Linear_Classifier.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "Linear Classifier.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HurleyJames/GoogleColabExercise/blob/master/Linear_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKtVOsBpUXSR",
        "colab_type": "text"
      },
      "source": [
        "Train a multiclass linear classifier on the CIFAR10 dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ma9yr-ZnUXSU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from __future__ import print_function\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from tensorflow.keras.datasets import mnist, cifar10\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfFo3ZTzUXSY",
        "colab_type": "code",
        "outputId": "a3610b85-ca09-4f3d-83fe-6cb2e15cc722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 10\n",
        "\n",
        "# The data, shuffled and split between train and test sets:\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "\n",
        "# Convert class vectors to binary class matrices.\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Flatten())\n",
        "#model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "# initiate RMSprop optimizer\n",
        "opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)\n",
        "\n",
        "# Let's train the model using RMSprop\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=opt,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train = x_train/255\n",
        "x_test = x_test/255\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "              batch_size=batch_size,\n",
        "              epochs=epochs,\n",
        "              validation_data=(x_test, y_test),\n",
        "              shuffle=True)\n",
        "\n",
        "# Score trained model.\n",
        "scores = model.evaluate(x_test, y_test, verbose=1)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])\n",
        "\n",
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28)\n",
            "60000 train samples\n",
            "10000 test samples\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 3s 51us/sample - loss: 1.0808 - acc: 0.7385 - val_loss: 0.5738 - val_acc: 0.8684\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 3s 49us/sample - loss: 0.4999 - acc: 0.8742 - val_loss: 0.4199 - val_acc: 0.8924\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 3s 49us/sample - loss: 0.4096 - acc: 0.8900 - val_loss: 0.3696 - val_acc: 0.9028\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3717 - acc: 0.8981 - val_loss: 0.3448 - val_acc: 0.9077\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 3s 49us/sample - loss: 0.3503 - acc: 0.9030 - val_loss: 0.3288 - val_acc: 0.9103\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3362 - acc: 0.9063 - val_loss: 0.3180 - val_acc: 0.9137\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3263 - acc: 0.9085 - val_loss: 0.3100 - val_acc: 0.9148\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 3s 48us/sample - loss: 0.3188 - acc: 0.9104 - val_loss: 0.3050 - val_acc: 0.9159\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 3s 49us/sample - loss: 0.3129 - acc: 0.9121 - val_loss: 0.3011 - val_acc: 0.9158\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 3s 49us/sample - loss: 0.3080 - acc: 0.9139 - val_loss: 0.2967 - val_acc: 0.9181\n",
            "10000/10000 [==============================] - 0s 27us/sample - loss: 0.2967 - acc: 0.9181\n",
            "Test loss: 0.2966593714952469\n",
            "Test accuracy: 0.9181\n",
            "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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8/RWfA7aGOtdd6cBLbmgkA68AD0TrM3Tb/j48fSU0bIPz74Q53xy0u/SVVQaZ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\nCxNjnJeDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hj+feP59HQ0s7n/ju7/n3J96kuU29AxE5kIJgpBRNgE+H5SfuuQie+3fYs7H/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\nFypx3B+yhAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfn6dSh9UXSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJT70e0dUXSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# history()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jELA4q-UXSi",
        "colab_type": "code",
        "outputId": "3af9d364-009e-453c-e33e-06474774f011",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "history.history"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'acc': [0.7384833,\n",
              "  0.87418336,\n",
              "  0.89005,\n",
              "  0.89811665,\n",
              "  0.9029667,\n",
              "  0.9062667,\n",
              "  0.90853333,\n",
              "  0.91041666,\n",
              "  0.91211665,\n",
              "  0.91393334],\n",
              " 'loss': [1.0808088469187418,\n",
              "  0.49993621060848237,\n",
              "  0.40956850295861563,\n",
              "  0.3717004458665848,\n",
              "  0.35030398622751235,\n",
              "  0.33624429000616074,\n",
              "  0.32633275642196335,\n",
              "  0.3187760640263557,\n",
              "  0.31287282100319863,\n",
              "  0.3080314559320609],\n",
              " 'val_acc': [0.8684,\n",
              "  0.8924,\n",
              "  0.9028,\n",
              "  0.9077,\n",
              "  0.9103,\n",
              "  0.9137,\n",
              "  0.9148,\n",
              "  0.9159,\n",
              "  0.9158,\n",
              "  0.9181],\n",
              " 'val_loss': [0.573790195274353,\n",
              "  0.4199317941188812,\n",
              "  0.369601609659195,\n",
              "  0.34484514322280885,\n",
              "  0.32882071619033815,\n",
              "  0.31802962260246276,\n",
              "  0.30999509747028353,\n",
              "  0.30499601831436157,\n",
              "  0.3011487965941429,\n",
              "  0.2966593717932701]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnHp0TGEUXSk",
        "colab_type": "code",
        "outputId": "1ca77cc0-ec33-4ae8-f6e5-84ae5c0659a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 6s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3],\n",
              "       [8],\n",
              "       [8],\n",
              "       ...,\n",
              "       [5],\n",
              "       [1],\n",
              "       [7]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bp4paSB_UXSo",
        "colab_type": "code",
        "outputId": "84e01c2c-6e0b-4a6c-a6a8-9dd8f5320bc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqoiWbL2UXSr",
        "colab_type": "code",
        "outputId": "72d02446-e6f9-4438-d6f4-fd1e9f23f935",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "4HfUtcBKUXSu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}