10_save_restore_net.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "10_save_restore_net.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EpHM0dOSZKp",
        "colab_type": "text"
      },
      "source": [
        "# Save and Restore"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhEA4rpZ1DDg",
        "colab_type": "code",
        "outputId": "9add879a-ca71-4879-e3fd-3e656e87da6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3SMvaI3SkYo",
        "colab_type": "code",
        "outputId": "c34b06b7-d28b-4248-fdf1-a2dec2767c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%pylab inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWxPyG1rSWS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftoodtdoS2hr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5ea5d8b7-e4dc-411b-b974-821015eb0e5f"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 5,
      "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": "LlasfmQyVgHV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = (x_train / 255).reshape((60000, 28, 28, 1))\n",
        "x_test = (x_test / 255).reshape((10000, 28, 28, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dXNTKCxTgoX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu',\n",
        "                     input_shape=x_train.shape[1:]),\n",
        "  tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "  tf.keras.layers.Dropout(0.25),\n",
        "\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(10, activation='softmax'),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0VIhv3HVG1r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXwyS0oRVOas",
        "colab_type": "code",
        "outputId": "1396bb2b-ce6f-4fd9-c77a-c1ec90f2633c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    epochs=10,\n",
        "                    batch_size=128,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 12s 203us/sample - loss: 0.2339 - accuracy: 0.9298 - val_loss: 0.0547 - val_accuracy: 0.9822\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0829 - accuracy: 0.9753 - val_loss: 0.0373 - val_accuracy: 0.9873\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0633 - accuracy: 0.9807 - val_loss: 0.0318 - val_accuracy: 0.9894\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0513 - accuracy: 0.9841 - val_loss: 0.0313 - val_accuracy: 0.9892\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0438 - accuracy: 0.9862 - val_loss: 0.0265 - val_accuracy: 0.9920\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0388 - accuracy: 0.9880 - val_loss: 0.0255 - val_accuracy: 0.9923\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 5s 76us/sample - loss: 0.0331 - accuracy: 0.9889 - val_loss: 0.0280 - val_accuracy: 0.9911\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0288 - accuracy: 0.9907 - val_loss: 0.0272 - val_accuracy: 0.9916\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 5s 76us/sample - loss: 0.0269 - accuracy: 0.9909 - val_loss: 0.0280 - val_accuracy: 0.9922\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0265 - accuracy: 0.9913 - val_loss: 0.0268 - val_accuracy: 0.9929\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80P8tYkmSWTE",
        "colab_type": "code",
        "outputId": "445fa78c-3a5d-4534-a14b-fe91ca51831e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plot(history.history['accuracy'], label='accuracy')\n",
        "plot(history.history['val_accuracy'], label='val accuracy')\n",
        "legend()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa63a7b1080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hy6CrLZil6dqfwMxLdZHTEDS2HuK5DcGR+5sftuIO51YU8pXl57Fi/iTmTSnRMLwio1D6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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_chFaFM1ZkB",
        "colab_type": "code",
        "outputId": "bfc9a3e9-8d21-496d-fb45-2556d3f0448a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plot(history.history['loss'], label='loss')\n",
        "plot(history.history['val_loss'], label='val loss')\n",
        "legend()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa63a27bd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ihg9ndQAvHBsG6tkOqdFj7UrneZOzhYu8DsFCEApnXmeeQ6Gs5XBWu8iktpnH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\nHOsvKYIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tmf_V9y4oq4M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights('model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysLTDoyAph62",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu',\n",
        "                     input_shape=x_train.shape[1:]),\n",
        "  tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "  tf.keras.layers.Dropout(0.25),\n",
        "\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(10, activation='softmax'),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pKviJ0ypQ6B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights('model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}