quantized an error may be raised.\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "\n",
        "tflite_quant_model_2 = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "tf_lite_model_file = pathlib.Path(filename)\n",
        "tf_lite_model_file.write_bytes(tflite_quant_model_2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wj6ScVTnHUoe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check it\n",
        "!ls -hal \"{filename}\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7F7QsK4RTer",
        "colab_type": "text"
      },
      "source": [
        "## TF Lite interpreter in Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-OKhvCAJh9Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load TFLite model and allocate tensors\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_quant_model_1)\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "\n",
        "# NOT FINISHED! You must set 'input_data' parameter.\n",
        "\n",
        "# Point the data to be used for testing and run the interpreter\n",
        "interpreter.set_tensor(input_details[0]['index'], input_data)\n",
        "interpreter.invoke()\n",
        "tflite_results = interpreter.get_tensor(output_details[0]['index'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SdJeBTjTEkK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}