cifar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.layers.core import Dense, Reshape, Dropout, Activation\n",
    "from keras.layers import Input, BatchNormalization, GlobalAveragePooling2D\n",
    "from keras.losses import mean_squared_error, categorical_crossentropy\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import plot_model, multi_gpu_model\n",
    "\n",
    "from utils import LossHistory, plotHistory, visualization_train, visualization_data, rotation_accuracy\n",
    "from keras_capsnet.layer.capsnet import PrimaryCaps, Caps, ClassesCaps, Mask, CapsCNN\n",
    "from keras_capsnet.losses import margin\n",
    "from keras_capsnet.activations import squash\n",
    "from datasets.cifar import dataGenerator\n",
    "from keras_lropt.callbacks import LRFinderAuto as LRFinder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting for dark theme\n",
    "sns.set(context='paper', style='darkgrid', rc={'figure.facecolor':'white'}, font_scale=1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_class = 10\n",
    "input_shape = (32, 32, 3)\n",
    "m_train = 50000\n",
    "m_test = 10000\n",
    "\n",
    "batch_size = 16\n",
    "\n",
    "data_augmentation = {\n",
    "    'width_shift_range': 2,\n",
    "    'height_shift_range': 2\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainGenerator = dataGenerator('train', batch_size=batch_size, reshape=False, **data_augmentation)\n",
    "testGenerator = dataGenerator('test', batch_size=batch_size, reshape=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([    \n",
    "    Convolution2D(filters=128, kernel_size=(9, 9), padding='valid', activation='relu', input_shape=input_shape),\n",
    "    Convolution2D(filters=128, kernel_size=(9, 9), strides=2, padding='valid', activation='relu'),\n",
    "    \n",
    "    CapsCNN(64, kernel_size=(5, 5), routings=3, activation_caps=squash),\n",
    "\n",
    "    GlobalAveragePooling2D(),\n",
    "    Dense(10, name='capsule')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 128)       31232     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 8, 128)         1327232   \n",
      "_________________________________________________________________\n",
      "caps_cnn_1 (CapsCNN)         (None, 4, 4, 64)          204800    \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "capsule (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 1,563,914\n",
      "Trainable params: 1,563,914\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=3e-4),\n",
    "              loss=margin(),\n",
    "              metrics={'capsule': 'categorical_accuracy'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/10\n",
      "3125/3125 [==============================] - 85s 27ms/step - loss: 0.4229 - categorical_accuracy: 0.3465 - val_loss: 0.3860 - val_categorical_accuracy: 0.4218\n",
      "Epoch 2/10\n",
      "3125/3125 [==============================] - 82s 26ms/step - loss: 0.3672 - categorical_accuracy: 0.4463 - val_loss: 0.3489 - val_categorical_accuracy: 0.4754\n",
      "Epoch 3/10\n",
      "3125/3125 [==============================] - 81s 26ms/step - loss: 0.3414 - categorical_accuracy: 0.4930 - val_loss: 0.3387 - val_categorical_accuracy: 0.5002\n",
      "Epoch 4/10\n",
      "3125/3125 [==============================] - 82s 26ms/step - loss: 0.3235 - categorical_accuracy: 0.5242 - val_loss: 0.3210 - val_categorical_accuracy: 0.5286\n",
      "Epoch 5/10\n",
      "3125/3125 [==============================] - 82s 26ms/step - loss: 0.3100 - categorical_accuracy: 0.5497 - val_loss: 0.3063 - val_categorical_accuracy: 0.5549\n",
      "Epoch 6/10\n",
      "3125/3125 [==============================] - 80s 26ms/step - loss: 0.3001 - categorical_accuracy: 0.5624 - val_loss: 0.3086 - val_categorical_accuracy: 0.5432\n",
      "Epoch 7/10\n",
      "3125/3125 [==============================] - 82s 26ms/step - loss: 0.2904 - categorical_accuracy: 0.5825 - val_loss: 0.3042 - val_categorical_accuracy: 0.5645\n",
      "Epoch 8/10\n",
      "3125/3125 [==============================] - 83s 26ms/step - loss: 0.2826 - categorical_accuracy: 0.5924 - val_loss: 0.2931 - val_categorical_accuracy: 0.5746\n",
      "Epoch 9/10\n",
      "3125/3125 [==============================] - 81s 26ms/step - loss: 0.2761 - categorical_accuracy: 0.6049 - val_loss: 0.2953 - val_categorical_accuracy: 0.5746\n",
      "Epoch 10/10\n",
      "3125/3125 [==============================] - 82s 26ms/step - loss: 0.2698 - categorical_accuracy: 0.6156 - val_loss: 0.2852 - val_categorical_accuracy: 0.5909\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit_generator(trainGenerator,\n",
    "                           steps_per_epoch=trainGenerator.n // trainGenerator.batch_size,\n",
    "                           epochs=10,\n",
    "                           validation_data=testGenerator,\n",
    "                           validation_steps=testGenerator.n // testGenerator.batch_size,\n",
    "                           workers=4,\n",
    "                           verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotHistory(hist.history)\n",
    "plt.savefig('cifar_history.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_tensorflow_p36",
   "language": "python",
   "name": "conda_tensorflow_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}