Deep Fashion Network Re-train Category.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import PIL\n",
    "from keras.applications import VGG16,imagenet_utils\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Flatten\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import pickle\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bbox = pd.read_table('Data/DeepFashion/list_bbox.txt',sep='\\s+')\n",
    "cat = pd.read_table('Data/DeepFashion/list_category_img.txt',sep='\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joined = bbox.join(cat,lsuffix='_',rsuffix='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name_</th>\n",
       "      <th>x_1</th>\n",
       "      <th>y_1</th>\n",
       "      <th>x_2</th>\n",
       "      <th>y_2</th>\n",
       "      <th>image_name</th>\n",
       "      <th>category_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000001.jpg</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "      <td>232</td>\n",
       "      <td>273</td>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000001.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000002.jpg</td>\n",
       "      <td>67</td>\n",
       "      <td>59</td>\n",
       "      <td>155</td>\n",
       "      <td>161</td>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000002.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000003.jpg</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>156</td>\n",
       "      <td>200</td>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000003.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000004.jpg</td>\n",
       "      <td>51</td>\n",
       "      <td>62</td>\n",
       "      <td>167</td>\n",
       "      <td>182</td>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000004.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000005.jpg</td>\n",
       "      <td>46</td>\n",
       "      <td>88</td>\n",
       "      <td>166</td>\n",
       "      <td>262</td>\n",
       "      <td>img/Sheer_Pleated-Front_Blouse/img_00000005.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       image_name_  x_1  y_1  x_2  y_2  \\\n",
       "0  img/Sheer_Pleated-Front_Blouse/img_00000001.jpg   72   79  232  273   \n",
       "1  img/Sheer_Pleated-Front_Blouse/img_00000002.jpg   67   59  155  161   \n",
       "2  img/Sheer_Pleated-Front_Blouse/img_00000003.jpg   65   65  156  200   \n",
       "3  img/Sheer_Pleated-Front_Blouse/img_00000004.jpg   51   62  167  182   \n",
       "4  img/Sheer_Pleated-Front_Blouse/img_00000005.jpg   46   88  166  262   \n",
       "\n",
       "                                        image_name  category_label  \n",
       "0  img/Sheer_Pleated-Front_Blouse/img_00000001.jpg               3  \n",
       "1  img/Sheer_Pleated-Front_Blouse/img_00000002.jpg               3  \n",
       "2  img/Sheer_Pleated-Front_Blouse/img_00000003.jpg               3  \n",
       "3  img/Sheer_Pleated-Front_Blouse/img_00000004.jpg               3  \n",
       "4  img/Sheer_Pleated-Front_Blouse/img_00000005.jpg               3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = joined['category_label'].value_counts()\n",
    "top_labels = labels[labels>1000].index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = pd.DataFrame([], columns=joined.columns)\n",
    "for l in top_labels:\n",
    "    has_label = joined[joined['category_label']==l]\n",
    "    sample = np.random.randint(has_label.shape[0],size=1000)\n",
    "    subset = subset.append(has_label.iloc[sample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset['category_label'] = pd.Categorical(subset['category_label'])\n",
    "subset['code'] = subset['category_label'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputShape = (224, 224)\n",
    "preprocess = imagenet_utils.preprocess_input\n",
    "\n",
    "def preprocess_DF(bbox):\n",
    "    image = crop_resize_DF(bbox)\n",
    "    image = img_to_array(image)\n",
    "    image = np.expand_dims(image,axis=0)\n",
    "    \n",
    "    return preprocess(image)\n",
    "\n",
    "def crop_resize_DF(bbox):\n",
    "    img = PIL.Image.open('./Data/DeepFashion/'+bbox['image_name'])\n",
    "    lx = bbox['x_1']\n",
    "    ly = bbox['y_1']\n",
    "    ux = bbox['x_2']\n",
    "    uy = bbox['y_2']\n",
    "    \n",
    "    img = img.crop((lx,ly,ux,uy))\n",
    "    img = img.resize(inputShape, PIL.Image.ANTIALIAS)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(df, batch_size):\n",
    "    while True:\n",
    "        subset = np.random.randint(df.shape[0],size=batch_size)\n",
    "        proc_imgs = []\n",
    "        for i in subset:\n",
    "            proc_imgs.append(np.squeeze(preprocess_DF(df.iloc[i])))\n",
    "    \n",
    "        attr = df.iloc[subset]['code'].values\n",
    "        yield np.array(proc_imgs), attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gen_batch(df,batch_size):\n",
    "    subset = np.random.randint(df.shape[0],size=batch_size)\n",
    "    proc_imgs = []\n",
    "    for i in subset:\n",
    "        proc_imgs.append(np.squeeze(preprocess_DF(df.iloc[i])))\n",
    "        print(df.iloc[i]['image_name'],df.iloc[i]['code'])\n",
    "    \n",
    "    attr = df.iloc[subset]['code'].values\n",
    "    return np.array(proc_imgs), attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "attr = subset['category_label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    0, 1000, 1000,    0,    0, 1000,    0,    0,    0, 1000,\n",
       "       1000,    0,    0,    0,    0, 1000, 1000, 1000, 1000,    0,    0,\n",
       "          0,    0, 1000,    0, 1000,    0,    0, 1000, 1000,    0, 1000,\n",
       "       1000, 1000, 1000,    0,    0,    0, 1000,    0, 1000, 1000,    0,\n",
       "       1000,    0,    0,    0, 1000])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(classes,counts) = np.unique(attr,return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  6, 10, 11, 16, 17, 18, 19, 24, 26, 29, 30, 32, 33, 34, 35,\n",
       "       39, 41, 42, 44, 48])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_weights = joined.shape[0]/(counts*len(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13.14645455,  13.14645455,  13.14645455,  13.14645455,\n",
       "        13.14645455,  13.14645455,  13.14645455,  13.14645455,\n",
       "        13.14645455,  13.14645455,  13.14645455,  13.14645455,\n",
       "        13.14645455,  13.14645455,  13.14645455,  13.14645455,\n",
       "        13.14645455,  13.14645455,  13.14645455,  13.14645455,\n",
       "        13.14645455,  13.14645455])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "from keras import regularizers\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers import Dropout\n",
    "l = 0.01\n",
    "\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "x = base_model.output\n",
    "\n",
    "x = Flatten(name='flatten')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(rate=0.2)(x)\n",
    "\n",
    "x = Dense(4096, activation='relu', name='fc1', kernel_initializer='glorot_normal',\n",
    "         bias_initializer='glorot_uniform', kernel_regularizer=regularizers.l2(l))(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(rate=0.2)(x)\n",
    "\n",
    "predictions = Dense(len(classes), activation='softmax',name='predictions', kernel_initializer='glorot_normal',\n",
    "         bias_initializer='glorot_uniform', kernel_regularizer=regularizers.l2(l))(x)\n",
    "\n",
    "new_model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "\n",
    "new_model.compile(optimizer='Adadelta', loss='sparse_categorical_crossentropy',\n",
    "                 metrics = ['sparse_categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_model.load_weights('DF-category-retrain.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "70/70 [==============================] - 168s - loss: 4.2671 - sparse_categorical_accuracy: 0.6184 - val_loss: 4.1439 - val_sparse_categorical_accuracy: 0.6562\n",
      "Epoch 2/80\n",
      "70/70 [==============================] - 168s - loss: 3.4504 - sparse_categorical_accuracy: 0.7382 - val_loss: 2.9864 - val_sparse_categorical_accuracy: 0.7969\n",
      "Epoch 3/80\n",
      "70/70 [==============================] - 170s - loss: 2.8829 - sparse_categorical_accuracy: 0.7834 - val_loss: 2.6766 - val_sparse_categorical_accuracy: 0.7812\n",
      "Epoch 4/80\n",
      "70/70 [==============================] - 169s - loss: 2.6509 - sparse_categorical_accuracy: 0.7980 - val_loss: 2.4615 - val_sparse_categorical_accuracy: 0.8359\n",
      "Epoch 5/80\n",
      "70/70 [==============================] - 168s - loss: 2.5525 - sparse_categorical_accuracy: 0.8090 - val_loss: 2.6361 - val_sparse_categorical_accuracy: 0.8281\n",
      "Epoch 6/80\n",
      "70/70 [==============================] - 169s - loss: 2.4985 - sparse_categorical_accuracy: 0.8116 - val_loss: 2.4659 - val_sparse_categorical_accuracy: 0.8281\n",
      "Epoch 7/80\n",
      "70/70 [==============================] - 169s - loss: 2.4071 - sparse_categorical_accuracy: 0.8234 - val_loss: 2.3387 - val_sparse_categorical_accuracy: 0.8359\n",
      "Epoch 8/80\n",
      "70/70 [==============================] - 169s - loss: 2.3537 - sparse_categorical_accuracy: 0.8251 - val_loss: 2.2391 - val_sparse_categorical_accuracy: 0.8359\n",
      "Epoch 9/80\n",
      "70/70 [==============================] - 168s - loss: 2.2428 - sparse_categorical_accuracy: 0.8323 - val_loss: 2.2172 - val_sparse_categorical_accuracy: 0.8594\n",
      "Epoch 10/80\n",
      "70/70 [==============================] - 169s - loss: 2.1712 - sparse_categorical_accuracy: 0.8473 - val_loss: 2.1614 - val_sparse_categorical_accuracy: 0.8594\n",
      "Epoch 11/80\n",
      "70/70 [==============================] - 169s - loss: 2.0770 - sparse_categorical_accuracy: 0.8519 - val_loss: 2.1298 - val_sparse_categorical_accuracy: 0.8438\n",
      "Epoch 12/80\n",
      "70/70 [==============================] - 168s - loss: 1.9917 - sparse_categorical_accuracy: 0.8604 - val_loss: 1.9654 - val_sparse_categorical_accuracy: 0.8438\n",
      "Epoch 13/80\n",
      "70/70 [==============================] - 169s - loss: 1.9039 - sparse_categorical_accuracy: 0.8682 - val_loss: 1.9369 - val_sparse_categorical_accuracy: 0.8594\n",
      "Epoch 14/80\n",
      "70/70 [==============================] - 168s - loss: 1.9434 - sparse_categorical_accuracy: 0.8567 - val_loss: 1.8539 - val_sparse_categorical_accuracy: 0.8750\n",
      "Epoch 15/80\n",
      "70/70 [==============================] - 168s - loss: 1.8581 - sparse_categorical_accuracy: 0.8700 - val_loss: 1.7554 - val_sparse_categorical_accuracy: 0.9062\n",
      "Epoch 16/80\n",
      "29/70 [===========>..................] - ETA: 88s - loss: 1.8252 - sparse_categorical_accuracy: 0.8763"
     ]
    },
    {
     "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-19-b76cf2cc2dbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m h = new_model.fit_generator(generator(subset,128),steps_per_epoch=70,epochs=80,\n\u001b[1;32m      6\u001b[0m                             \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                             verbose=1,callbacks=[tbCB,checkpointCB])\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)\u001b[0m\n\u001b[1;32m   1838\u001b[0m                     outs = self.train_on_batch(x, y,\n\u001b[1;32m   1839\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-> 1840\u001b[0;31m                                                class_weight=class_weight)\n\u001b[0m\u001b[1;32m   1841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1842\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\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[0;32m/usr/local/lib/python3.4/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1563\u001b[0m             \u001b[0mins\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[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\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/python3.4/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2266\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2267\u001b[0m                               \u001b[0mfeed_dict\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[0;32m-> 2268\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2269\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   2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\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    780\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/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\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/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras import callbacks\n",
    "tbCB = callbacks.TensorBoard(log_dir=\"logs/DF_category{}\".format(time.time()), histogram_freq=0, write_graph=False, write_images=False)\n",
    "checkpointCB = callbacks.ModelCheckpoint(\"checkpoints/DF_category{}\".format(time.time()))\n",
    "\n",
    "h = new_model.fit_generator(generator(subset,128),steps_per_epoch=70,epochs=80,\n",
    "                            validation_data=generator(subset,128),validation_steps=1,\n",
    "                            verbose=1,callbacks=[tbCB,checkpointCB])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_model.save('DF-Category-trained-7-24.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "h = new_model.fit_generator(generator(subset,128),steps_per_epoch=70,epochs=80,\n",
    "                            validation_data=generator(subset,128),validation_steps=1,\n",
    "                            verbose=1,callbacks=[tbCB,checkpointCB])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_model.save_weights('DF-category-retrain.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p = new_model.predict_generator(generator(subset,64),steps=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.argmax(p,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "counts[17]/np.sum(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(np.arange(len(counts)),counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, Y = gen_batch(subset,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = new_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.argmax(p,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.sum(np.argmax(p,axis=1)==Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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