autokerasCNN.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "autokerasCNN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc9Tm_kpikEX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ebbd6a5-35b6-497b-c4c1-b7a2f4ca2df1"
      },
      "source": [
        "!pip install autokeras"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting autokeras\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/09/12/cf698586ccc8245f08d1843dcafb65b064a2e9e2923b889dc58e1019f099/autokeras-1.0.12-py3-none-any.whl (164kB)\n",
            "\u001b[K     |████████████████████████████████| 174kB 8.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from autokeras) (1.1.5)\n",
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            "Collecting keras-tuner>=1.0.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/20/ec/1ef246787174b1e2bb591c95f29d3c1310070cad877824f907faba3dade9/keras-tuner-1.0.2.tar.gz (62kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 7.7MB/s \n",
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            "Collecting terminaltables\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
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            "Building wheels for collected packages: keras-tuner, terminaltables\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2-cp36-none-any.whl size=78939 sha256=db9a60282d06c0aef733bd06396efd67283026e5bc3eab2a79acdebb1df4e339\n",
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            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp36-none-any.whl size=15358 sha256=d4b4b03f0253a33aa43257a6b5be01e5b8aa7063d9cb8036744b0fb02d6bdd64\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built keras-tuner terminaltables\n",
            "Installing collected packages: terminaltables, colorama, keras-tuner, autokeras\n",
            "Successfully installed autokeras-1.0.12 colorama-0.4.4 keras-tuner-1.0.2 terminaltables-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtHQDegPiQj-"
      },
      "source": [
        "import numpy as np\r\n",
        "import tensorflow as tf\r\n",
        "from tensorflow.keras.datasets import mnist\r\n",
        "from tensorflow.python.keras.utils.data_utils import Sequence\r\n",
        "import autokeras as ak"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15iIj51_jCxb",
        "outputId": "c64d5669-3543-44cd-c6a4-0ed79cca78fb"
      },
      "source": [
        "physical_devices = tf.config.experimental.list_physical_devices('GPU')\r\n",
        "print(\"GPUs Available: \", len(physical_devices))\r\n",
        "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPUs Available:  1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eIfqmbQipeT",
        "outputId": "373d7556-8d19-49b7-e995-37392503ea1b"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n",
        "print(x_train.shape)  # (60000, 28, 28)\r\n",
        "print(y_train.shape)  # (60000,)\r\n",
        "print(y_train[:3])  # array([7, 2, 1], dtype=uint8)"
      ],
      "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",
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "[5 0 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JjujOup54LB",
        "outputId": "8ede31cf-44e3-4efe-bde2-9094d8db771d"
      },
      "source": [
        "y_train[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t5chOuEip1z",
        "outputId": "6c816427-60c7-46f3-f233-be9b127fc90a"
      },
      "source": [
        "# Initialize the image classifier.\r\n",
        "clf = ak.ImageClassifier(\r\n",
        "    overwrite=True,\r\n",
        "    max_trials=1)\r\n",
        "# Feed the image classifier with training data.\r\n",
        "history = clf.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trial 1 Complete [00h 02m 09s]\n",
            "val_loss: 0.040986694395542145\n",
            "\n",
            "Best val_loss So Far: 0.040986694395542145\n",
            "Total elapsed time: 00h 02m 09s\n",
            "INFO:tensorflow:Oracle triggered exit\n",
            "Epoch 1/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3040 - accuracy: 0.9061\n",
            "Epoch 2/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0791 - accuracy: 0.9759\n",
            "Epoch 3/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0627 - accuracy: 0.9801\n",
            "Epoch 4/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0527 - accuracy: 0.9832\n",
            "Epoch 5/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0453 - accuracy: 0.9854\n",
            "Epoch 6/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0406 - accuracy: 0.9871\n",
            "Epoch 7/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0377 - accuracy: 0.9880\n",
            "Epoch 8/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0331 - accuracy: 0.9895\n",
            "Epoch 9/9\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0339 - accuracy: 0.9896\n",
            "INFO:tensorflow:Assets written to: ./image_classifier/best_model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hN6NP3iTkWYM",
        "outputId": "959e08d7-68e8-416c-ccf0-6e0075f85f9f"
      },
      "source": [
        "model = clf.export_model()\r\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 28, 28)]          0         \n",
            "_________________________________________________________________\n",
            "cast_to_float32 (CastToFloat (None, 28, 28)            0         \n",
            "_________________________________________________________________\n",
            "expand_last_dim (ExpandLastD (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "normalization (Normalization (None, 28, 28, 1)         3         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 10)                92170     \n",
            "_________________________________________________________________\n",
            "classification_head_1 (Softm (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 110,989\n",
            "Trainable params: 110,986\n",
            "Non-trainable params: 3\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOzSRUwRlpuO"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "def plotImages(images_arr):\r\n",
        "    fig, axes = plt.subplots(1, 10, figsize=(20,20))\r\n",
        "    axes = axes.flatten()\r\n",
        "    for img, ax in zip( images_arr, axes):\r\n",
        "        ax.imshow(img)\r\n",
        "        ax.axis('off')\r\n",
        "    plt.tight_layout()\r\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "5So15gv8lqmk",
        "outputId": "2fc7a190-7500-44ee-ca08-5cdc357bacea"
      },
      "source": [
        "plotImages(x_test[:6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrtuNjlYlHbF",
        "outputId": "fe87c98d-a83b-4615-df9b-aa130eaae927"
      },
      "source": [
        "# Predict with the best model.\r\n",
        "predicted_y = clf.predict(x_test[:6])\r\n",
        "print(predicted_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['7']\n",
            " ['2']\n",
            " ['1']\n",
            " ['0']\n",
            " ['4']\n",
            " ['1']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mq2VRTpIlbuc",
        "outputId": "5b786e48-4bad-4d8c-d370-714e36b737ef"
      },
      "source": [
        "# Evaluate the best model with testing data.\r\n",
        "print(clf.evaluate(x_test, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0377 - accuracy: 0.9889\n",
            "[0.037700191140174866, 0.9889000058174133]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}