vanilla_mobilenet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import optimizers\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "from keras.applications.mobilenet import preprocess_input\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from mobilenet import get_mobilenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = '/home/ubuntu/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25600 images belonging to 256 classes.\n",
      "Found 5120 images belonging to 256 classes.\n"
     ]
    }
   ],
   "source": [
    "data_generator = ImageDataGenerator(\n",
    "    data_format='channels_last',\n",
    "    preprocessing_function=preprocess_input\n",
    ")\n",
    "\n",
    "train_generator = data_generator.flow_from_directory(\n",
    "    data_dir + 'train', \n",
    "    target_size=(224, 224),\n",
    "    batch_size=64\n",
    ")\n",
    "\n",
    "val_generator = data_generator.flow_from_directory(\n",
    "    data_dir + 'val', shuffle=False,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=64\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284336"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = get_mobilenet(224, alpha=0.25, weight_decay=1e-5, dropout=0.1)\n",
    "model.count_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=optimizers.SGD(lr=1e-2, momentum=0.9, nesterov=True), \n",
    "    loss='categorical_crossentropy', metrics=['accuracy', 'top_k_categorical_accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "400/400 [==============================] - 24s - loss: 3.9156 - acc: 0.2379 - top_k_categorical_accuracy: 0.4393 - val_loss: 2.5220 - val_acc: 0.4686 - val_top_k_categorical_accuracy: 0.7375\n",
      "Epoch 2/30\n",
      "400/400 [==============================] - 23s - loss: 2.2534 - acc: 0.5200 - top_k_categorical_accuracy: 0.7672 - val_loss: 1.9509 - val_acc: 0.5574 - val_top_k_categorical_accuracy: 0.8055\n",
      "Epoch 3/30\n",
      "400/400 [==============================] - 23s - loss: 1.8197 - acc: 0.5913 - top_k_categorical_accuracy: 0.8228 - val_loss: 1.7527 - val_acc: 0.5898 - val_top_k_categorical_accuracy: 0.8256\n",
      "Epoch 4/30\n",
      "400/400 [==============================] - 23s - loss: 1.6057 - acc: 0.6363 - top_k_categorical_accuracy: 0.8502 - val_loss: 1.6489 - val_acc: 0.6090 - val_top_k_categorical_accuracy: 0.8389\n",
      "Epoch 5/30\n",
      "400/400 [==============================] - 23s - loss: 1.4687 - acc: 0.6618 - top_k_categorical_accuracy: 0.8668 - val_loss: 1.5938 - val_acc: 0.6188 - val_top_k_categorical_accuracy: 0.8400\n",
      "Epoch 6/30\n",
      "400/400 [==============================] - 23s - loss: 1.3803 - acc: 0.6791 - top_k_categorical_accuracy: 0.8756 - val_loss: 1.5586 - val_acc: 0.6281 - val_top_k_categorical_accuracy: 0.8412\n",
      "Epoch 7/30\n",
      "400/400 [==============================] - 23s - loss: 1.2948 - acc: 0.6963 - top_k_categorical_accuracy: 0.8887 - val_loss: 1.5236 - val_acc: 0.6350 - val_top_k_categorical_accuracy: 0.8502\n",
      "Epoch 8/30\n",
      "400/400 [==============================] - 23s - loss: 1.2423 - acc: 0.7039 - top_k_categorical_accuracy: 0.8960 - val_loss: 1.5072 - val_acc: 0.6363 - val_top_k_categorical_accuracy: 0.8510\n",
      "Epoch 9/30\n",
      "400/400 [==============================] - 23s - loss: 1.1892 - acc: 0.7147 - top_k_categorical_accuracy: 0.9007 - val_loss: 1.5006 - val_acc: 0.6393 - val_top_k_categorical_accuracy: 0.8512\n",
      "Epoch 10/30\n",
      "400/400 [==============================] - 23s - loss: 1.1480 - acc: 0.7249 - top_k_categorical_accuracy: 0.9066 - val_loss: 1.4935 - val_acc: 0.6406 - val_top_k_categorical_accuracy: 0.8551\n",
      "Epoch 11/30\n",
      "400/400 [==============================] - 23s - loss: 1.1107 - acc: 0.7330 - top_k_categorical_accuracy: 0.9092 - val_loss: 1.4888 - val_acc: 0.6395 - val_top_k_categorical_accuracy: 0.8541\n",
      "Epoch 12/30\n",
      "400/400 [==============================] - 23s - loss: 1.0489 - acc: 0.7484 - top_k_categorical_accuracy: 0.9185 - val_loss: 1.4566 - val_acc: 0.6459 - val_top_k_categorical_accuracy: 0.8605\n",
      "Epoch 13/30\n",
      "400/400 [==============================] - 24s - loss: 1.0338 - acc: 0.7553 - top_k_categorical_accuracy: 0.9205 - val_loss: 1.4537 - val_acc: 0.6475 - val_top_k_categorical_accuracy: 0.8611\n",
      "Epoch 14/30\n",
      "400/400 [==============================] - 23s - loss: 1.0380 - acc: 0.7545 - top_k_categorical_accuracy: 0.9200 - val_loss: 1.4546 - val_acc: 0.6465 - val_top_k_categorical_accuracy: 0.8594\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe7e1fea080>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "    train_generator, \n",
    "    steps_per_epoch=400, epochs=30, verbose=1,\n",
    "    callbacks=[\n",
    "        ReduceLROnPlateau(monitor='val_acc', factor=0.1, patience=2, epsilon=0.007),\n",
    "        EarlyStopping(monitor='val_acc', patience=4, min_delta=0.01)\n",
    "    ],\n",
    "    validation_data=val_generator, validation_steps=80, workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss/epoch plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WSyN37zCzKHCtmQH80d1/c5LD2oDPufvLYc9ilZn9zt039Gj3J3e/tM+V99WGx+HxT8HH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\niEgXhYKIiHRRKIiISBeFgoiIdPn/ft2jjoeXnkQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7e1ff8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['loss'], label='train');\n",
    "plt.plot(model.history.history['val_loss'], label='val');\n",
    "plt.legend();\n",
    "plt.xlabel('epoch');\n",
    "plt.ylabel('logloss');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LoZ9Ny4HMQhg3AZJSYNx4SBwHicmQNA4SU4JXUvhYYvhYUkq/c333e7bDn0lIDO4WQt3B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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7dfed7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['acc'], label='train');\n",
    "plt.plot(model.history.history['val_acc'], label='val');\n",
    "plt.legend();\n",
    "plt.xlabel('epoch');\n",
    "plt.ylabel('accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XYFMiA0tFg0MRtrV2cbISg4iksXgTw98A/cAPo68+4GOJCipV7d5/iP7BCEvV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\nZAQlBhERGUGJQURERlBiEBGREf4/LQh7dywQRgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7dff484e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['top_k_categorical_accuracy'], label='train');\n",
    "plt.plot(model.history.history['val_top_k_categorical_accuracy'], label='val');\n",
    "plt.legend();\n",
    "plt.xlabel('epoch');\n",
    "plt.ylabel('top5_accuracy');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.4546428885310889, 0.646484375, 0.859375]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(val_generator, 80)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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