EE 298 HW CNN_in_CIFAR10 (74%).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN in CIFAR10(74%).ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTG0ZujES8r-",
        "colab_type": "code",
        "outputId": "65f30c42-09bb-47cc-c496-d2c5ac6fdb77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, Dropout\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten\n",
        "from keras.utils import to_categorical, plot_model\n",
        "from keras.datasets import cifar10\n",
        "\n",
        "# load cifar10 dataset\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n",
        "# compute the number of labels\n",
        "num_labels = len(np.unique(y_train))\n",
        "\n",
        "# convert to one-hot vector\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "\n",
        "# input image dimensions\n",
        "image_size = x_train.shape[1]\n",
        "# resize and normalize\n",
        "x_train = np.reshape(x_train,[-1, image_size, image_size, 3])\n",
        "x_test = np.reshape(x_test,[-1, image_size, image_size, 3])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "# network parameters\n",
        "# image is processed as is (square RGB)\n",
        "input_shape = (image_size, image_size, 3)\n",
        "batch_size = 128\n",
        "kernel_size = 3\n",
        "pool_size = 2\n",
        "filters = 64\n",
        "dropout = 0.2\n",
        "\n",
        "# model is a stack of CNN-ReLU-MaxPooling\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu',\n",
        "                 input_shape=input_shape))\n",
        "model.add(MaxPooling2D(pool_size))\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size))\n",
        "model.add(Conv2D(filters=filters,\n",
        "                 kernel_size=kernel_size,\n",
        "                 activation='relu'))\n",
        "model.add(Flatten())\n",
        "# dropout added as regularizer\n",
        "model.add(Dropout(dropout))\n",
        "# output layer is 10-dim one-hot vector\n",
        "model.add(Dense(num_labels))\n",
        "model.add(Activation('softmax'))\n",
        "model.summary()\n",
        "plot_model(model, to_file='cnn-mnist.png', show_shapes=True)\n",
        "\n",
        "# loss function for one-hot vector\n",
        "# use of adam optimizer\n",
        "# accuracy is good metric for classification tasks\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "# train the network\n",
        "model.fit(x_train, y_train, epochs=20, batch_size=batch_size)\n",
        "\n",
        "loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)\n",
        "print(\"\\nTest accuracy: %.1f%%\" % (100.0 * acc))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 30, 30, 64)        1792      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 4, 4, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                10250     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 85,898\n",
            "Trainable params: 85,898\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Epoch 1/20\n",
            "50000/50000 [==============================] - 13s 268us/step - loss: 1.6690 - acc: 0.3894\n",
            "Epoch 2/20\n",
            "50000/50000 [==============================] - 7s 130us/step - loss: 1.3418 - acc: 0.5213\n",
            "Epoch 3/20\n",
            "50000/50000 [==============================] - 6s 129us/step - loss: 1.2000 - acc: 0.5774\n",
            "Epoch 4/20\n",
            "50000/50000 [==============================] - 7s 130us/step - loss: 1.0967 - acc: 0.6155\n",
            "Epoch 5/20\n",
            "50000/50000 [==============================] - 7s 134us/step - loss: 1.0256 - acc: 0.6398\n",
            "Epoch 6/20\n",
            "50000/50000 [==============================] - 7s 131us/step - loss: 0.9750 - acc: 0.6613\n",
            "Epoch 7/20\n",
            "50000/50000 [==============================] - 7s 134us/step - loss: 0.9287 - acc: 0.6771\n",
            "Epoch 8/20\n",
            "50000/50000 [==============================] - 7s 134us/step - loss: 0.8949 - acc: 0.6876\n",
            "Epoch 9/20\n",
            "50000/50000 [==============================] - 7s 133us/step - loss: 0.8682 - acc: 0.6979\n",
            "Epoch 10/20\n",
            "50000/50000 [==============================] - 7s 132us/step - loss: 0.8372 - acc: 0.7075\n",
            "Epoch 11/20\n",
            "50000/50000 [==============================] - 7s 133us/step - loss: 0.8102 - acc: 0.7189\n",
            "Epoch 12/20\n",
            "50000/50000 [==============================] - 7s 134us/step - loss: 0.7895 - acc: 0.7243\n",
            "Epoch 13/20\n",
            "50000/50000 [==============================] - 7s 131us/step - loss: 0.7672 - acc: 0.7325\n",
            "Epoch 14/20\n",
            "50000/50000 [==============================] - 7s 131us/step - loss: 0.7466 - acc: 0.7382\n",
            "Epoch 15/20\n",
            "50000/50000 [==============================] - 7s 132us/step - loss: 0.7298 - acc: 0.7464\n",
            "Epoch 16/20\n",
            "50000/50000 [==============================] - 7s 132us/step - loss: 0.7091 - acc: 0.7526\n",
            "Epoch 17/20\n",
            "50000/50000 [==============================] - 7s 130us/step - loss: 0.6920 - acc: 0.7574\n",
            "Epoch 18/20\n",
            "50000/50000 [==============================] - 6s 130us/step - loss: 0.6799 - acc: 0.7607\n",
            "Epoch 19/20\n",
            "50000/50000 [==============================] - 7s 131us/step - loss: 0.6651 - acc: 0.7680\n",
            "Epoch 20/20\n",
            "50000/50000 [==============================] - 7s 133us/step - loss: 0.6506 - acc: 0.7709\n",
            "10000/10000 [==============================] - 1s 58us/step\n",
            "\n",
            "Test accuracy: 72.3%\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}