model_pretrained_extract_feature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n",
      "\t image count mean: 183\n",
      "\t image count : 915\n",
      "train\n",
      "\t image count mean: 368\n",
      "\t image count : 1840\n",
      "validation\n",
      "\t image count mean: 181\n",
      "\t image count : 905\n",
      "---------------\n",
      "total image count: 3660\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "base_dir = 'dataset/flowers-recognition_configed'\n",
    "input_data_dir = {'test':None, 'train':None, 'validation':None}\n",
    "\n",
    "totalcounter = 0\n",
    "dfs = {'train':None, 'validation':None, 'test':None}\n",
    "for i in input_data_dir:\n",
    "    print(i)\n",
    "    classes_names = os.listdir(os.path.join(base_dir, i))\n",
    "    input_data_dir[i] = os.path.join(base_dir, i)\n",
    "    classes = {}\n",
    "    for s in classes_names:\n",
    "        classes[s] = len(os.listdir(os.path.join(base_dir, i, s)))\n",
    "        \n",
    "    inputdata = {'flower':None, 'count':None}\n",
    "    inputdata['flower'] = [f for f, c in classes.items()]\n",
    "    inputdata['count'] = [c for f, c in classes.items()]\n",
    "    \n",
    "    dfs[i] = pd.DataFrame(inputdata)\n",
    "    totalcounter += dfs[i]['count'].sum()\n",
    "    print('\\t', 'image count mean:', int(dfs[i]['count'].mean()))\n",
    "    print('\\t', 'image count :', int(dfs[i]['count'].sum()))\n",
    "print('-'.join(['' for i in range(0, 16)]))\n",
    "print('total image count:', totalcounter)    \n",
    "\n",
    "class_count = len(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hyper parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import optimizers\n",
    "import math\n",
    "\n",
    "h_params = {}\n",
    "\n",
    "# image\n",
    "h_params['image_size'] = 150 # defult 150\n",
    "h_params['batch_size'] = 20 # defult 20\n",
    "\n",
    "# fit\n",
    "h_params['epochs'] = 40\n",
    "h_params['steps_per_epoch'] = math.ceil(dfs['train']['count'].sum()/h_params['batch_size']) # defult 100\n",
    "h_params['validation_steps'] = math.ceil(dfs['validation']['count'].sum()/h_params['batch_size']) # defult 50\n",
    "\n",
    "# compile\n",
    "h_params['learning_rate'] = 1e-3 # defult 2e-5\n",
    "h_params['optimizer'] = optimizers.RMSprop(lr=h_params['learning_rate'])\n",
    "h_params['metrics'] = ['acc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 150, 150, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                include_top=False,\n",
    "                input_shape=(h_params['image_size'],\n",
    "                             h_params['image_size'],\n",
    "                             3))\n",
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InceptionResNetV2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [""
     ]
    },
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "import keras\n",
    "\n",
    "conv_base = InceptionResNetV2(weights='imagenet',\n",
    "                include_top=False,\n",
    "                input_shape=(h_params['image_size'],\n",
    "                             h_params['image_size'],\n",
    "                             3))\n",
    "len(conv_base.layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ""
     ]
    },
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications.xception import Xception\n",
    "conv_base = Xception(weights='imagenet',\n",
    "                include_top=False,\n",
    "                input_shape=(h_params['image_size'],\n",
    "                             h_params['image_size'],\n",
    "                             3))\n",
    "len(conv_base.layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ""
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ""]
    },
    {
     "data": {
      "text/plain": [
       "175"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "conv_base = ResNet50(weights='imagenet',\n",
    "                include_top=False,\n",
    "                input_shape=(h_params['image_size'],\n",
    "                             h_params['image_size'],\n",
    "                             3))\n",
    "len(conv_base.layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1840 images belonging to 5 classes.\n",
      "Found 905 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "#base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'\n",
    "train_dir = input_data_dir['train']\n",
    "validation_dir = input_data_dir['validation']\n",
    "test_dir = input_data_dir['test']\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count, 5))\n",
    "    generator = datagen.flow_from_directory(directory,\n",
    "                                            target_size=(h_params['image_size'],\n",
    "                                                         h_params['image_size']),\n",
    "                                            batch_size=batch_size,\n",
    "                                            class_mode='categorical')\n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        #print(labels.shape)\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            break\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = extract_features(train_dir,\n",
    "                                                dfs['train']['count'].sum())\n",
    "validation_features, validation_labels = extract_features(validation_dir,\n",
    "                                                          dfs['validation']['count'].sum())\n",
    "#test_features, test_labels = extract_features(test_dir,\n",
    "#                                                dfs['test']['count'].sum())\n",
    "\n",
    "\n",
    "train_features = np.reshape(train_features, (dfs['train']['count'].sum(), 4 * 4 * 512))\n",
    "validation_features = np.reshape(validation_features, (dfs['validation']['count'].sum(), 4 * 4 * 512))\n",
    "#test_features = np.reshape(test_features, (dfs['test']['count'].sum(), 4 * 4 * 512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1840, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1840, 8192)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1840 samples, validate on 905 samples\n",
      "Epoch 1/40\n",
      "1840/1840 [==============================] - 1s 533us/step - loss: 1.5727 - acc: 0.2734 - val_loss: 1.3438 - val_acc: 0.5105\n",
      "Epoch 2/40\n",
      "1840/1840 [==============================] - 1s 432us/step - loss: 1.3456 - acc: 0.3984 - val_loss: 1.0759 - val_acc: 0.6110\n",
      "Epoch 3/40\n",
      "1840/1840 [==============================] - 1s 406us/step - loss: 1.2249 - acc: 0.4641 - val_loss: 0.9920 - val_acc: 0.6917\n",
      "Epoch 4/40\n",
      "1840/1840 [==============================] - 1s 402us/step - loss: 1.1197 - acc: 0.5141 - val_loss: 0.8216 - val_acc: 0.7669\n",
      "Epoch 5/40\n",
      "1840/1840 [==============================] - 1s 401us/step - loss: 1.0214 - acc: 0.5696 - val_loss: 0.8113 - val_acc: 0.7348\n",
      "Epoch 6/40\n",
      "1840/1840 [==============================] - 1s 399us/step - loss: 1.0048 - acc: 0.5592 - val_loss: 0.6994 - val_acc: 0.7624\n",
      "Epoch 7/40\n",
      "1840/1840 [==============================] - 1s 416us/step - loss: 0.9958 - acc: 0.5793 - val_loss: 0.6686 - val_acc: 0.7757\n",
      "Epoch 8/40\n",
      "1840/1840 [==============================] - 1s 398us/step - loss: 0.9547 - acc: 0.5734 - val_loss: 0.6659 - val_acc: 0.7912\n",
      "Epoch 9/40\n",
      "1840/1840 [==============================] - 1s 399us/step - loss: 0.9232 - acc: 0.6071 - val_loss: 0.6840 - val_acc: 0.7779\n",
      "Epoch 10/40\n",
      "1840/1840 [==============================] - 1s 397us/step - loss: 0.8786 - acc: 0.6255 - val_loss: 0.6062 - val_acc: 0.7989\n",
      "Epoch 11/40\n",
      "1840/1840 [==============================] - 1s 403us/step - loss: 0.8887 - acc: 0.6190 - val_loss: 0.6340 - val_acc: 0.7746\n",
      "Epoch 12/40\n",
      "1840/1840 [==============================] - 1s 401us/step - loss: 0.8369 - acc: 0.6522 - val_loss: 0.5484 - val_acc: 0.8144\n",
      "Epoch 13/40\n",
      "1840/1840 [==============================] - 1s 406us/step - loss: 0.8303 - acc: 0.6571 - val_loss: 0.5675 - val_acc: 0.8144\n",
      "Epoch 14/40\n",
      "1840/1840 [==============================] - 1s 405us/step - loss: 0.8111 - acc: 0.6630 - val_loss: 0.6680 - val_acc: 0.7757\n",
      "Epoch 15/40\n",
      "1840/1840 [==============================] - 1s 413us/step - loss: 0.8015 - acc: 0.6609 - val_loss: 0.5749 - val_acc: 0.8276\n",
      "Epoch 16/40\n",
      "1840/1840 [==============================] - 1s 416us/step - loss: 0.7912 - acc: 0.6533 - val_loss: 0.5605 - val_acc: 0.8199\n",
      "Epoch 17/40\n",
      "1840/1840 [==============================] - 1s 401us/step - loss: 0.7569 - acc: 0.6783 - val_loss: 0.6143 - val_acc: 0.8110\n",
      "Epoch 18/40\n",
      "1840/1840 [==============================] - 1s 404us/step - loss: 0.7923 - acc: 0.6696 - val_loss: 0.6839 - val_acc: 0.7790\n",
      "Epoch 19/40\n",
      "1840/1840 [==============================] - 1s 398us/step - loss: 0.7850 - acc: 0.6554 - val_loss: 0.6943 - val_acc: 0.7912\n",
      "Epoch 20/40\n",
      "1840/1840 [==============================] - 1s 423us/step - loss: 0.7815 - acc: 0.6793 - val_loss: 0.6340 - val_acc: 0.7890\n",
      "Epoch 21/40\n",
      "1840/1840 [==============================] - 1s 422us/step - loss: 0.7895 - acc: 0.6652 - val_loss: 0.5905 - val_acc: 0.8022\n",
      "Epoch 22/40\n",
      "1840/1840 [==============================] - 1s 402us/step - loss: 0.8114 - acc: 0.6592 - val_loss: 0.6745 - val_acc: 0.8122\n",
      "Epoch 23/40\n",
      "1840/1840 [==============================] - 1s 395us/step - loss: 0.7371 - acc: 0.6853 - val_loss: 0.6214 - val_acc: 0.8199\n",
      "Epoch 24/40\n",
      "1840/1840 [==============================] - 1s 396us/step - loss: 0.7402 - acc: 0.6973 - val_loss: 0.6014 - val_acc: 0.8122\n",
      "Epoch 25/40\n",
      "1840/1840 [==============================] - 1s 396us/step - loss: 0.7283 - acc: 0.6859 - val_loss: 0.6057 - val_acc: 0.8110\n",
      "Epoch 26/40\n",
      "1840/1840 [==============================] - 1s 398us/step - loss: 0.7793 - acc: 0.6625 - val_loss: 0.6150 - val_acc: 0.8188\n",
      "Epoch 27/40\n",
      "1840/1840 [==============================] - 1s 404us/step - loss: 0.7802 - acc: 0.6761 - val_loss: 0.6733 - val_acc: 0.8055\n",
      "Epoch 28/40\n",
      "1840/1840 [==============================] - 1s 408us/step - loss: 0.7442 - acc: 0.6870 - val_loss: 0.9024 - val_acc: 0.7315\n",
      "Epoch 29/40\n",
      "1840/1840 [==============================] - 1s 392us/step - loss: 0.7885 - acc: 0.6837 - val_loss: 0.6926 - val_acc: 0.7823\n",
      "Epoch 30/40\n",
      "1840/1840 [==============================] - 1s 396us/step - loss: 0.7654 - acc: 0.6815 - val_loss: 0.6589 - val_acc: 0.8077\n",
      "Epoch 31/40\n",
      "1840/1840 [==============================] - 1s 411us/step - loss: 0.7304 - acc: 0.6913 - val_loss: 0.6804 - val_acc: 0.7912\n",
      "Epoch 32/40\n",
      "1840/1840 [==============================] - 1s 441us/step - loss: 0.7356 - acc: 0.6978 - val_loss: 0.7796 - val_acc: 0.7724\n",
      "Epoch 33/40\n",
      "1840/1840 [==============================] - 1s 408us/step - loss: 0.7343 - acc: 0.6880 - val_loss: 0.7566 - val_acc: 0.7823\n",
      "Epoch 34/40\n",
      "1840/1840 [==============================] - 1s 395us/step - loss: 0.7705 - acc: 0.6723 - val_loss: 0.7424 - val_acc: 0.7890\n",
      "Epoch 35/40\n",
      "1840/1840 [==============================] - 1s 394us/step - loss: 0.7679 - acc: 0.6788 - val_loss: 0.9837 - val_acc: 0.7901\n",
      "Epoch 36/40\n",
      "1840/1840 [==============================] - 1s 392us/step - loss: 0.7520 - acc: 0.6995 - val_loss: 0.7521 - val_acc: 0.8022\n",
      "Epoch 37/40\n",
      "1840/1840 [==============================] - 1s 393us/step - loss: 0.7834 - acc: 0.6712 - val_loss: 0.7561 - val_acc: 0.8000\n",
      "Epoch 38/40\n",
      "1840/1840 [==============================] - 1s 393us/step - loss: 0.7722 - acc: 0.6696 - val_loss: 0.8445 - val_acc: 0.7978\n",
      "Epoch 39/40\n",
      "1840/1840 [==============================] - 1s 394us/step - loss: 0.7364 - acc: 0.6837 - val_loss: 0.7729 - val_acc: 0.8088\n",
      "Epoch 40/40\n",
      "1840/1840 [==============================] - 1s 398us/step - loss: 0.7340 - acc: 0.6967 - val_loss: 0.7786 - val_acc: 0.7967\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "from keras import optimizers\n",
    "import time\n",
    "\n",
    "startt = time.time()\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(64, activation='relu', input_dim=4 * 4 * 512))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(class_count, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer=h_params['optimizer'],\n",
    "                loss='categorical_crossentropy',\n",
    "                metrics=h_params['metrics'])\n",
    "history = model.fit(train_features, train_labels,\n",
    "                    epochs=h_params['epochs'],\n",
    "                    batch_size=h_params['batch_size'],\n",
    "                    validation_data=(validation_features, validation_labels))\n",
    "\n",
    "# save model\n",
    "import helper as hlp\n",
    "model_h = {}\n",
    "model_h['history'] = history\n",
    "model_h['train_duration'] = round(time.time() - startt)\n",
    "model_h['hyper_parameters'] = h_params\n",
    "model_h['hyper_parameters']['optimizer'] = str(h_params['optimizer'])\n",
    "filename = str(round(time.time()))\n",
    "filename = '_'.join([filename[:5], filename[5:6], filename[6:7], filename[7:8], filename[8:], 'pretrained_vgg16_extract_feature'])\n",
    "hlp.save(model_h, 'models/'+filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model file name is: 15781_8_5_3_93_pretrained_vgg16_extract_feature\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show result with plot\n",
    "print('model file name is:', filename)\n",
    "hlp.show_model_plot(model_h);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}