cap9_3_alexnet_cifar10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cap9_3_alexnet_cifar10.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN+V4HxNZoyx1n9CqaH3ZHM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/cic0193_machinelearning/blob/main/cap9_3_alexnet_cifar10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9iPNgojc80V"
      },
      "source": [
        "# Capítulo 9\n",
        "\n",
        "## 9.3. Criando uma AlexNet (incompleto)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ojjoPUlSwHX"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,MaxPooling2D,Activation,Flatten,Conv2D,BatchNormalization,Dropout\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from sklearn.metrics import classification_report\n",
        "from keras.datasets import cifar10\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils.np_utils import to_categorical"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iauyYtjtRch-"
      },
      "source": [
        "![An-illustration-of-the-architecture-of-AlexNet-deep-convolutional-neural-network.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTc5bGFDVWoh"
      },
      "source": [
        "(trainX,trainY), (testX,testY) = cifar10.load_data()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gT9_0RzWTG1B"
      },
      "source": [
        "AlexNet\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0i-jXDqTJZn"
      },
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(trainX,trainY,test_size=0.3,random_state=43)\n",
        "\n",
        "y_train = to_categorical(y_train)\n",
        "y_valid = to_categorical(y_valid)\n",
        "y_test = to_categorical(testY)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPs7ZURjTHzI"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(layers.experimental.preprocessing.Resizing(224,224,interpolation=\"bilinear\",input_shape=(224,224,3)))\n",
        "model.add(Conv2D(filters=96,padding=\"same\",strides=4,kernel_size=(11,11)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=256,padding=\"same\",kernel_size=(5,5)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=384,padding=\"same\",kernel_size=(3,3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=384,padding=\"same\",kernel_size=(3,3)))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(Conv2D(filters=256,padding=\"same\",kernel_size=(3,3)))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(4096))\n",
        "model.add(Activation(\"relu\"))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(4096))\n",
        "model.add(Activation(\"relu\"))\n",
        "\n",
        "#model.add(Dropout(0.5))\n",
        "model.add(Dense(10))\n",
        "model.add(Activation(\"softmax\"))"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwytQN2LWWFV"
      },
      "source": [
        "sgd = SGD(learning_rate=0.1)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "y6UWQTxPWdq0",
        "outputId": "9a606294-3778-48ff-d1f3-6450b4c75cd1"
      },
      "source": [
        "model.fit(X_train,y_train,validation_data=(X_valid,y_valid),epochs=10)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='resizing_6_input'), name='resizing_6_input', description=\"created by layer 'resizing_6_input'\"), but it was called on an input with incompatible shape (None, 32, 32, 3).\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 224, 224, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='resizing_6_input'), name='resizing_6_input', description=\"created by layer 'resizing_6_input'\"), but it was called on an input with incompatible shape (None, 32, 32, 3).\n",
            "  71/1094 [>.............................] - ETA: 1:04:30 - loss: nan - accuracy: 0.0946"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-cad0ecc6c062>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_valid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1182\u001b[0m                 _r=1):\n\u001b[1;32m   1183\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3038\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3039\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3042\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1962\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1965\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1966\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRtTuAUpB0fK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9kevu3rBwab"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PaiB_pl4eutf"
      },
      "source": [
        "CIFAR-10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHHFrHh7C81m"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}