knowledge_distillation_for_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 numpy as np\n",
    "import sys\n",
    "sys.path.append('utils/')\n",
    "\n",
    "import keras\n",
    "from keras import optimizers\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "# use non standard flow_from_directory\n",
    "from image_preprocessing_ver2 import ImageDataGenerator\n",
    "# it outputs y_batch that contains onehot targets and logits\n",
    "# logits came from xception\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Lambda, concatenate, Activation\n",
    "from keras.losses import categorical_crossentropy as logloss\n",
    "from keras.metrics import categorical_accuracy, top_k_categorical_accuracy\n",
    "from keras import backend as K\n",
    "\n",
    "from mobilenet import get_mobilenet\n",
    "from keras.applications.mobilenet import preprocess_input\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_dir = '/home/ubuntu/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_logits = np.load(data_dir + 'train_logits.npy')[()]\n",
    "val_logits = np.load(data_dir + 'val_logits.npy')[()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "# note: i'm also passing dicts of logits\n",
    "train_generator = data_generator.flow_from_directory(\n",
    "    data_dir + 'train', train_logits,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=64\n",
    ")\n",
    "\n",
    "val_generator = data_generator.flow_from_directory(\n",
    "    data_dir + 'val', val_logits,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=64\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temperature = 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_mobilenet(224, alpha=0.25, weight_decay=1e-5, dropout=0.1)\n",
    "\n",
    "# remove softmax\n",
    "model.layers.pop()\n",
    "\n",
    "# usual probabilities\n",
    "logits = model.layers[-1].output\n",
    "probabilities = Activation('softmax')(logits)\n",
    "\n",
    "# softed probabilities\n",
    "logits_T = Lambda(lambda x: x/temperature)(logits)\n",
    "probabilities_T = Activation('softmax')(logits_T)\n",
    "\n",
    "output = concatenate([probabilities, probabilities_T])\n",
    "model = Model(model.input, output)\n",
    "# now model outputs 512 dimensional vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create custom loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def knowledge_distillation_loss(y_true, y_pred, lambda_const):    \n",
    "    \n",
    "    # split in \n",
    "    #    onehot hard true targets\n",
    "    #    logits from xception\n",
    "    y_true, logits = y_true[:, :256], y_true[:, 256:]\n",
    "    \n",
    "    # convert logits to soft targets\n",
    "    y_soft = K.softmax(logits/temperature)\n",
    "    \n",
    "    # split in \n",
    "    #    usual output probabilities\n",
    "    #    probabilities made softer with temperature\n",
    "    y_pred, y_pred_soft = y_pred[:, :256], y_pred[:, 256:]    \n",
    "    \n",
    "    return lambda_const*logloss(y_true, y_pred) + logloss(y_soft, y_pred_soft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For testing use usual output probabilities (without temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(y_true, y_pred):\n",
    "    y_true = y_true[:, :256]\n",
    "    y_pred = y_pred[:, :256]\n",
    "    return categorical_accuracy(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def top_5_accuracy(y_true, y_pred):\n",
    "    y_true = y_true[:, :256]\n",
    "    y_pred = y_pred[:, :256]\n",
    "    return top_k_categorical_accuracy(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def categorical_crossentropy(y_true, y_pred):\n",
    "    y_true = y_true[:, :256]\n",
    "    y_pred = y_pred[:, :256]\n",
    "    return logloss(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# logloss with only soft probabilities and targets\n",
    "def soft_logloss(y_true, y_pred):     \n",
    "    logits = y_true[:, 256:]\n",
    "    y_soft = K.softmax(logits/temperature)\n",
    "    y_pred_soft = y_pred[:, 256:]    \n",
    "    return logloss(y_soft, y_pred_soft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lambda_const = 0.07\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.SGD(lr=1e-1, momentum=0.9, nesterov=True), \n",
    "    loss=lambda y_true, y_pred: knowledge_distillation_loss(y_true, y_pred, lambda_const), \n",
    "    metrics=[accuracy, top_5_accuracy, categorical_crossentropy, soft_logloss]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "400/400 [==============================] - 24s - loss: 5.8086 - accuracy: 0.2173 - top_5_accuracy: 0.4171 - categorical_crossentropy: 4.0499 - soft_logloss: 5.5225 - val_loss: 5.6917 - val_accuracy: 0.4475 - val_top_5_accuracy: 0.7285 - val_categorical_crossentropy: 2.6774 - val_soft_logloss: 5.5013\n",
      "Epoch 2/30\n",
      "400/400 [==============================] - 23s - loss: 5.6726 - accuracy: 0.4953 - top_5_accuracy: 0.7514 - categorical_crossentropy: 2.4414 - soft_logloss: 5.4984 - val_loss: 5.6416 - val_accuracy: 0.5660 - val_top_5_accuracy: 0.8070 - val_categorical_crossentropy: 2.0523 - val_soft_logloss: 5.4944\n",
      "Epoch 3/30\n",
      "400/400 [==============================] - 24s - loss: 5.6378 - accuracy: 0.5785 - top_5_accuracy: 0.8072 - categorical_crossentropy: 2.0036 - soft_logloss: 5.4938 - val_loss: 5.6240 - val_accuracy: 0.5951 - val_top_5_accuracy: 0.8303 - val_categorical_crossentropy: 1.8349 - val_soft_logloss: 5.4916\n",
      "Epoch 4/30\n",
      "400/400 [==============================] - 23s - loss: 5.6212 - accuracy: 0.6181 - top_5_accuracy: 0.8376 - categorical_crossentropy: 1.7899 - soft_logloss: 5.4917 - val_loss: 5.6167 - val_accuracy: 0.6207 - val_top_5_accuracy: 0.8410 - val_categorical_crossentropy: 1.7269 - val_soft_logloss: 5.4914\n",
      "Epoch 5/30\n",
      "400/400 [==============================] - 23s - loss: 5.6116 - accuracy: 0.6418 - top_5_accuracy: 0.8532 - categorical_crossentropy: 1.6668 - soft_logloss: 5.4904 - val_loss: 5.6128 - val_accuracy: 0.6244 - val_top_5_accuracy: 0.8428 - val_categorical_crossentropy: 1.6673 - val_soft_logloss: 5.4915\n",
      "Epoch 6/30\n",
      "400/400 [==============================] - 23s - loss: 5.6048 - accuracy: 0.6636 - top_5_accuracy: 0.8650 - categorical_crossentropy: 1.5771 - soft_logloss: 5.4896 - val_loss: 5.6079 - val_accuracy: 0.6213 - val_top_5_accuracy: 0.8496 - val_categorical_crossentropy: 1.6315 - val_soft_logloss: 5.4888\n",
      "Epoch 7/30\n",
      "400/400 [==============================] - 23s - loss: 5.6001 - accuracy: 0.6760 - top_5_accuracy: 0.8740 - categorical_crossentropy: 1.5145 - soft_logloss: 5.4891 - val_loss: 5.6029 - val_accuracy: 0.6369 - val_top_5_accuracy: 0.8518 - val_categorical_crossentropy: 1.5752 - val_soft_logloss: 5.4875\n",
      "Epoch 8/30\n",
      "400/400 [==============================] - 24s - loss: 5.5966 - accuracy: 0.6841 - top_5_accuracy: 0.8798 - categorical_crossentropy: 1.4656 - soft_logloss: 5.4888 - val_loss: 5.6024 - val_accuracy: 0.6422 - val_top_5_accuracy: 0.8596 - val_categorical_crossentropy: 1.5532 - val_soft_logloss: 5.4884\n",
      "Epoch 9/30\n",
      "400/400 [==============================] - 23s - loss: 5.5935 - accuracy: 0.6929 - top_5_accuracy: 0.8843 - categorical_crossentropy: 1.4258 - soft_logloss: 5.4883 - val_loss: 5.6016 - val_accuracy: 0.6430 - val_top_5_accuracy: 0.8539 - val_categorical_crossentropy: 1.5479 - val_soft_logloss: 5.4878\n",
      "Epoch 10/30\n",
      "400/400 [==============================] - 23s - loss: 5.5913 - accuracy: 0.7013 - top_5_accuracy: 0.8882 - categorical_crossentropy: 1.3932 - soft_logloss: 5.4882 - val_loss: 5.6010 - val_accuracy: 0.6479 - val_top_5_accuracy: 0.8586 - val_categorical_crossentropy: 1.5299 - val_soft_logloss: 5.4882\n",
      "Epoch 11/30\n",
      "400/400 [==============================] - 23s - loss: 5.5893 - accuracy: 0.7086 - top_5_accuracy: 0.8922 - categorical_crossentropy: 1.3639 - soft_logloss: 5.4881 - val_loss: 5.6000 - val_accuracy: 0.6496 - val_top_5_accuracy: 0.8643 - val_categorical_crossentropy: 1.5198 - val_soft_logloss: 5.4878\n",
      "Epoch 12/30\n",
      "400/400 [==============================] - 23s - loss: 5.5876 - accuracy: 0.7136 - top_5_accuracy: 0.8962 - categorical_crossentropy: 1.3396 - soft_logloss: 5.4879 - val_loss: 5.6018 - val_accuracy: 0.6475 - val_top_5_accuracy: 0.8602 - val_categorical_crossentropy: 1.5318 - val_soft_logloss: 5.4886\n",
      "Epoch 13/30\n",
      "400/400 [==============================] - 23s - loss: 5.5863 - accuracy: 0.7170 - top_5_accuracy: 0.8991 - categorical_crossentropy: 1.3194 - soft_logloss: 5.4879 - val_loss: 5.5964 - val_accuracy: 0.6559 - val_top_5_accuracy: 0.8660 - val_categorical_crossentropy: 1.4852 - val_soft_logloss: 5.4863\n",
      "Epoch 14/30\n",
      "400/400 [==============================] - 24s - loss: 5.5855 - accuracy: 0.7220 - top_5_accuracy: 0.8981 - categorical_crossentropy: 1.3094 - soft_logloss: 5.4877 - val_loss: 5.5999 - val_accuracy: 0.6492 - val_top_5_accuracy: 0.8609 - val_categorical_crossentropy: 1.4970 - val_soft_logloss: 5.4889\n",
      "Epoch 15/30\n",
      "400/400 [==============================] - 24s - loss: 5.5845 - accuracy: 0.7254 - top_5_accuracy: 0.9034 - categorical_crossentropy: 1.2905 - soft_logloss: 5.4879 - val_loss: 5.5961 - val_accuracy: 0.6584 - val_top_5_accuracy: 0.8604 - val_categorical_crossentropy: 1.4825 - val_soft_logloss: 5.4860\n",
      "Epoch 16/30\n",
      "400/400 [==============================] - 24s - loss: 5.5835 - accuracy: 0.7258 - top_5_accuracy: 0.9059 - categorical_crossentropy: 1.2741 - soft_logloss: 5.4879 - val_loss: 5.5975 - val_accuracy: 0.6564 - val_top_5_accuracy: 0.8686 - val_categorical_crossentropy: 1.4793 - val_soft_logloss: 5.4875\n",
      "Epoch 17/30\n",
      "400/400 [==============================] - 23s - loss: 5.5794 - accuracy: 0.7401 - top_5_accuracy: 0.9128 - categorical_crossentropy: 1.2293 - soft_logloss: 5.4868 - val_loss: 5.5948 - val_accuracy: 0.6627 - val_top_5_accuracy: 0.8682 - val_categorical_crossentropy: 1.4618 - val_soft_logloss: 5.4860\n",
      "Epoch 18/30\n",
      "400/400 [==============================] - 24s - loss: 5.5788 - accuracy: 0.7466 - top_5_accuracy: 0.9130 - categorical_crossentropy: 1.2241 - soft_logloss: 5.4866 - val_loss: 5.5970 - val_accuracy: 0.6592 - val_top_5_accuracy: 0.8641 - val_categorical_crossentropy: 1.4787 - val_soft_logloss: 5.4870\n",
      "Epoch 19/30\n",
      "400/400 [==============================] - 23s - loss: 5.5783 - accuracy: 0.7441 - top_5_accuracy: 0.9142 - categorical_crossentropy: 1.2181 - soft_logloss: 5.4866 - val_loss: 5.5931 - val_accuracy: 0.6641 - val_top_5_accuracy: 0.8715 - val_categorical_crossentropy: 1.4433 - val_soft_logloss: 5.4856\n",
      "Epoch 20/30\n",
      "400/400 [==============================] - 23s - loss: 5.5787 - accuracy: 0.7448 - top_5_accuracy: 0.9116 - categorical_crossentropy: 1.2227 - soft_logloss: 5.4866 - val_loss: 5.5967 - val_accuracy: 0.6607 - val_top_5_accuracy: 0.8658 - val_categorical_crossentropy: 1.4738 - val_soft_logloss: 5.4871\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb007d570f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(\n",
    "    train_generator, \n",
    "    steps_per_epoch=400, epochs=30, verbose=1,\n",
    "    callbacks=[\n",
    "        EarlyStopping(monitor='val_accuracy', patience=4, min_delta=0.01), \n",
    "        ReduceLROnPlateau(monitor='val_accuracy', factor=0.1, patience=2, epsilon=0.007)\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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1yLSS8FAws7uAS4CDzrklEZ4/D/gVsNNf9ZBz7jvjva5CYXLp6u3ntdomXtjW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\nEBGRIQoFEREZ8v8BO6upO01BxZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb007d6a128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['categorical_crossentropy'], label='train');\n",
    "plt.plot(model.history.history['val_categorical_crossentropy'], label='val');\n",
    "plt.legend();\n",
    "plt.xlabel('epoch');\n",
    "plt.ylabel('logloss');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rJ50hLyE2sxvMrMrMqkKhUIwhH4aBshajVES96/nNFOWk8/nT5459DCIi40Cs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\nCiIiEqGkICIiEf8fCnWPO1wdV+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004fedf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['accuracy'], label='train');\n",
    "plt.plot(model.history.history['val_accuracy'], label='val');\n",
    "plt.legend();\n",
    "plt.xlabel('epoch');\n",
    "plt.ylabel('accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IdS2dgCboEZHMkWyJ4b8JEoERDIkxH3gVOHWM/VYCC8xsPkFCuBE4ZNq0cBym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\nIjKEEoOIiAzx/wEl4REedkiXWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004f23550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['top_5_accuracy'], label='train');\n",
    "plt.plot(model.history.history['val_top_5_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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5120 images belonging to 256 classes.\n"
     ]
    }
   ],
   "source": [
    "val_generator_no_shuffle = data_generator.flow_from_directory(\n",
    "    data_dir + 'val', val_logits,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=64, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.595249432325363, 0.66171875000000002, 0.86679687500000002, 1.4644993908703328, 5.4862456738948824]\n"
     ]
    }
   ],
   "source": [
    "print(model.evaluate_generator(val_generator_no_shuffle, 80))"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}