inception-activations-keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, BatchNormalization\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 70/70 [01:48<00:00,  1.48s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048 -> 69\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "dir = os.path.expanduser('../data/food-101/inception_activations')\n",
    "for filename in tqdm(sorted(os.listdir(dir))):\n",
    "    if filename.endswith('.pickle'):\n",
    "        with open(os.path.join(dir, filename)) as f:\n",
    "            for path, vector in pickle.load(f).iteritems():\n",
    "                x.append(vector)\n",
    "                y.append(i)\n",
    "        i += 1\n",
    "\n",
    "x = np.stack(x)\n",
    "y = np.array(y, dtype=np.int32)\n",
    "\n",
    "n_classes = np.max(y)+1\n",
    "input_dim = x.shape[-1]\n",
    "print input_dim, '->', n_classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print x.shape, np.expand_dims(y, 1).shape\n",
    "# y2 = to_categorical(y, 70)\n",
    "indices = range(len(x))\n",
    "random.shuffle(indices)\n",
    "x = x[indices]\n",
    "y = y[indices]\n",
    "y2 = to_categorical(y, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_2 (Batch (None, 2048)              8192      \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 70)                35910     \n",
      "_________________________________________________________________\n",
      "activation_35 (Activation)   (None, 70)                0         \n",
      "=================================================================\n",
      "Total params: 1,095,238.0\n",
      "Trainable params: 1,090,118.0\n",
      "Non-trainable params: 5,120.0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(BatchNormalization(input_shape=(input_dim,)))\n",
    "model.add(Dense(512, activation='elu'))\n",
    "\n",
    "model.add(BatchNormalization(input_shape=(input_dim,)))\n",
    "\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Dense(n_classes))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "print model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 56000 samples, validate on 14000 samples\n",
      "Epoch 1/10\n",
      "13s - loss: 3.3630 - acc: 0.1776 - val_loss: 3.8970 - val_acc: 0.1049\n",
      "Epoch 2/10\n",
      "13s - loss: 3.3250 - acc: 0.1879 - val_loss: 3.9081 - val_acc: 0.1082\n",
      "Epoch 3/10\n",
      "13s - loss: 3.2866 - acc: 0.1953 - val_loss: 3.9170 - val_acc: 0.1036\n",
      "Epoch 4/10\n",
      "13s - loss: 3.2431 - acc: 0.2026 - val_loss: 3.9610 - val_acc: 0.1069\n",
      "Epoch 5/10\n",
      "13s - loss: 3.2132 - acc: 0.2094 - val_loss: 3.9904 - val_acc: 0.1039\n",
      "Epoch 6/10\n",
      "13s - loss: 3.1726 - acc: 0.2173 - val_loss: 4.0325 - val_acc: 0.1046\n",
      "Epoch 7/10\n",
      "13s - loss: 3.1459 - acc: 0.2222 - val_loss: 4.0474 - val_acc: 0.1039\n",
      "Epoch 8/10\n",
      "13s - loss: 3.1164 - acc: 0.2279 - val_loss: 4.0958 - val_acc: 0.1016\n",
      "Epoch 9/10\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-9702ee3b481f>\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\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\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[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/models.pyc\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, **kwargs)\u001b[0m\n\u001b[1;32m    843\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    846\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\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, **kwargs)\u001b[0m\n\u001b[1;32m   1483\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1484\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1485\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1487\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\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/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1152\u001b[0m                         val_outs = self._test_loop(val_f, val_ins,\n\u001b[1;32m   1153\u001b[0m                                                    \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m                                                    verbose=0)\n\u001b[0m\u001b[1;32m   1155\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1156\u001b[0m                             \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_test_loop\u001b[0;34m(self, f, ins, batch_size, verbose)\u001b[0m\n\u001b[1;32m   1254\u001b[0m                 \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1256\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1257\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1258\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2073\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2074\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2075\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2076\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2077\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten_dict_items\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_val\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 916\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0msubfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubfeed_val\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_feed_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_val\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    917\u001b[0m           \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m             subfeed_t = self.graph.as_graph_element(subfeed, allow_tensor=True,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_feed_fn\u001b[0;34m(feed, feed_val)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;34m\"\"\"Perform either run or partial_run, depending the presence of `handle`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_feed_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m       \u001b[0;32mfor\u001b[0m \u001b[0mtensor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_REGISTERED_EXPANSIONS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m           \u001b[0;32mreturn\u001b[0m \u001b[0mfeed_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(x, y2, epochs=10, batch_size=32, validation_split=0.2, verbose=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[40 40 40 40 40 40 40 40 40 40]\n"
     ]
    }
   ],
   "source": [
    "print np.argmax(model.predict(x[2000:2010]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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