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Keras_model_Continue_training.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Keras_model_Continue_training.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Shamim-38/b415ef2d2af22fe7fb4824a92a3fcb1c/keras_model_continue_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "sOs9J5tQy1vJ", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 768 | |
| }, | |
| "outputId": "e3bd22a6-19a3-41fe-9e3f-2c504648e471" | |
| }, | |
| "source": [ | |
| "!pip install tf-nightly" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting tf-nightly\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/cf/29/eaa43f27d2045ed4813c72e6f52d73b58ed603c5f1817c599993373bdf67/tf_nightly-2.2.0.dev20200405-cp36-cp36m-manylinux2010_x86_64.whl (517.1MB)\n", | |
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| "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/59/0b/446c007433c44a2aab87b34fc272afe8574a1c47cee35dbc19f6394dada8/tb_nightly-2.3.0a20200405-py3-none-any.whl (2.9MB)\n", | |
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| "Collecting tf-estimator-nightly\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/ca/94/373b909bdc1cd30015118d0c9da1b2be368c391691764181deb653b3d0ce/tf_estimator_nightly-2.3.0.dev2020040501-py2.py3-none-any.whl (455kB)\n", | |
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| "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.24.3)\n", | |
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| "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.0)\n", | |
| "Installing collected packages: tb-nightly, tf-estimator-nightly, tf-nightly\n", | |
| "Successfully installed tb-nightly-2.3.0a20200405 tf-estimator-nightly-2.3.0.dev2020040501 tf-nightly-2.2.0.dev20200405\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "9pCcQGkmy5xm", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "634ac278-896d-4eb3-b747-d6a7059d3d12" | |
| }, | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "tf.__version__" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'2.2.0-dev20200405'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "MPNRCcYSy931", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 51 | |
| }, | |
| "outputId": "43527cdf-815a-46c2-fe1f-eb74e5514349" | |
| }, | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "from tensorflow import keras\n", | |
| "mnist = tf.keras.datasets.mnist\n", | |
| "\n", | |
| "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n", | |
| "x_train, x_test = x_train / 255.0, x_test / 255.0\n", | |
| " \n", | |
| "def create_model():\n", | |
| " model = tf.keras.models.Sequential([\n", | |
| " tf.keras.layers.Flatten(input_shape=(28, 28)),\n", | |
| " tf.keras.layers.Dense(512, activation=tf.nn.relu), \n", | |
| " tf.keras.layers.Dropout(0.2),\n", | |
| " tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n", | |
| " ])\n", | |
| "\n", | |
| " model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n", | |
| " return model" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", | |
| "11493376/11490434 [==============================] - 0s 0us/step\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zKg4WTrQzF0N", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 374 | |
| }, | |
| "outputId": "d89cd341-7364-411e-a0b1-373fc182d7f3" | |
| }, | |
| "source": [ | |
| "# Create a basic model instance\n", | |
| "model=create_model()\n", | |
| "model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2178 - accuracy: 0.9347 - val_loss: 0.1012 - val_accuracy: 0.9694\n", | |
| "Epoch 2/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0965 - accuracy: 0.9707 - val_loss: 0.0879 - val_accuracy: 0.9718\n", | |
| "Epoch 3/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0695 - accuracy: 0.9782 - val_loss: 0.0744 - val_accuracy: 0.9774\n", | |
| "Epoch 4/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0529 - accuracy: 0.9832 - val_loss: 0.0719 - val_accuracy: 0.9782\n", | |
| "Epoch 5/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0430 - accuracy: 0.9857 - val_loss: 0.0693 - val_accuracy: 0.9796\n", | |
| "Epoch 6/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0367 - accuracy: 0.9879 - val_loss: 0.0693 - val_accuracy: 0.9799\n", | |
| "Epoch 7/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0331 - accuracy: 0.9893 - val_loss: 0.0724 - val_accuracy: 0.9793\n", | |
| "Epoch 8/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0284 - accuracy: 0.9904 - val_loss: 0.0668 - val_accuracy: 0.9818\n", | |
| "Epoch 9/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0232 - accuracy: 0.9920 - val_loss: 0.0730 - val_accuracy: 0.9817\n", | |
| "Epoch 10/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0235 - accuracy: 0.9919 - val_loss: 0.0741 - val_accuracy: 0.9819\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<tensorflow.python.keras.callbacks.History at 0x7fb0b0ebd6a0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "wwJsUwzrzZLa", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "df957bb8-4f8f-42dd-8087-fb017fbacb7c" | |
| }, | |
| "source": [ | |
| "# saving the model in tensorflow format\n", | |
| "model.save('./MyModel_tf',save_format='tf')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "INFO:tensorflow:Assets written to: ./MyModel_tf/assets\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "unlMA9gWzjpV" | |
| }, | |
| "source": [ | |
| "# loading the saved model\n", | |
| "loaded_model = tf.keras.models.load_model('./MyModel_tf')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "clibGjx3zscb", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 374 | |
| }, | |
| "outputId": "2cba31a1-a0d2-47f5-b1f4-5faaf064b2f2" | |
| }, | |
| "source": [ | |
| "# retraining the model\n", | |
| "loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0220 - accuracy: 0.9922 - val_loss: 0.0703 - val_accuracy: 0.9845\n", | |
| "Epoch 2/10\n", | |
| "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0196 - accuracy: 0.9935 - val_loss: 0.0815 - val_accuracy: 0.9811\n", | |
| "Epoch 3/10\n", | |
| "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0177 - accuracy: 0.9944 - val_loss: 0.0790 - val_accuracy: 0.9833\n", | |
| "Epoch 4/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0171 - accuracy: 0.9943 - val_loss: 0.0830 - val_accuracy: 0.9825\n", | |
| "Epoch 5/10\n", | |
| "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0168 - accuracy: 0.9945 - val_loss: 0.0809 - val_accuracy: 0.9825\n", | |
| "Epoch 6/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0157 - accuracy: 0.9946 - val_loss: 0.0949 - val_accuracy: 0.9806\n", | |
| "Epoch 7/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0171 - accuracy: 0.9944 - val_loss: 0.0889 - val_accuracy: 0.9829\n", | |
| "Epoch 8/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0149 - accuracy: 0.9951 - val_loss: 0.0889 - val_accuracy: 0.9830\n", | |
| "Epoch 9/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0142 - accuracy: 0.9954 - val_loss: 0.0912 - val_accuracy: 0.9825\n", | |
| "Epoch 10/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0128 - accuracy: 0.9959 - val_loss: 0.1032 - val_accuracy: 0.9827\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<tensorflow.python.keras.callbacks.History at 0x7fb0ad5e1588>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Ij3vy8rI0XDf" | |
| }, | |
| "source": [ | |
| "model.save('./MyModel_h5.h5', save_format='h5')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gSYpd8--pwWc" | |
| }, | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Bo3hXXTNnhx0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 34 | |
| }, | |
| "outputId": "8cce03e2-3d89-456c-8b74-dead88c6ea53" | |
| }, | |
| "source": [ | |
| "# loading the saved model\n", | |
| "loaded_model_h5 = tf.keras.models.load_model('./MyModel_h5.h5')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "XosFU_mVnr_R", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 374 | |
| }, | |
| "outputId": "96326e58-6821-4037-a0ac-8898597fb27b" | |
| }, | |
| "source": [ | |
| "# retraining the model\n", | |
| "loaded_model_h5.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0234 - accuracy: 0.9922 - val_loss: 0.0841 - val_accuracy: 0.9793\n", | |
| "Epoch 2/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0187 - accuracy: 0.9936 - val_loss: 0.0773 - val_accuracy: 0.9820\n", | |
| "Epoch 3/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0180 - accuracy: 0.9941 - val_loss: 0.0823 - val_accuracy: 0.9817\n", | |
| "Epoch 4/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0192 - accuracy: 0.9935 - val_loss: 0.0826 - val_accuracy: 0.9822\n", | |
| "Epoch 5/10\n", | |
| "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0165 - accuracy: 0.9947 - val_loss: 0.1009 - val_accuracy: 0.9814\n", | |
| "Epoch 6/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0857 - val_accuracy: 0.9827\n", | |
| "Epoch 7/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0143 - accuracy: 0.9953 - val_loss: 0.0830 - val_accuracy: 0.9832\n", | |
| "Epoch 8/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0124 - accuracy: 0.9959 - val_loss: 0.0964 - val_accuracy: 0.9827\n", | |
| "Epoch 9/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.0922 - val_accuracy: 0.9832\n", | |
| "Epoch 10/10\n", | |
| "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0142 - accuracy: 0.9954 - val_loss: 0.0965 - val_accuracy: 0.9831\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<tensorflow.python.keras.callbacks.History at 0x7fb0acb321d0>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vOvjziczqnm2" | |
| }, | |
| "source": [], | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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