keras_example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fermi_lab.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5b4Fq3l6wFO",
        "outputId": "4d0b2616-8d75-458c-fba3-f435d324a5e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)           [(None, 48, 48, 1)]  0           []                               \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 44, 44, 48)   1248        ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)            (None, 44, 44, 48)   0           ['conv2d_2[0][0]']               \n",
            "                                                                                                  \n",
            " flatten_2 (Flatten)            (None, 92928)        0           ['dropout_4[0][0]']              \n",
            "                                                                                                  \n",
            " dense_8 (Dense)                (None, 128)          11894912    ['flatten_2[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)            (None, 128)          0           ['dense_8[0][0]']                \n",
            "                                                                                                  \n",
            " dense_9 (Dense)                (None, 32)           4128        ['dropout_5[0][0]']              \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 3)            99          ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            " dense_11 (Dense)               (None, 1)            33          ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 11,900,420\n",
            "Trainable params: 11,900,420\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from keras import layers, models\n",
        "from tensorflow.keras.layers import Input, Dense, Flatten, Concatenate, Conv2D, Dropout\n",
        "\n",
        "input_layer = Input((48,48,1))\n",
        "x = Conv2D(48, 5, activation='relu')(input_layer) \n",
        "x = Dropout(0.1)(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.1)(x)\n",
        "x = Dense(32, activation='relu')(x)\n",
        "x1 = Dense(3, activation='softmax')(x)\n",
        "x2 = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "final_output = [x1, x2]\n",
        "model = tf.keras.Model(input_layer, final_output)\n",
        "model.compile(optimizer='adam', metrics=['accuracy'], loss='categorical_crossentropy')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converion to int tflite using fake quantization\n",
        "def representative_data_gen():\n",
        "  for i in range(100):\n",
        "      data = np.random.rand(1, 48, 48, 1)\n",
        "      yield [data.astype(np.float32)]\n",
        "\n",
        "model.input.set_shape((1,) + model.input.shape[1:])\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "converter.representative_dataset = representative_data_gen\n",
        "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
        "converter.target_spec.supported_types = [tf.int8]\n",
        "converter.inference_input_type = tf.uint8\n",
        "converter.inference_output_type = tf.uint8\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with open('model.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wh7q-zyH7fOB",
        "outputId": "e2e1db6c-f736-4f1a-c0e7-6433a814f4c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpv94y4u9j/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpv94y4u9j/assets\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/convert.py:746: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
            "  warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n",
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -\n",
        "\n",
        "! echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list\n",
        "\n",
        "! sudo apt-get update\n",
        "\n",
        "! sudo apt-get install edgetpu-compiler\t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THslRTYo7p7C",
        "outputId": "aa3bd888-1ae5-4415-96ba-c0397975b7ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  2537  100  2537    0     0  33826      0 --:--:-- --:--:-- --:--:-- 33826\n",
            "OK\n",
            "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\n",
            "Get:1 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease [6,722 B]\n",
            "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:3 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Get:4 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Ign:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Ign:7 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 Packages\n",
            "Hit:8 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:9 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Get:10 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Get:7 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 Packages [2,317 B]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:12 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Get:14 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [22.8 kB]\n",
            "Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,503 kB]\n",
            "Hit:16 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:17 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,765 kB]\n",
            "Get:18 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:19 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [932 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,277 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,199 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [966 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [29.8 kB]\n",
            "Get:24 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [44.3 kB]\n",
            "Fetched 12.0 MB in 2s (5,556 kB/s)\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  edgetpu-compiler\n",
            "0 upgraded, 1 newly installed, 0 to remove and 47 not upgraded.\n",
            "Need to get 7,913 kB of archives.\n",
            "After this operation, 31.2 MB of additional disk space will be used.\n",
            "Get:1 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 edgetpu-compiler amd64 16.0 [7,913 kB]\n",
            "Fetched 7,913 kB in 1s (8,500 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package edgetpu-compiler.\n",
            "(Reading database ... 155629 files and directories currently installed.)\n",
            "Preparing to unpack .../edgetpu-compiler_16.0_amd64.deb ...\n",
            "Unpacking edgetpu-compiler (16.0) ...\n",
            "Setting up edgetpu-compiler (16.0) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!edgetpu_compiler -s \"/content/model.tflite\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuI_HMtK71OV",
        "outputId": "d4d14050-ce1b-4a06-f96f-c9341cdcdc8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Edge TPU Compiler version 16.0.384591198\n",
            "Started a compilation timeout timer of 180 seconds.\n",
            "\n",
            "Model compiled successfully in 2457 ms.\n",
            "\n",
            "Input model: /content/model.tflite\n",
            "Input size: 11.35MiB\n",
            "Output model: model_edgetpu.tflite\n",
            "Output size: 11.42MiB\n",
            "On-chip memory used for caching model parameters: 19.75KiB\n",
            "On-chip memory remaining for caching model parameters: 7.85MiB\n",
            "Off-chip memory used for streaming uncached model parameters: 11.34MiB\n",
            "Number of Edge TPU subgraphs: 1\n",
            "Total number of operations: 11\n",
            "Operation log: model_edgetpu.log\n",
            "\n",
            "Operator                       Count      Status\n",
            "\n",
            "LOGISTIC                       1          Mapped to Edge TPU\n",
            "RESHAPE                        1          Mapped to Edge TPU\n",
            "SOFTMAX                        1          Mapped to Edge TPU\n",
            "QUANTIZE                       3          Mapped to Edge TPU\n",
            "CONV_2D                        1          Mapped to Edge TPU\n",
            "FULLY_CONNECTED                4          Mapped to Edge TPU\n",
            "Compilation child process completed within timeout period.\n",
            "Compilation succeeded! \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "L8FE72_379Hc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}