29-Transfer_learning_Fine_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://akhavanpour.ir/notebook/images/srttu.gif\" alt=\"SRTTU\" style=\"width: 150px;\"/>\n",
    "\n",
    "[![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://notebooks.azure.com/import/gh/Alireza-Akhavan/class.vision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning - Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "در این نوت بوک Fine Tuning بررسی خواهد شد.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                  include_top=False,\n",
    "                  input_shape=(150, 150, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what our model looks like now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 256)               2097408   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 16,812,353\n",
      "Trainable params: 16,812,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 30\n"
     ]
    }
   ],
   "source": [
    "print('This is the number of trainable weights '\n",
    "      'before freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after freezing the conv base: 4\n"
     ]
    }
   ],
   "source": [
    "print('This is the number of trainable weights '\n",
    "      'after freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/30\n",
      "74s - loss: 0.4465 - acc: 0.7810 - val_loss: 0.2056 - val_acc: 0.9120\n",
      "Epoch 2/30\n",
      "72s - loss: 0.2738 - acc: 0.8905 - val_loss: 0.1239 - val_acc: 0.9550\n",
      "Epoch 3/30\n",
      "72s - loss: 0.2088 - acc: 0.9145 - val_loss: 0.1194 - val_acc: 0.9560\n",
      "Epoch 4/30\n",
      "72s - loss: 0.1835 - acc: 0.9280 - val_loss: 0.1025 - val_acc: 0.9550\n",
      "Epoch 5/30\n",
      "72s - loss: 0.1642 - acc: 0.9330 - val_loss: 0.0903 - val_acc: 0.9680\n",
      "Epoch 6/30\n",
      "72s - loss: 0.1360 - acc: 0.9410 - val_loss: 0.0794 - val_acc: 0.9740\n",
      "Epoch 7/30\n",
      "72s - loss: 0.1426 - acc: 0.9465 - val_loss: 0.0968 - val_acc: 0.9560\n",
      "Epoch 8/30\n",
      "72s - loss: 0.1013 - acc: 0.9580 - val_loss: 0.1411 - val_acc: 0.9430\n",
      "Epoch 9/30\n",
      "72s - loss: 0.1177 - acc: 0.9500 - val_loss: 0.2105 - val_acc: 0.9310\n",
      "Epoch 10/30\n",
      "72s - loss: 0.0949 - acc: 0.9620 - val_loss: 0.0900 - val_acc: 0.9710\n",
      "Epoch 11/30\n",
      "72s - loss: 0.0915 - acc: 0.9655 - val_loss: 0.1204 - val_acc: 0.9630\n",
      "Epoch 12/30\n",
      "72s - loss: 0.0782 - acc: 0.9645 - val_loss: 0.0995 - val_acc: 0.9650\n",
      "Epoch 13/30\n",
      "72s - loss: 0.0717 - acc: 0.9755 - val_loss: 0.1269 - val_acc: 0.9580\n",
      "Epoch 14/30\n",
      "72s - loss: 0.0670 - acc: 0.9715 - val_loss: 0.0994 - val_acc: 0.9680\n",
      "Epoch 15/30\n",
      "71s - loss: 0.0718 - acc: 0.9735 - val_loss: 0.0558 - val_acc: 0.9790\n",
      "Epoch 16/30\n",
      "72s - loss: 0.0612 - acc: 0.9780 - val_loss: 0.0870 - val_acc: 0.9690\n",
      "Epoch 17/30\n",
      "71s - loss: 0.0693 - acc: 0.9765 - val_loss: 0.0972 - val_acc: 0.9720\n",
      "Epoch 18/30\n",
      "71s - loss: 0.0596 - acc: 0.9785 - val_loss: 0.0832 - val_acc: 0.9730\n",
      "Epoch 19/30\n",
      "71s - loss: 0.0497 - acc: 0.9800 - val_loss: 0.1160 - val_acc: 0.9610\n",
      "Epoch 20/30\n",
      "71s - loss: 0.0546 - acc: 0.9780 - val_loss: 0.1057 - val_acc: 0.9660\n",
      "Epoch 21/30\n",
      "71s - loss: 0.0568 - acc: 0.9825 - val_loss: 0.2012 - val_acc: 0.9500\n",
      "Epoch 22/30\n",
      "71s - loss: 0.0493 - acc: 0.9830 - val_loss: 0.1384 - val_acc: 0.9610\n",
      "Epoch 23/30\n",
      "71s - loss: 0.0328 - acc: 0.9905 - val_loss: 0.1281 - val_acc: 0.9640\n",
      "Epoch 24/30\n",
      "71s - loss: 0.0524 - acc: 0.9860 - val_loss: 0.0846 - val_acc: 0.9760\n",
      "Epoch 25/30\n",
      "71s - loss: 0.0422 - acc: 0.9845 - val_loss: 0.1002 - val_acc: 0.9670\n",
      "Epoch 26/30\n",
      "71s - loss: 0.0617 - acc: 0.9825 - val_loss: 0.0858 - val_acc: 0.9760\n",
      "Epoch 27/30\n",
      "71s - loss: 0.0568 - acc: 0.9830 - val_loss: 0.0889 - val_acc: 0.9700\n",
      "Epoch 28/30\n",
      "71s - loss: 0.0296 - acc: 0.9915 - val_loss: 0.1406 - val_acc: 0.9620\n",
      "Epoch 29/30\n",
      "71s - loss: 0.0432 - acc: 0.9890 - val_loss: 0.1535 - val_acc: 0.9650\n",
      "Epoch 30/30\n",
      "71s - loss: 0.0354 - acc: 0.9885 - val_loss: 0.1832 - val_acc: 0.9510\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=40,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      shear_range=0.2,\n",
    "      zoom_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # This is the target directory\n",
    "        train_dir,\n",
    "        # All images will be resized to 150x150\n",
    "        target_size=(150, 150),\n",
    "        batch_size=20,\n",
    "        # Since we use binary_crossentropy loss, we need binary labels\n",
    "        class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "              metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=30,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50,\n",
    "      verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_3.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our results again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1a12dfm30z6/NcXKMMaamkrL3TsmjEWvrpqzsbAvyxpjEkJQl/dxc1+XwmGNi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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd55306e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4YyU72/WvHTIkvM+lp7ugsGFDbMpl4mfnTtfI56UxtaTRo92VqnYTj9AOHYKf\n/cy1Ua1YAX/4g7vDVdn2tkTyVXD381AE4V71qurGjR48GBo2DG9dNgyBfwQHCwun5g7uHqvp6Zaa\nKaugAJ580t1U/tFHYexYd3ez8ePjNyCYV74K7p06wRln+C+4V+Wq19Wr3fjt4aZkwDUCN25swd0P\nli93jeTBXlDhGD3a9UDbvj365UpFb78N55/v/ve++U1YtcoF+pYtE12y0HwV3EVc7f3f/3aNmX5R\nlates7Ndv/Yrrwx/fSKun64F99S3YoXrbODlGoeyghfgVPebeOzc6b6Liy929xmeO9cF+l69El2y\nivkquIML7gcOwHvvJbok3nhJt4R71asqvPACXHKJG+2vKtLTYf16fx0kq5vDh127SbgpmaA2baBf\nv+p7Ew9V+N3v3P0PXn4Z7rvPNaCOHFnxGE3JwnfBfdAg95gKqRmv6ZZwr3pdvNjlASPpgpmeDl9+\naafkqey//3W/q6oGd3C/oU2bXHqmulm2DCZOdO1Wmze7fuwNGiS6VN75Lri3bOkCUzL0d6+M13RL\nOFe9qsLkye4sIJL+tt27u0dLzaSuFStcOqZPn6ovY8QI1/7Srx9cf3316kE1Y4Y7850717VDpRrf\nBXdwqZkVK04OnMnGa7olnKteFy92DT333hv6pgxede7sUkUW3FPX8uXuIB1ub6mSmjd3AX3cONeO\n062buyhu2TJ/p2p27nT3HP7hDxM/jEBV+TK4Dx7s7lu4bFmiS1KxcNItXq56LVlrv+GGyMpWr567\nhNqCe2o6ccKN4R5u//ZQWrd2dxPauRMeeMBVHvr3h299ywVAP7bL/PnP7vFHP0psOSLhy+B+0UXu\nitVkT81Ee5CxYK190qTIau1BNgxB6lq3zg1gFUm+vaxmzdwZ4Y4dMHOmu3p1+HDXBfmvf/XPGPDH\njrkujt/9buJGc40GXwb3evVcjjDZG1WjOchYNGvtQenp7h/50KHoLM/ET7iDhYWjXj1Xo/3oI5g3\nz1VIbrnF3fz5ySejv754mzMHPv/cpaJSmS+DO7jUzAcfwJ49iS5JxaI1yNirrxbX2qN1pVzwStX1\n66OzvEj8/veuX/FnnyW6JKlhxQpo395d1BcrtWq5MVXWrHEVqfbtXW+vZPi9VJWqGx+ma1fXrz2V\n+Ta4B4cieOgh1+f9xInElieWYlFrh+QZhuCll+AXv4C1a+Gaa/y9L6NB1QX3WNTaQxFxXZAXLnTD\nS0+eHJ+UTh41AAASr0lEQVT1xsLbb7vf+7hxqdGXvSK+De7durmj7x//CD17ukuwL7gA7rwTnnkG\nNm5040T4wauvwrvvhl9rr+wCqtNPhxYtEhvcP/zQdcHr3Rueeso1ko8fn7jypILt2+HTT6PTmBqO\npk3dvsnOdgfiVDRjhtuO0aMTXZIoUNWE/PXq1UtjrbBQdds21XnzVO++W7V/f9WGDVVd3Ua1QQPV\niy5S/elPVRcscPOnmsJC1d69Vdu1U/36a++fmz1btX794u8C3OvZs0vPN3CgakZGeGWaN0916FDV\nPXvC+1xZBw+qnnOOasuWqrm5btrPfubK+tRTkS3bz555xn1HGzbEf92HDqk2bar6ne/Ef92R2rFD\ntWZN1V/8ItElqRiwWj3EWF8H91AKClQ3bVJ99lnVceNUv/Ut1Xr13DcxapTqkSMJKVaVLVrkyj5r\nVnifa9u2dGAP/rVtW3q+8eNV69ZVPXHC23I3biz+Ps85RzUnJ7xyBeXnq15+uWpamury5cXTT5xQ\nHTRItXZt1Xfeqdqy/e6HP1Rt0sT91hNh6lS3/1euTMz6q2rCBNUaNar+m40XC+5hOHHC/SBr1FDt\n0CExNZ6qCNba27YNr9auqioSOriLlJ4vWAv88MPKl5mXp9q1q+qpp6q++KILMK1bq27ZEl7ZVN0/\nGqg+8cTJ7+3fr9q+veoZZ0R+dpAK5s9XHTZM9dVXvZ1dduqkesUVsS9Xeb74QrV5c9UhQxJXhnDl\n5bkyDx+e6JJUzoJ7FbzxhksB1Kvnglqyq2qtXdV7zX3dOjd97tzKl3nHHW7eRYvc6/fec4H+G99w\ny/Hquefccm67rfx51q1zaaRvfSv8A1sq+fhjl0qsUcN9J927q86ZU/6Z1IEDbr6pU+NazJM89JAr\nx4oViS2HV3/9qyvv0qWJLknlLLhX0e7dLjcPqrfc4o7oySiSWruq95z711+71MiECRUvLzvbLWP8\n+NLTN29WbdXK1eL/+9/KyxUM2n37Vr5dwYPArbdWvtxUVFCgOmCAC+5btrgA1KGD2+b27VX/9CfV\nL78s/ZmFC937b72VmDIHHT3qDuoDBya2HF4UFrqDZpcuqdHuZsE9AidOqE6c6L6d9HTVjz5KdIlO\nFkmtPWj2bHdwEHGPZQN7ULduLv9dnp07XSNar16hA3JOjsu/N2ig+vrr5S9n/37XMHzmmd7TLRWl\nb1Ldn/508j4uKFB96SXVCy9077VooXr//e67U3XfR1paclRKHnnElfHNNxNdkootWxb5/1I8WXCP\ngldeUW3WTLVRI9Xnn090aYpFWmsP1/XXu/x2KCdOqPbr52qXFR0E9+xxNaM6dVzPpFDLGTjQvf/u\nu97Llp/vcrtpaamTAvBi61Z3BjNkSOjaZGGhC0pDhxafdd11l2qPHqp9+sS/vKHk5amefrrqxRcn\nd434mmtc5aTsWVCyimpwB4YAW4BtwIQQ718MrAXygRFelpkKwV3V1Tp793bf1F13JUd+d/Hiimsa\nXmvkXk2b5tb32Wcnvzd5snvv2WcrX86BA6rnn++6m82ZU/q9n/7ULacqbR2ff6569tmqp51W3GUy\nleXnuy66jRurfvJJ5fNv2OAOwLVqacjUWCI99pgr07//neiShLZzp/s9/vzniS6Jd1EL7kBN4H/A\nWUBt4H2gU5l52gHdgGf9FtxVXUAfN859W336uL7ziVJY6MrQpk3oA43XXHo4XnvNLee110pPf+st\n19B3/fXel/XFF65NQ6Q4lfLss8UHz6r64AOX9undW/XYsaovJxkE0xl/+1t4n9uxQ/W3v3WPyeLY\nMdfm8q1vJWftfeJE9xv++ONEl8S7aAb3C4ElJV7/EvhlOfP+zY/BPegf/3ApmrQ0V9M8cCD+ZQjW\n2svLMXvtBROOzz5zy5g2rXja/v3un/acc1zADkdenuuqB6o//rHrRz9ggOrx41Uvo6rqCy+4Zd58\nc3IGEi82b3bfx5VXpu42lPX4426/vPpq+J89eNBdlxILwe6P3/1ubJYfK9EM7iOAv5R4fT3wp3Lm\n9XVwV3Wn/Tff7GqeTZq4gPfVV/FZd2W1dlXv/dfD1aSJqxmLuPX36uUOcqtXV215X3/tcp3BA0+o\nlE9V3Htv1Wq9yeDECbd/mzXzV//9r792+/j888M7YL36qsvZi7hKQLQvMHzqKfdbeeON6C431pIy\nuAOZwGpgdZs2beLxPcTM+vWusQtcD485c2J/ReDcuRXX2lVjU3OfPbu4n3XJv9Gjq75MVZdbfuwx\nV1uNloICl5pp0yZ+B92gDz5Qvekm1csuq9rVsw8+6L7Xsu0RfvCXv7htW7iw8nnz8orToJ07q2Zm\nFrcfLVkSnfIUFrrG51Tp/liSpWXi5F//ct0lwdVMYtHtq6BA9YEH3A+8R4+KG3VjkXMv74AR6vgc\n7cbcqliyxJXvz3+O/bqCvVa+853i77ply+LapteU1YYNbkiF730v9YKNF8ePq551lvv9VrR9a9e6\nK2xB9Sc/KW4/Wb68uI//2LGRp0TffrvyilKyimZwrwVsB9qXaFDtXM681S64q7oa6N/+5nLQoHrV\nVdHLEx48WBw4Ro92F4dUJtoB1muqJxYHlqooLHQXQZ15ZuwaV/PzXY7/ggu0qL/5lCmuLeLwYdU7\n73TfT6tWqi+/XPGyjh9X7dnTXc0brfRUMgoOZfHiiye/l5/vrmpNS3Pdbv/1r5PnOXZMddIk17ul\nZcvIuidfe61LNXr5f0o20e4KeQXwUaDXzKTAtCnAVYHn5wO5wJfAAWBjZcv0U3APystzvRUaNXI/\nwFtvjaznwvvvuy5+tWq59EWianReUz2xSAlV1euvu3XPmBHd5R475mp7557rln/WWe4MIVQf6Xfe\ncWPtgOrVV5ffTfP++908yXQtRSycOKH6zW+676RkCjMnp/iq8BEjii/IKs9777kzgOD3unt3eOX4\n5BP3/3n33WFvQlKwi5gSaO9eN85KWpr7y8wMv6vV7NlujJvTT0/8xTnBslRWI49VY25VFBa6gHHa\nadG5WvPzz914LS1bum3KyHADeuXnV/y548dVf/c71wPmlFNUZ84sHdjee88dvEeNiryMqSAry31/\n8+e717Nnu++lUSNXs/dagTlxwrVR1KnjauBPP33yZ7/6yp1BL1yo+uij7n/y2992Z3Qiqtu3R3XT\n4saCexLYsUP1Rz9yudRatVwvm61bK/7M11+7XC24K/uSpdfE7Nkux15RqieZau6qrv0DXL/xSOzY\n4dIu4IZhWLo0/LOobdvcUMXghg7YsMHt627d3AEoEd1qEyE/3+XUO3ZUHTnSfR99+1Y90G7e7K6Q\nBneF8623usdgWrLk77BxY3dQHjkytRutLbgnkdxc1/pft27xRT+heojs2uUu9gheZRhpv+94S5ac\ne0kDB7oBrKqaW83PdwfZhg1VV62KrCyFhe6CrebN3cH+oovUcw8SP5k/3213rVqqv/mN93sFlKeg\nwKXGGjd2322fPqpjxqj++tfut/fOOy7V45eGagvuSWjPHncnofr1Xa1i5EjXfU7VXe3ZsqXrS/7c\nc4ktZyTCacyNR8+a5cvdr/zhh6v2+d/+VqPeb37fPtUbbtCinh/VTUGB6h/+EPnBsiy/BO/KWHBP\nYnv3qt5zjwvkwdPJmjVVzzvP3cmoOohnLf/b33Y1unCvpF21ytUur7kmNoFjy5bUOzsziec1uIub\nN/4yMjJ09erVCVl3sjhwAKZPdzflvewydwPoU05JdKnio1072LHj5Olt20JOTnTXtXKluzn6b38L\nv/ylt898+aW7sXpenrtBeLNm0S2TMVUlImtUNaOy+WrEozAmtObN4YEH4PPP4fnnq09gB9i50/v0\nrCx3MKhRwz1mZYW3rj59YOhQ+P3v4YsvvC1z/HjYuhWefdYCu0lNFtyTQM2aiS5B/LVp4216VhZk\nZrpavqp7zMwMP8Dffz8cPAh//GPly3z5ZZg1C+6+GwYM8L6OSA9CxkSVl9xNLP6qc87deM+5h9u9\nsqJG2mHDim/aXd4yd+923R579AhvbJpk7Clk/AlrUDXJzktvmXAujKoswL73Xuhllfz79rddl9UP\nPwxvW5Ktj7/xL6/B3RpUTVILp+HVy7wjRsCLL7rQW1bTpi51M3Mm/OhH4ZWzRo3QyxSBwsLwlmVM\nRaxB1fjC1KlQv37pafXru+lleWmknTzZPdaqVXqeunXh6FHX8Hr77aXf85JL99qGYGLD2jtC8FK9\nj8WfpWWMV14vdvKaGvn+913qpVWr4puPtG7trmTdu/fkdXvJpYebc0+GoZH9orq1d2A5d1PdeP0n\n37jRBdV77nGvgzfnfuWVk5cZTi7da8BOdDDy24ElFvsomVlwN9WS13/e0aOLAyq4EQNDicVIl7Fq\nfPWy7Yk+sMRCqt1vIFIW3I2pwObNxbcO7Nix/GGBYxGIY3HAiFXX0liIdu05Fe83EAkL7sZUYuxY\nNxzze++VP088b1sYSRrB6zITPeZ+LL5Pr8tM9LZHiwV3Yypx/Li3O2VFu6YZi0Zar4ErmheFVUUi\nU1JWc7fgbkzMRTsYeZ03nANGLGrZiaw9W87dgrsxSSGaV+eWnTeaqZ5wJLr2HIveMvHugWPB3ZgU\nl+gUSiIbfoPzJnu3xUScDVhwNybFJTqNYF02K5eIMxGvwd3GljEmiWVlwaRJbgiFNm3csAtjxsRv\n3ZmZ7oYlQfXru+GQY12GeN7MJRKJGFPIxpYxxgfGjHHBrLDQPcYrsAfXPWuWC6gi7jEegR3Cu5lL\nIoU7plA8x8Cx4G6MKZfXg0u0g1aqDMQWzsB20brxjFcW3I0xEYlF0AonaCZSOGc3kyaVTnGBez1p\nUmzKZsHdGBORWAStRKaEgryejXg9u4l3qskaVI0xEfHjjUpi0ZgcrUZia1A1xsRFquTHwxGLs5F4\np5osuBtjIpIq+fFwxCKFEu9UkwV3Y0xEkiE/Hm2xOhuJZ9dWC+7GmIglsj9+LPjhbMSCuzHGlOGH\ns5Falc9ijDHVz5gxqRXMy7KauzHG+JAFd2OM8SEL7sYY40MW3I0xxocsuBtjjA8lbGwZEdkHhBhp\nwZMWwP4oFicZ+G2b/LY94L9t8tv2gP+2KdT2tFXVUyv7YMKCeyREZLWXgXNSid+2yW/bA/7bJr9t\nD/hvmyLZHkvLGGOMD1lwN8YYH0rV4D4r0QWIAb9tk9+2B/y3TX7bHvDfNlV5e1Iy526MMaZiqVpz\nN8YYUwEL7sYY40MpF9xFZIiIbBGRbSIyIdHliZSI5IjIBhFZJyIpeVNZEXlKRD4TkQ9KTGsmIq+J\nyNbAY9NEljEc5WzPZBHZFdhP60TkikSWMVwi0lpElorIhyKyUUTuCkxPyf1Uwfak7H4Skboi8q6I\nvB/YpvsD09uLyMpAzHtORGp7Wl4q5dxFpCbwEXAZkAusAkap6ocJLVgERCQHyFDVlL3wQkQuBo4C\nz6pql8C0h4HPVfXBwEG4qarek8hyelXO9kwGjqrqtESWrapE5HTgdFVdKyKNgDXAd4GxpOB+qmB7\nriVF95OICNBAVY+KSBqwHLgLGA+8qKrzROT/gPdV9fHKlpdqNffewDZV3a6qx4F5wLAEl6naU9Vl\nwOdlJg8Dngk8fwb3j5cSytmelKaqe1R1beD5EWATcCYpup8q2J6Upc7RwMu0wJ8ClwLPB6Z73kep\nFtzPBD4p8TqXFN+huJ33LxFZIyKZiS5MFLVU1T2B558CLRNZmCi5U0TWB9I2KZG+CEVE2gE9gJX4\nYD+V2R5I4f0kIjVFZB3wGfAa8D/gkKrmB2bxHPNSLbj70UWq2hO4HLgjkBLwFXW5v9TJ/4X2OHA2\n0B3YA/whscWpGhFpCLwA/ERVvyj5XirupxDbk9L7SVULVLU70AqXqehQ1WWlWnDfBbQu8bpVYFrK\nUtVdgcfPgGzcDvWDvYG8aDA/+lmCyxMRVd0b+McrBJ4kBfdTII/7ApClqi8GJqfsfgq1PX7YTwCq\neghYClwINBGR4C1RPce8VAvuq4BzA63HtYGRwIIEl6nKRKRBoDEIEWkADAY+qPhTKWMBcGPg+Y3A\nywksS8SCATBgOCm2nwKNdX8FNqnqIyXeSsn9VN72pPJ+EpFTRaRJ4Hk9XMeRTbggPyIwm+d9lFK9\nZQACXZumAzWBp1R1aoKLVGUichautg7uZuVzUnF7RGQucAlueNK9wK+Bl4D5QBvc0M7XqmpKNFKW\nsz2X4E71FcgBbi2Rq056InIR8DawASgMTJ6Iy1On3H6qYHtGkaL7SUS64RpMa+Iq3vNVdUogTswD\nmgHvAdep6teVLi/VgrsxxpjKpVpaxhhjjAcW3I0xxocsuBtjjA9ZcDfGGB+y4G6MMT5kwd0YY3zI\ngrsxxvjQ/wPcz2v1dLH/YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd544300160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we reach a validation accuracy of about 96%. This is much better than our small convnet trained from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning\n",
    "\n",
    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
    "Fine-tuning consists in unfreezing a few of the top layers \n",
    "of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in our case, the \n",
    "fully-connected classifier) and these top layers. This is called \"fine-tuning\" because it slightly adjusts the more abstract \n",
    "representations of the model being reused, in order to make them more relevant for the problem at hand.\n",
    "\n",
    "![fine-tuning VGG16](https://s3.amazonaws.com/book.keras.io/img/ch5/vgg16_fine_tuning.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have stated before that it was necessary to freeze the convolution base of VGG16 in order to be able to train a randomly initialized \n",
    "classifier on top. For the same reason, it is only possible to fine-tune the top layers of the convolutional base once the classifier on \n",
    "top has already been trained. If the classified wasn't already trained, then the error signal propagating through the network during \n",
    "training would be too large, and the representations previously learned by the layers being fine-tuned would be destroyed. Thus the steps \n",
    "for fine-tuning a network are as follow:\n",
    "\n",
    "* 1) Add your custom network on top of an already trained base network.\n",
    "* 2) Freeze the base network.\n",
    "* 3) Train the part you added.\n",
    "* 4) Unfreeze some layers in the base network.\n",
    "* 5) Jointly train both these layers and the part you added.\n",
    "\n",
    "We have already completed the first 3 steps when doing feature extraction. Let's proceed with the 4th step: we will unfreeze our `conv_base`, \n",
    "and then freeze individual layers inside of it.\n",
    "\n",
    "As a reminder, this is what our convolutional base looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "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: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We will fine-tune the last 3 convolutional layers, which means that all layers up until `block4_pool` should be frozen, and the layers \n",
    "`block5_conv1`, `block5_conv2` and `block5_conv3` should be trainable.\n",
    "\n",
    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base? We could. However, we need to consider that:\n",
    "\n",
    "* Earlier layers in the convolutional base encode more generic, reusable features, while layers higher up encode more specialized features. It is \n",
    "more useful to fine-tune the more specialized features, as these are the ones that need to be repurposed on our new problem. There would \n",
    "be fast-decreasing returns in fine-tuning lower layers.\n",
    "* The more parameters we are training, the more we are at risk of overfitting. The convolutional base has 15M parameters, so it would be \n",
    "risky to attempt to train it on our small dataset.\n",
    "\n",
    "Thus, in our situation, it is a good strategy to only fine-tune the top 2 to 3 layers in the convolutional base.\n",
    "\n",
    "Let's set this up, starting from where we left off in the previous example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv_base.trainable = True\n",
    "\n",
    "set_trainable = False\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name == 'block5_conv1':\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start fine-tuning our network. We will do this with the RMSprop optimizer, using a very low learning rate. The reason for using \n",
    "a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the 3 layers that we are \n",
    "fine-tuning. Updates that are too large may harm these representations.\n",
    "\n",
    "Now let's proceed with fine-tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 32s - loss: 0.0215 - acc: 0.9935 - val_loss: 0.0980 - val_acc: 0.9720\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 32s - loss: 0.0131 - acc: 0.9960 - val_loss: 0.1247 - val_acc: 0.9700\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 32s - loss: 0.0140 - acc: 0.9940 - val_loss: 0.1044 - val_acc: 0.9790\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 33s - loss: 0.0102 - acc: 0.9965 - val_loss: 0.1259 - val_acc: 0.9770\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 33s - loss: 0.0137 - acc: 0.9945 - val_loss: 0.1036 - val_acc: 0.9800\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 33s - loss: 0.0183 - acc: 0.9935 - val_loss: 0.1260 - val_acc: 0.9750\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 33s - loss: 0.0141 - acc: 0.9945 - val_loss: 0.1575 - val_acc: 0.9690\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 33s - loss: 0.0094 - acc: 0.9965 - val_loss: 0.0935 - val_acc: 0.9780\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 33s - loss: 0.0079 - acc: 0.9985 - val_loss: 0.1452 - val_acc: 0.9760\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 33s - loss: 0.0127 - acc: 0.9970 - val_loss: 0.1027 - val_acc: 0.9790\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 33s - loss: 0.0097 - acc: 0.9965 - val_loss: 0.1463 - val_acc: 0.9720\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9980 - val_loss: 0.1361 - val_acc: 0.9720\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 33s - loss: 0.0274 - acc: 0.9955 - val_loss: 0.1446 - val_acc: 0.9740\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9985 - val_loss: 0.1123 - val_acc: 0.9790\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 33s - loss: 0.0057 - acc: 0.9975 - val_loss: 0.1912 - val_acc: 0.9700\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 33s - loss: 0.0144 - acc: 0.9960 - val_loss: 0.1415 - val_acc: 0.9780\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 33s - loss: 0.0048 - acc: 0.9990 - val_loss: 0.1231 - val_acc: 0.9780\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 33s - loss: 0.0188 - acc: 0.9965 - val_loss: 0.1551 - val_acc: 0.9720\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 33s - loss: 0.0160 - acc: 0.9970 - val_loss: 0.2155 - val_acc: 0.9740\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9965 - val_loss: 0.1559 - val_acc: 0.9730\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 33s - loss: 0.0132 - acc: 0.9980 - val_loss: 0.1518 - val_acc: 0.9740\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 33s - loss: 0.0086 - acc: 0.9965 - val_loss: 0.1517 - val_acc: 0.9790\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 33s - loss: 0.0070 - acc: 0.9980 - val_loss: 0.1887 - val_acc: 0.9670\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 33s - loss: 0.0044 - acc: 0.9985 - val_loss: 0.1818 - val_acc: 0.9740\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 33s - loss: 0.0159 - acc: 0.9970 - val_loss: 0.1860 - val_acc: 0.9680\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 33s - loss: 0.0056 - acc: 0.9980 - val_loss: 0.1657 - val_acc: 0.9740\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 33s - loss: 0.0118 - acc: 0.9980 - val_loss: 0.1542 - val_acc: 0.9760\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9990 - val_loss: 0.1493 - val_acc: 0.9770\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 33s - loss: 0.0114 - acc: 0.9965 - val_loss: 0.1921 - val_acc: 0.9680\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9990 - val_loss: 0.1188 - val_acc: 0.9830\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 33s - loss: 0.0068 - acc: 0.9985 - val_loss: 0.1814 - val_acc: 0.9740\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 33s - loss: 0.0096 - acc: 0.9985 - val_loss: 0.2034 - val_acc: 0.9760\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9985 - val_loss: 0.1970 - val_acc: 0.9730\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9990 - val_loss: 0.2349 - val_acc: 0.9680\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9990 - val_loss: 0.1865 - val_acc: 0.9740\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 33s - loss: 0.0115 - acc: 0.9975 - val_loss: 0.1933 - val_acc: 0.9750\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9980 - val_loss: 0.1779 - val_acc: 0.9780\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9975 - val_loss: 0.1887 - val_acc: 0.9700\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 33s - loss: 0.0093 - acc: 0.9980 - val_loss: 0.2159 - val_acc: 0.9720\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 33s - loss: 0.0049 - acc: 0.9990 - val_loss: 0.1412 - val_acc: 0.9790\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 33s - loss: 0.0052 - acc: 0.9985 - val_loss: 0.2066 - val_acc: 0.9690\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9990 - val_loss: 0.1860 - val_acc: 0.9770\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9985 - val_loss: 0.2361 - val_acc: 0.9680\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 33s - loss: 0.0012 - acc: 0.9995 - val_loss: 0.2440 - val_acc: 0.9680\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 33s - loss: 0.0035 - acc: 0.9985 - val_loss: 0.1428 - val_acc: 0.9820\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 33s - loss: 0.0111 - acc: 0.9970 - val_loss: 0.1822 - val_acc: 0.9720\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9990 - val_loss: 0.1726 - val_acc: 0.9720\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 33s - loss: 0.0039 - acc: 0.9995 - val_loss: 0.2164 - val_acc: 0.9730\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 33s - loss: 0.0060 - acc: 0.9970 - val_loss: 0.1856 - val_acc: 0.9810\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 33s - loss: 0.0126 - acc: 0.9980 - val_loss: 0.1824 - val_acc: 0.9720\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 33s - loss: 0.0155 - acc: 0.9965 - val_loss: 0.1867 - val_acc: 0.9710\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 33s - loss: 0.0059 - acc: 0.9985 - val_loss: 0.2287 - val_acc: 0.9700\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 33s - loss: 0.0046 - acc: 0.9980 - val_loss: 0.2337 - val_acc: 0.9650\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 33s - loss: 0.0087 - acc: 0.9970 - val_loss: 0.1168 - val_acc: 0.9820\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 33s - loss: 0.0046 - acc: 0.9985 - val_loss: 0.1496 - val_acc: 0.9790\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 33s - loss: 0.0067 - acc: 0.9985 - val_loss: 0.1615 - val_acc: 0.9750\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9975 - val_loss: 0.2520 - val_acc: 0.9630\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 33s - loss: 0.0017 - acc: 0.9990 - val_loss: 0.1899 - val_acc: 0.9740\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 33s - loss: 0.0022 - acc: 0.9990 - val_loss: 0.2321 - val_acc: 0.9680\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 33s - loss: 0.0091 - acc: 0.9975 - val_loss: 0.1416 - val_acc: 0.9790\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9985 - val_loss: 0.1749 - val_acc: 0.9720\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 33s - loss: 0.0028 - acc: 0.9995 - val_loss: 0.2065 - val_acc: 0.9740\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 33s - loss: 0.0058 - acc: 0.9985 - val_loss: 0.1749 - val_acc: 0.9750\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 33s - loss: 0.0076 - acc: 0.9980 - val_loss: 0.1542 - val_acc: 0.9760\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 33s - loss: 0.0081 - acc: 0.9980 - val_loss: 0.2627 - val_acc: 0.9660\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 33s - loss: 0.0107 - acc: 0.9980 - val_loss: 0.1748 - val_acc: 0.9740\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 33s - loss: 5.1450e-04 - acc: 1.0000 - val_loss: 0.1922 - val_acc: 0.9710\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9985 - val_loss: 0.2299 - val_acc: 0.9680\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 33s - loss: 0.0091 - acc: 0.9965 - val_loss: 0.1533 - val_acc: 0.9730\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 33s - loss: 0.0190 - acc: 0.9965 - val_loss: 0.2232 - val_acc: 0.9690\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 33s - loss: 0.0058 - acc: 0.9985 - val_loss: 0.1773 - val_acc: 0.9710\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9990 - val_loss: 0.1660 - val_acc: 0.9760\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 33s - loss: 0.0036 - acc: 0.9990 - val_loss: 0.2354 - val_acc: 0.9700\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 33s - loss: 0.0023 - acc: 0.9995 - val_loss: 0.1904 - val_acc: 0.9700\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 33s - loss: 0.0137 - acc: 0.9955 - val_loss: 0.1363 - val_acc: 0.9810\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9990 - val_loss: 0.2119 - val_acc: 0.9710\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 33s - loss: 9.2985e-04 - acc: 1.0000 - val_loss: 0.1777 - val_acc: 0.9760\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 33s - loss: 0.0032 - acc: 0.9990 - val_loss: 0.2568 - val_acc: 0.9710\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 33s - loss: 0.0112 - acc: 0.9970 - val_loss: 0.2066 - val_acc: 0.9720\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9975 - val_loss: 0.1793 - val_acc: 0.9700\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 33s - loss: 0.0045 - acc: 0.9980 - val_loss: 0.2082 - val_acc: 0.9690\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 33s - loss: 0.0095 - acc: 0.9985 - val_loss: 0.1985 - val_acc: 0.9700\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 33s - loss: 0.0032 - acc: 0.9985 - val_loss: 0.1992 - val_acc: 0.9710\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 33s - loss: 0.0019 - acc: 0.9995 - val_loss: 0.2285 - val_acc: 0.9690\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9970 - val_loss: 0.1833 - val_acc: 0.9760\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9990 - val_loss: 0.2054 - val_acc: 0.9690\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9980 - val_loss: 0.2084 - val_acc: 0.9730\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9980 - val_loss: 0.1840 - val_acc: 0.9690\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9980 - val_loss: 0.1953 - val_acc: 0.9700\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 33s - loss: 0.0092 - acc: 0.9990 - val_loss: 0.1996 - val_acc: 0.9740\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 33s - loss: 0.0081 - acc: 0.9965 - val_loss: 0.2136 - val_acc: 0.9750\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9985 - val_loss: 0.1775 - val_acc: 0.9790\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 33s - loss: 3.4598e-04 - acc: 1.0000 - val_loss: 0.2102 - val_acc: 0.9760\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 33s - loss: 0.0109 - acc: 0.9985 - val_loss: 0.2081 - val_acc: 0.9760\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 33s - loss: 0.0028 - acc: 0.9985 - val_loss: 0.2033 - val_acc: 0.9720\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 33s - loss: 0.0074 - acc: 0.9990 - val_loss: 0.2118 - val_acc: 0.9750\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 33s - loss: 0.0034 - acc: 0.9990 - val_loss: 0.2621 - val_acc: 0.9740\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9975 - val_loss: 0.2589 - val_acc: 0.9650\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 33s - loss: 0.0060 - acc: 0.9990 - val_loss: 0.2242 - val_acc: 0.9700\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 33s - loss: 0.0010 - acc: 0.9995 - val_loss: 0.2403 - val_acc: 0.9750\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-5),\n",
    "              metrics=['acc'])\n",
    "\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=100,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_4.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our results using the same plotting code as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bll/+2faDvn01f3IV8MaohdC7t/5Pawm5FgLAhz6kkWz5RGVVV+t3c3NruhZC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\nicg7IrJQRE5q6fdaRK4Nnu03ReRhEenQEu+1iNwvIh+IyJv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5442d3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AzABgYUXfKzwu7JCJZnQvILylb7t2gOQsfcDJv2NEccAAOY50NuQyNyybuUam\nonRb+uZNwU9Iq6tF9Fu1kn34LWe2a0YS9bP0a2sbXmtm6bXqlxto3TrnzSiR6L/4IuLyQQUxf77z\n2y36778vYz//6EdA//4yJKg9ZsOePfJ2c8klTgWeT6joFwC2P7RQXDymNy4gbpCOHaNZ+uvWOSLs\nJfrbtsVbhX6REmEtfVsswoSJpiL6fpb+tm3ytlJenriyqq0NNgDcDblAw0pkyxa5Rn6WfqdOUiEl\nsvSBYME118+4Scx+3D59oOE9smoVcNllIqxudu+W82aLvl15G2pqxDc/cSJwww3eZXTzyivimunU\nqWEggTFGFiyQqKE77gCuuca5H3/5S9nn7beH21e2UdEvAGyhL5TGXNMb1xC1g5Zx7QANRX/XLvHj\n2nlL/BrNwoq+cU0MGpSapX/4cGIXhdvSt9073bqJlR+UOgEA7r9fMjP67cvdkGvvF5BKY+vWYEvf\nuG68xNwMoGJEv1u31Cx90+7jdhGZ++C3v204QI1pYDXunW7dpFymwxkAfPQRUFEBPPGEdODavDnx\nm2NdnVj6F1wgOe/dlv7GjXLNzj4bePllSUw3Zw7wk5/IOXnkERknecSI4P3kChX9AqDQLH0z2Lex\n9IHkRb9Pn4YP6dat8jr91lvOPD9LP6x7x4jN6afLEI9BFnttrezPS/QBcU0FceCACLtZ3hZ9s82g\nJGmAuBe2bPHvxep27wDxom/elBKJvt91M8uazmVBkTNG5I2l7+XTN8LtjpQx0zt3As89F/+fidG3\nLX0gvvK57jp5Zt54Q9wxgLw9BPHPf8rxX3ihpL/2Ev1+/Zzp228HrrwSuPtuGTZz926x/vOVUKJP\nROOJaDURrSWi6R7/f4+IVhLRciJ6g4j6Wv9dSURrYp8r01n4UsEW/Xy19GfMcB4qO1zTEDU6Y/Vq\neYh7924o+satsWiRMy9V944RrNNOk2/zCu+FERU/0U/k4qmpkXKZRmAzepYt+oksfeOC8nJFMSd2\n79id5/zcO0GWvmnAjOLeCbL0+/cXN6CJ2DKsXSvursGDxW3i/g+QNhBTBsApR12d9PWYOlUGKRk2\nTOavWOFdTsOrr0pZzj8/nOgTAY89JmmS//Y32deppwbvI5ckFH0iagxgFoALAAwFcCkRDXUttgRA\nBTOPAPA8gAdi63YE8EMApwA4GcAPiahD+opfGhSCpf+b30g0w1tvxXfMMiRj6Z9wglihfqL/4Ydi\n8R85IiJRxNUoAAAgAElEQVSUqui3bg0MHy7TQS4erxh9ILzom7TKhg4dZJ1Nm8Jb+kGif+SI+LWD\nLH0v0Y/i3vESfb9UDH4+fVv0mzQRa9926wHAmjXSGPqd7wD//rcTUQOI6NujlrmjiNasEZeQycvT\nv79EWCUS/VdeAc48U46td285VlNm5oaiD0jHsD/9CZgyBfjpT4O3n2vCWPonA1jLzOuZ+QiAeQAm\n2Asw8wJmNo/WvwAYG+8/ALzOzLuZeQ+A1wGMT0/RS4d89+l//rkjhNOmOa/dbkt/9+7wIaeffCL+\n9SDRP3JEfLZBw9KFde9s3y4+YWM1Bom+ERV3DHYUS98WfeMi2b/f2WaQpV9d7ZwDL9G3c+kD3qJv\n3sZ69pS+AmVl8ed53z5H9PfsiY9OAbxFH/B+m7MtfWY5zkaNnErJcMIJDUV/7Vpx31x5pRzHrFnO\nf3bkjl0GU0ktWybfI0fKd+PG0hEtSPQ3bxZj4qtflenevZ35gNxrBw9Km5Kb8nLx7Y8a5b/9fCCM\n6PcEYL/gVMXm+XENgNeirEtE04iokogqdUjEhuzZI5ZEmzb5aembh+v22+WhvftumXZb+kC4TIrV\n1VKRJLL0AXl9D8pbHsXS79ZNKo6yssxa+iaXvqGD9e4bxtK3hd5L9M3xBo0VXFUlVm/HjjLdpo2/\newdo+JbmFn2/DlrMcv1MJWbSTbRp0zC89oQTxDo30TeHD4vYDhwo98EVVwDz5gEvvAA8/LBU+KYt\nAJDKs1mzeNFv2tTpcQyIiydI9H/7W/n2E33j9nNb+oVEWhtyiWgqgAoAD0ZZj5lnM3MFM1d0zsd+\nyzlmzx55ON2DaOcLJiHabbeJRbZ1qzyAtrBF6aBlrL1Eln6bNuLXD7L0jegniqgxok8kboAwom8E\n0ZCKe8dgi76fpW+EvmXLYEs/kXunVy9HeO3zfPiwWPZt2/r3pvaz9N2NuQcOyLk3Y8B++ml8hk2b\nQYOkEdy4gTZskHWNNX/jjfL/pEnArbeKS+jCC531ieIHdFm6VAS/WTNnmWHD5Ni9OlX9+tfAj38M\nfPObTkVRqqK/BUBva7pXbF4cRHQ+gDsBXMzMX0RZVwlmzx4Rho4d89fS791byvfTn4r49u4dv0yU\nVAxG9I2lf+BAfFrm6moRtIoKEf2gYenKykQ43OF+bkwPW0BcPIlEv1OneDEB0mvpB7l3jNCfcUaw\npZ+oIdd2v9mWvi3ofnl1/ETfvZzZphH9TZsaZtg0nHCCfJvrbxpqjegPHQq8+y7w3ntS0e/cCXz9\n6/HbsNsgli1zXDsG05i7cmX8/CeekHaDiy4C/vd/ncqwRw/5bUTfNEZ7uXcKhTCivxDA8UTUn4ia\nAZgM4CV7ASIaBeAxiODbj/V8AF8hog6xBtyvxOYpEdi9W4Qhny1901hWXi4RDI89Fr9MFNFfvVr8\nrwMGOOGEtuuhulreJEaPBpYvlxBLwN/SB4JdPF98IefVuCiM6Pu9HXjF6APpsfRNGRK5d9q1A8aM\nERFyj1Pgdu+0bNmwh6/pmGWwLf10ir7Zpm3pJyv6gHSGOv30+JxHNkb0TTuTuS8NQ2MhKLaL59VX\ngW99C/jyl4Fnn42vzJs2lWtiGr43bpR7z7TDFCIJRZ+Z6wDcBBHrVQCeZeYVRDSDiC6OLfYggNYA\nniOipUT0Umzd3QB+DKk4FgKYEZunRCCfLf3Dh4GPP463qEaPdkIfDW73ztGj8UMD2nzyibhYmjXz\njiE3oj9mjAj2W2+JqBn/tE0Y0TcVkS36Bw/6V1Cpir6fpU/kiKdfvhxARL9/f/nU1jqVnsHdkEsU\n7y6qr2/YeS4Z0W/WTNoFTHm9UjGYbR53nCxrLH2vAd+7dZNt2KLfvr33dfXDdBIz7Uxu0e/fX86L\nbenfe69c8xdfdI7Hxg7b9IrcKTRC+fSZ+VVmPoGZj2PmmbF5dzOzEffzmbkrM4+MfS621v0DMw+M\nfR7PzGEUN7n26a9d6y+aK1aIgLsfLjcdO0rEhhHSW26RUDyv3pEmXBNILPoA8OabYvmZcVJtwgyZ\n6I7GSRTBk25L31iNnTqJZQk4/3uV2xZ9M23jtvTN9ozo79ghlUUY905ZmXy8RN899qtXBy1z3dq1\nk452QZY+kfj1Taz+2rVyj0QZbrBrV7nHTDuT+75s1Cg+gmfDBnEZXX11w2giQ0mKvpJ+Hn44XHd/\nILeWfm2thKDZoXI27rA4Pxo1cgbKWLxYtnfgAPD22/HL1dc74ZpAsOgPHCjiUVPjn7c8zJCJfqLv\n5S+vr3fCO90ka+k3bSrTdkVi/nf79U2ceCqibyxpW7z8LH3Au4+Fn+j7Wfpt24offNMm/4ZcID5s\n04RrRqFrVwkLXrBA3FdeEV12BM/cufI9ZYr/No3o+8XoFxoq+jmgulqiD+bMSbxsXZ08JMan/8UX\n4ZJ6pYvPPxexcLsQDEuXiqAYoQzCRFbceKM8jC1bAn//e/wyn30mohXG0m/USFxJgP8IRWHcO27R\nNw+1V6W8e7dUhMla+vX1Uhbb0gfk2toVifnfXVlt3y7b799fLGeihqLvdu+Y7RnRf+89+T7lFOf/\nINH3EvOwom8nV0tk6QNy3Y0LaOPG5EQfAP7xD/+3z2HDxL1VXQ08+SQwblxww2zv3nId1q6VbxV9\nJTLm4QoTvmjCEzt2dHybYaz9ykrg8ssTR60kwgiiX5KqZcsksVSjEHdS587Aa6/J2LcPPii9Ht2i\nb17tw4g+4Lh4/Cz9KO4d09jcsqVEbXiJvl+MPuD4g4NE35TDLfpTp8bnXvez9I3AmzaPXr3CWfp2\nJ7V335W0BvY5a9NGKrMvvkhe9E1OfBs7z07fvnKu9+4NFn1myZVTX5+86B865P/2aSJ4nnhC3iqm\nTg3epolEe+cd+VbRVyJjrJ8wom98+MbSt+cF8Ze/yJvEg5F6TDTEiJxXXDOzd1icH126SPz3GWdI\nhXTeefKabTfovvyyiJnp1egWfeZ40TeWfirune3bpUK1ozb8wjaDRD+Mpe/OsGm4915Jz2vws/Rt\n0Tff7nK64/TN9mpqREjff98ZSNxgn2dzrc28sKJfXi6pGOyoJ7d7B5AyeDXkAk5l/+qr8p2s6APB\nlj4g2TGbN5e4/yBU9JWUiSL6xqo3Pn17XhAmxOzee53YYkAyDF5zjf+gFG6CLP1Nm+ThT9SIa+jR\nQxpbZ80St8T558v8N96Q7yNHxMc6YYJTwblF38Tsh7X0w7p33D76AQOkm78tYEePAo/HQhG8Hvym\nTeX4gkTfnUvfD78c+Eb0zf779/e39L3cO6tWidFwxhnx69j5d/buleVNw3jXrmLB26GhfqJv3JGG\nffukMm3eXNw77v25Safo+xkjffvKfbFjB3DxxYnDL92iX8gx+oCKfk5I1dIPK/rGGrz1VvlevVoy\nAP7hDxLxEgb3EHc2JkIirKX//e9LeKXJMz5ypFRkxsXz17+KuFx9tbOOET+zf+PuMg/qCSdI+tyL\nL4YnYd07btH/0pfkHF5+udOr9PrrJQ3AzJkNO58ZEg2k4mfp+5XbS/S7dnUqs/79pR3ETud88KC4\n2+w3FyP6774r027Rt/tDuAW9Sxexzu089WbULBvTrmIvt2+fs21bLP1Ev21buRZbtsgyUTvod+gg\nPXVbtYpP0WBjIniAxK4dQMrTpIkYAYUeow+o6OeEKD59I/q2Tz+Me2fzZrGC77xTcpX85jci+Mxi\nwdlpiYMIsvSXLpUHyHS8SUTXrvFuhUaNxMXz979LuR5/XN4GvvKV+GXatPEX/UaN5Ni+9CXvfYaN\n3nGL/ne+I13yn3pKGjyvuQb43e8kT3pQrnQzkIofUS19L/eOqcwB+c0c/zZnBkW3Qx1t0e/ataEg\nut07tqC7Y/XdA6gYTIcpP9Hv2dMpk5/oA461P3BgtHBNQO6Hrl0lW6pXCK+hokKWGx8i/WPjxnJf\nAoXv2gFU9HOCsfTDDASdjKXP7HSzv/VWecBvuEEa6t58U3olVlaGK2uQT3/ZMnlA/eKbw3D++WLV\nvfWWNPJecUXDh9WOLHGLfiK83Dv33hsv3HYKBkOjRsAPfiC9i7dtkwrpu9+V0ZGCyIal7xZ9M99g\nj5plsEX/jDMaiqmdU99k2DS4Rd+OvbfxEn07UqdZM0c8w4p+Mlx3XeJhER98UAwfdyoNP0yfBhV9\nJSmM6NfVJR7o3Pbpt20rgpjI0t+7VyzEXr0komT2bLFs3nhDGrEqKkT0E1U4QLClv3JleCvfD+PX\nv+46sSBt144hFdFv1kzOmS36zz0HPPCAVIw1NXKuvOLuAXnrWLJEhmZ8+OHElmci0U/F0q+rk5DH\nRKJvj5plb+/IEQmDdDfiAtEsfXd0j8G4d+wIHtvSBxwXj19DLuD00Uh2UPG77pLEf0G0aROfhiIR\nxp2noq8khd2ImsjFs2ePWH3NmongdOiQ2NK3B8gAgHPPlcEnjC99zBjZr3vAZy+MpV9T0zDHy65d\nTphjsgwYIML1ySeSU8VYeTapiD6RCKAtnlu2yLE89pgjZH6iD8gDP3lyOFdDuix9I9ruJGlHj8aL\nfo8ecm+4LX27EReIr2Tc/nwgPaKfyL0DOI25mbT0M4GKvpISttUcRvTthFxhUjG4Rd9NRYV8J3Lx\nMIulb1ID2ALkDp1MBWPte1n5QGqiD8Tn1K+tdXqXzp7t+MKDRD8K6bL0zSAjdmXlDtc0y/Xtm9i9\nYyqZVq28G95t945b9Nu1k4rFnDc/0Tcd5oJE31j6QaJ/xhniaz/vPP9lso2KvpISUS19W/TDpGIw\neUL8IkxGjAjXmLtvnzRKGovL9usby98uW7JceaWML/rNb3r/7yX6bsEJwhZ98+YyaZKI2C9+IdPZ\nEv2wlr5Zxq5ovUTfTIdx7wAydqupxN37InIsfVuoiaRfgsl66Sf6jRrJ/Rnk3hk7Vu7LoKicjh2l\nfccO8cw1w4fL8ZkY/0JGRT8H7N/vNCAlEn2TVtkQ1tI3D6oXLVuKLz6RpW8E0rxu228odgNzqowd\nKw25QWF8tui3ahW+AQ6I741q0klceaUc14svyrS7ITdZwlr6YRq/3emVN2wQ4XFX5m7RD3LveLl2\nANlu69Yi2IcPNxT0Cy6Q2HnzJgB4v22ZDloGd8qFiy6SdgmvbJb5zDnnyL2TTy6nZFHRzwH79zuv\nuWEsfTu1bBhLv6pKLFcvi84wZoxY+kGNuaYR1zSseYl+NmKW3aIfdZ+2pW/Ghu3VC7jpJvltksGl\ngzCWfqtWweGEBi9Lv3fvhtf1uONEaM194eXeMY2WxpXmRdu2jmvQLfpTp8pxvfiiv6UPxIt+ba2s\nE9RoW0ikyzDINSr6OWDfPmkAbd06cz59P3++oaJCrDozNJ0XxtL3En3jZkmHpZ8II/rJtiPYom8s\n/Z49ncG2u3QJJ8JhCCP6YVw7QMMhEzdt8vYpGx/9kiXybeL0bUaNknQNZ57pv7+2bR3XoFvQTz9d\n9j1nTmLRN+4dO9makj+o6OcA88rbuXPikaTc7p2OHUX0vQbXMGze7O/PN5j0BUF+fbelb/v00+ne\nSUTbtiL4Bw4kJ/q2e2fLFrGUy8tlu3feCUycmL6yhnHvJGrENbjdOxs3eqcAMPmHzLX0svSBhm0B\nbtq08bf0icTa//vfpWe3PYCKTadOjqVv591R8gcV/Rxgi36QpX/kiDzAbkuf2T/rJRDO0h8xQrqW\nB/n1t26Nz5mSS/eO2X86LP3u3Z2soNOnA7/6VfrKmk5L33bvmBGyvCz98nKpDBYvlmmvhtwwtG3r\nWOleVvyUKWJsPPusf0O67d5R0c9PVPRzQFjRt1MwGBIlXdu3T7afSPRbtEjcmGvSE5gHPJfuHbP/\ndIh+lE45UWnRIjOWflWVCK5fsq8xYxzR92rIDYMtzl6iPniwuAW9GnoN5eVy/AcPqujnKyr6OcCE\nsYUVfbelb//nJlGMvk1FRXBj7tatzrilJpzPLhtRdh7oVEXfjDcLiHvHpALIBC1bilXu7shmSNbS\n37hRvv1Ef/RoYM0aOT+HDydn6dtRNn6ibkaY8vvfTrpmfPpBMflK9lHRzzLM8iDblr6f6NopGAyJ\nLP1EMfo2Y8bIdoyguDGWvkl65vbpt2sXbvCUVDGiv3dv8pa+HbKZSUs/UU79ZC1904nMr3OQ8ev/\n85/ynax7x+An6pMnyzUPsvQBEX219PMTFf0sY9L0GtE/csQ/t72Xeyedlv6pp8q3yWfvZts2J9bf\nDpsERHyz4doBHEtx69b4XPphadVKRNgkE8u0pQ/4i35US9/cLxs3ypuVX2VuRN/kfE/GvWNb5H5C\n3a2bJJ772te8/1fRz3+a5LoApYb9ymuiH3bs8H4wvNw7iSx9I/phhO2kk6SzydNPA9deG//fkSPS\nqGd6qrpF3x1KmknMuTFvMcm4dwDJhw4UlqXPLNvatMnJs+NF165yXCZffiqWfqtWwX08fvpT///s\npGsq+vmJWvpZxh4z1HRF9/PrJ+vT79o1XI9VIvHRLljgdFoymFBSY+m3a9dQ9LM1mIQRDdOnIBlL\nHxCfN5AdS98vp35NTXjRt9Mrb9qUeMSm0aMlsZ5djiiY8xwlxYUbL0s/7PEq2UFFP8vYHVYSib6x\n5m2Ra9FCHuggn34Yf77hssvEmnzmmfj5pmOWbem7ffrZdu+kS/RzZenv2SMCHrb89pCJfjH6NmPG\nOJVNKg25qYi+eRM1ot+6dXbafZTw6OXIMrZ7J4yl37atxNPbBKVXDhOjb3PCCRLFM3du/HzTMSsf\nfPqmI1Cyom8s5mxa+l6i/+c/y/cFF4Tblj1U5ObNiTM8Gr8+kJp7JxXRb9pU1t+5U+51de3kHyr6\nHtTUhBvKMBmiir6XsJpeuV5EFX1ArP3Fi4GPP3bmuS39XLp3ABGPdFj6bdpkNoQwSPSfeUbGDzC9\noRNhV1Z1deHcO+5yRCEdog84HbTcGTaV/EBF34PbbosfpzWd2D79sjKxYIPcO16i72fpmwyIUUX/\nm98U//5TTznzjKVvkkzZlv7hw/LJlqVv9m9i1lMR/Uxa+YC/6O/YIVFSYQdjARxLf8UK+U5k6ffo\n4VyvXLl3ABX9fEdF34Ply+MHmk4ntk+fKLiDljvDpqFzZ2DVKid5mMHOIBmFHj1kdK2nnnL6DGzb\nJg+vaRA2onv0aHZ74xrCxJD7YSzmzz/PrD8f8Bf9F16Qc+c3ZoAXptxG9BNZ+kSOtZ8r9w6gop/v\nqOh7sGGDWMxBSc2Sxd1LMUj0d+xwoiFs/t//kx6m557ruGGAaB2z3Fx2mYQ0vv22TJveuAbz8O7f\nn928O+79R82lb9Yx5MrSf+YZSWMwfHj4bbkt/TCDihjRTyVOP1Wh7tTJCdnU3rj5RyjRJ6LxRLSa\niNYS0XSP/88iosVEVEdEk1z/HSWipbHPS+kqeKY4dEis3Pr6+LS26WL/fmcoPMBf9Ovr/VPpnnKK\njCxUVSXCb1wxUTpmuZk0SdabOlUE3+6YBcTn38lmhk2DEaJkKppci/5nn8kgMVFcO0C8T79Ll3DW\n++WXA9/6VnL3QLt2kmI61bEFjKWvDbn5ScLOWUTUGMAsAF8GUAVgIRG9xMwrrcU+BXAVgNs8NnGI\nmT1G5cxP7JQEJnomnZgwNvPwd+4c34Bq2LYN+OIL/3S4Z5whwn/BBWIBtmolOV+A5IStbVvg5Zdl\nuxMmiKvo3HPj/zflz6V7JxnRt3vA5sK98/zz4jaL4toBHEs/TCOuYdAg4Pe/j7YfQ8uWwN/+Ft8g\nnAzl5XKfHD2qop+PhOmRezKAtcy8HgCIaB6ACQCOiT4zb4z9lwGHSHaxh52rrg7/sIXFbf34Wfqm\nHEGNd2eeKRbkM89ID9ojR+ShT3YoupEjJXRz4kQRKdvSt/PfqKXvj5foP/OMpLIePDjatuzKKlsD\ncgeNrBUW86Zw4ICKfj4SRvR7AthsTVcBOCXCPloQUSWAOgD3MfOL7gWIaBqAaQDQJ8ejIa9f7/w2\nFm06cY8Z2rmz+OfdA1+YN45EA1+MGRM+BDAMEyYADzwA/M//xFvFtqWfS59+qqKfaUvfVLhG9Pfs\nAd5/H5gxI/q2GjeW7R0+nH7jI5PY7VAq+vlHNnLv9GXmLUQ0AMCbRPQhM6+zF2Dm2QBmA0BFRUXA\nqK2Zx7b0Ew1LmAxeog+ItW8/2KYcuXjYb71V3hjsofVsn36huXeaNpVPbW3mLf1GjaSh2Yi+uY5R\nGnBtWrdW0VfSS5iG3C0A7HiQXrF5oWDmLbHv9QD+AWBUhPJlnQ0bHMswrKW/bp00hIZZ3h3R4NdB\na8MGiZ5JJgojVYiAiy6KF1i3pV9WFpyUK92kIvqAc01tl1WmsEfPMm9sybpnjIsnW+6ddGA3BGv0\nTv4RRvQXAjieiPoTUTMAkwGEisIhog5E1Dz2uxOAsbDaAvKRDRsk+yTQUMSfe04audwDZLz5psRh\nP/54/Py6OmlUs/27Xj59oKHob9yY2LWTTdw+/Wy6duz9pyL6nTtHD/dMhnSKvmnMVUtfSRcJRZ+Z\n6wDcBGA+gFUAnmXmFUQ0g4guBgAi+hIRVQH4BoDHiCgWWYwhACqJaBmABRCffsGIvtu9869/AUuW\nOOOIGkzI5OzZ8QOiPP64pCz+y1+ceUHuHXc58kn0y8qc0bOymWzNkKrol5Vl3p9vcIt+u3aplRtQ\n0VfSRyifPjO/CuBV17y7rd8LIW4f93rvA0jSm5l99uwRS3bgQLlZ3Za+Eftt25zu7mYakNDLd94B\nzjpLrPz77pP5djtBGNGvq5M8M5demp7jSgeNGjmpGLKZbM2QquiXl2fen29wi34qrpnWreVcF5J4\ntmzpnINCKnepoD1yLYw4DxggD5qf6Nu9YAER/X79xKJ77DGZN2+eEwlktsvc0Kffrh3QvHl8SoUt\nW8SFlE+WPuCIfi7cO6aS8UpLEYY5c4BHH01feYJwi34qVnrv3hLuWWgYv76Kfv6hom9hRLp/fxE1\nt3snSPQHDJDekM8/LzleZs6Uh7WiwvHrfvGFWPH2g0Ak8fFm8AsgXIx+LjA59XPh3jnpJOB3vwMu\nvDC59QcOTK6XajK0bCkRN2aYw1Su46xZ8e7BQsG4eLQhN/9Q0bcwYmtE323pGxeMcecYzADi110n\nHaQuuURcPXfeKZWBEX133h3DqacClZVOj9qwMfrZxqRXzoV7hwi45prkO55lE2Pp79kj1zwV0S8r\nSz0BWi4woq+Wfv6hom+xYYOIWbt24d07zI7on3gicPrp0kt20CDgP/9THvhNmySXjp/on3aaiMSy\nZU45GjVKLnFaJmnbVoRs377su3cKCSP6qUbuFDKdOklIb/PmuS6J4kZF38KOmHG7d774whFt29Kv\nqZHetCYj5XXXyfftt0uPyn79xPrfujU+l77NaafJ97/+5ZSjZ8/shBdGwR7IJNuWfiGhoi/PUe/e\n0RLMKdlBRd/CLfq2pb9rl/PbtvRNBWBEf+pUGSzjiitk2mxv48b4XPo2vXtLp6F//tNZNt9cO4CU\n27i4VPT9UdEH7roLeO+9XJdC8UJFP0Z9fbzYduggVnxdnUwb107z5vGWvnuEqUaNJDulsXDMA79h\ng797h0isfSP6+Rajb7B9y+re8ccW/bZtS/NctWoVPx6Dkj+o6MdwpzI2D6qx9o2FO3RosKXvxoTr\n2Za+V0TDaaeJ2H/6qYRs5qN1aL+hqKXvjy36/fqpi0PJL1T0Y9jhmkBD0TeW/vDhkjLWDLCSSPRb\ntpT/Nm709+kDjl//ueekcTgfLX0V/XAY0d+wIT8rb6W0UdGPYYdrAo6oeYk+4Fj727ZJg63XsIaG\nfv3i3TteYWyjRwNNmgBPPx1fjnzCLncpuizC0rKluAvXrSus9AlKaaCiH8PdIcqImongMaI/bJh8\nGwt/+3YZyq5xY/9t9+8f794xSbRsWrYERo0CFi2KL0c+Yfv01dL3x86pn4/XUSltSk70Fy6Mz4Vj\n2LBBcrOYB9bLvdOhg9Or07b0EzVY9esnvvrqamng8qsgjIunadPsJQeLgrH0mzYNN15rqWKnw1bR\nV/KNohX9Z55xBgo3MMvIULfe2nD5Tz4BjjvOmfZy73Tq5ORjN5Z+WNGvq5NeukHd0k89Vb779Al+\nc8gVdtIzbZz0R0VfyWeKUvSrq4HJk4GHHoqfv22bWOhLl8bPr68HPvwwPrGV272zY4eIfseO4nuP\nYukb//zy5cHd0o2ln69CYcqurp1gVPSVfKYoRX/1avk2/nHDkiXyvWGDE0kDOP52k0cfkJwnjRvH\nW/qdO0scfrduIvb19eLTD2PpA5JJM8jS79tXEoPZ5cgnjE9fRT8YI/pt2ui5UvKPbIyRm3U+/li+\nFy+WFMXGVWJEHxCr+4wznN9AvKVPFJ9/Z+dOZwDybt3E0t+9W9w2iUS/Tx/ZHnOw6BNJm0MuhkgM\nQ6o57UsFc/00Rl/JR4ra0j940PkNiFvHWKsmuZn5TSQJ02xM/h1mx6cPiF9/27bEMfqG5s2dATwS\npZpt3z5/k1SZ0bPUeg3GFn1FyTeKVvTNg1dZ6cxfsgT48pdFtIx1D4joDxzoDE1nMPl3DhyQ3rpG\n9I2lH1b0AUcACjnVLJG4uOxRw5SGqOgr+UzRiv6554qIG9Hfu1c6y4waJT5z29Jfvtzbj27cOyYF\ng23p79jhRAeFEX3TmFvog0q88gowfXquS5HfqOgr+UzRif7Ro8CaNZIjx+7sZCz7kSNF4D/8UJbd\nv18qAy/RN+4d0zHLtvSZnW1GsfQLXfQrKjSRViL69pVxFc47L9clUZSGFF1D7saNkr9+0CAZieqx\nx6Sx1TTijholbpmDB0XsTcpkr3FIjXvHiL4ZxNzE6i9dKlZdGCEvFktfSUzr1ppWWMlfik70TcPt\noEHSIHrokETzLFkivuju3R2rftkyR/SD3Dtelj4got+tW7gIjWLw6SuKUvgUtegbka6sFIEeOVKm\nhwDuS+0AABDWSURBVA2TMM7ly0XQ27WTsEo37dtLA+7mzTJt+/QBcf0MHhyuXCec4MT4K4qi5Iqi\nFP0OHUSgy8vlVfv994EVK4Dx42WZFi2kUjCW/ogR3ta6iUdfu1YqCRPuaUevhBXxXr1kf2ErCUVR\nlExQdA25q1eLsBKJZT16tOSor611LH1AhH7pUv/IHcCJR1+zRioRUzG0aOH8FyV88cQTJYWDoihK\nrihK0R80yJmuqHB61Y4a5cw/6SRx29TU+Iu+sfTXrHEacQ3GxaPuGkVRComiEv19+6TTlC36JnVC\nWZl0wDLYQu8VuQM4or99u+PPNxixV9FXFKWQKCrRtxtxDRUV8n3SSeLuMRjRb9SoYfoFg51uwC36\naukrilKIFJWH2Uv0Bw4Uv/vpp8cv2727NPR26uQ/IIidWEwtfUVRioFQlj4RjSei1US0logadMIn\norOIaDER1RHRJNd/VxLRmtjnynQV3IvVq8VytwdDadRIGmxnzHCXGbjmGuCKK/y3FyT6JoGair6i\nKIVEQkufiBoDmAXgywCqACwkopeYeaW12KcArgJwm2vdjgB+CKACAANYFFt3T3qKH8/q1cCAAQ2z\nVPoJ8/33B2+veXPpcXvoUEPRnzpV3hC84vsVRVHylTCW/skA1jLzemY+AmAegAn2Asy8kZmXA6h3\nrfsfAF5n5t0xoX8dwPg0lNuTjz+Od+2kA2Ptu6N3unQBrr9e86UrilJYhBH9ngA2W9NVsXlhCLUu\nEU0jokoiqtxhUlpGpL5eQiszJfpuS19RFKUQyYvoHWaezcwVzFzR2W1Sh2TLFuDw4fSLvongUdFX\nFKUYCBO9swVAb2u6V2xeGLYAONu17j9CrhuJ3r2lo1W6UUtfUZRiIoylvxDA8UTUn4iaAZgM4KWQ\n258P4CtE1IGIOgD4SmxeRigrazj6Vaqo6CuKUkwkFH1mrgNwE0SsVwF4lplXENEMIroYAIjoS0RU\nBeAbAB4johWxdXcD+DGk4lgIYEZsXsHQqZMkbfOL5VcURSkkiJlzXYY4KioquNIe2DbHfPqpNBDr\nKEiKouQzRLSImSsSLVdUPXIzQZ8+GouvKErxkBfRO4qiKEp2UNFXFEUpIVT0FUVRSggVfUVRlBJC\nRV9RFKWEUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWEUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9R\nFKWEUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWEUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWE\nUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWEUNFXFEUpIVT0FUVRSohQok9E44loNRGtJaLpHv83\nJ6JnYv9/QET9YvP7EdEhIloa+/wmvcVXFEVRotAk0QJE1BjALABfBlAFYCERvcTMK63FrgGwh5kH\nEtFkAPcD+Gbsv3XMPDLN5VYURVGSIIylfzKAtcy8npmPAJgHYIJrmQkAnoj9fh7AeURE6SumoiiK\nkg7CiH5PAJut6arYPM9lmLkOwF4A5bH/+hPREiJ6i4jOTLG8iqIoSgokdO+kyFYAfZh5FxGNAfAi\nEQ1j5n32QkQ0DcA0AOjTp0+Gi6QoilK6hLH0twDobU33is3zXIaImgBoB2AXM3/BzLsAgJkXAVgH\n4AT3Dph5NjNXMHNF586dox+FoiiKEoowor8QwPFE1J+ImgGYDOAl1zIvAbgy9nsSgDeZmYmoc6wh\nGEQ0AMDxANanp+iKoihKVBK6d5i5johuAjAfQGMAf2DmFUQ0A0AlM78E4PcAniSitQB2QyoGADgL\nwAwiqgVQD+B6Zt6diQNRFEVREkPMnOsyxFFRUcGVlZW5LoaiKEpBQUSLmLki0XLaI1dRFKWEUNFX\nFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWEUNFXFEUpIVT0FUVRSggVfUVRlBJCRV9RFKWEUNHPIHPn\nAv36AY0ayffcubkukaIopU6mUyuXLHPnAtOmAQcPyvSmTTINAFOm5K5ciqKUNmrpZ4g773QE33Dw\noMxXFEXJFSr6GeLTT6PNVxRFyQZFI/r55j/3GwBMBwZTFCWXFIXoG//5pk0As+M/z6Xwz5wJtGoV\nP69VK5mvKIqSK4pC9FP1n2fiLWHKFGD2bKBvX4BIvmfPDt+Im29vLkppofdfEcPMefUZM2YMR4WI\nWWz8+A9R4nXnzGFu1Sp+vVatZH6uyMcyKaWD3n+FCWQkw4QaWxSWfir+83yMsvEr09SpqVldar2l\nRqmcv3x8JpQ0EqZmyOYnGUs/FcsklbeEdDBnDnPfvrK/vn1l2q9MqVhdar2lRimdv1w/E0pyIKSl\nn3ORd3+SEX3mePEsL5eP+7cRVXv5IHG1l88EfkJSXh5cLrts7grDD79j7ds3c8dXTJTS+Uv1WKPc\nl0r6KDnRN3gJqVtUb7gheJlE1ly6bmq/h6u8PFz5olieiay3bD6ohSQKiYyDYrR+U3mrKaU3onyj\nZEU/kfWezMe2cNJ5UwcJcSKxadw4cVnDnBcjun7HlG6BzrUoRDmeRAZEIVn6Ua9jstc9mbeEQjIC\n3ORT2UtW9BP5w1MR/kRCHPWih3lA/EQyquUZJLZR3jhSffMJOn9h3HKpELXCSWRAZLqySpeghD3u\ndOwvantAlGuSCYH1cwuHrRjz6a2mZEU/FUvfz3q2b9xE24hy0VN5GJOpfPwemqgVZSpvPslWyul4\nmKJaoUFlDRKFdIhTMoLiJ2Bh3grTJWBRz3HY5TMhsIne5Mz197uG+dbOU7KiH+aV3E9Uovj6w4pi\nmPImIxCp3rA2USvKMO4n977DLJ/O8+pFVCs0WVdFGFdZIqsy6r6Tue/t444ivkH3a7qMAPc1SaZ8\nid4co9yPXscQxj2bTbdPyYo+s/eFz6Y4JRLbdL62hylrotdlc6O61/E7b2Ebms120lWZmnIm6/qJ\nKhx+5yWorSPZxnn3NQr7lpHK/RpG/NwVvN/58Dp/qVRujRuHC2O2K4coFV+y92Oy1zqZij8qJS36\nXqTbnxv2xvJ6OKMKifs43MuEdZm4xcJ9PtxvB37nLExIaTY/Yd9qgo7ZNg7c5zPMeWnaNPXzYlc+\nYdoTUqlMw64ftoJP1uoPI9SJKsB0PrNhz10yz0fUij8qKvoeRLGww9yMYR4Iv+iYMNsK84BEFeFE\njbe29ef3ipzuByid2/OqAPze/KK0L2RDXJK1WsN+3NZzmIolyvV2n/tk7rFE7WpB+8tUEEfQ/RDV\nqxDlPouKin4aiGKdB92YqYhEGLGJ4m4x2wx6QLwqlnS6aMI8/PbvZLdv1o3iugraliGT4pKpSjaK\nT9q+Nqk0vIe5Nu7yhd2f39tYOs59mO252xzSWUkn6+pJq+gDGA9gNYC1AKZ7/N8cwDOx/z8A0M/6\n7/bY/NUA/iPRvvJJ9G0SvSUEWTWpCpchbFx/mBs2yJcaZb790Kba6c2PbL22J3oQc1GeZN1pQf7i\nMNFf6TjWKFa72W+urrWXiAeVJepbUzLXParwp030ATQGsA7AAADNACwDMNS1zHcA/Cb2ezKAZ2K/\nh8aWbw6gf2w7jYP2l6+in4gg/2Uqr9FhHsCghki/5f3Km8wNGrVhMaolkwlXRyoPYNjyBIleFCs+\nasN5Km7LMMcaxYiJct2M8ZKuax3lrSmVqKhM3ZtRXT3pFP3TAMy3pm8HcLtrmfkATov9bgJgJwBy\nL2sv5/cpVNFn9n8bSKXRNIxPPyhKKGh5r/JGfQOI8rCk0lCVDr+8WxBSCXl1l6dZs4bH6vXmEyVK\nxy2G6ahMwxgDQccadL/6bS+sFez2k3td37BvPunoDOZ1/FHfhFOp+P2OwY90iv4kAL+zpi8H8EvX\nMh8B6GVNrwPQCcAvAUy15v8ewCSPfUwDUAmgsk+fPtGOtEBINjInmWVSXT6sTz/Kw5KOkLSgfUSp\nADIRPhdU4SfjFvQSQ7O9VCrTqH0VwhyznzjbZQrztpvuCidRuZO51uloCwt7bnJp6Wdc9O1PIVv6\nxUKyApZrgkQoSme1bBLV3WLWSfY6pEtgvI4jqExh3naT2V+YCiedhI1IivLcpOutWN07SkmT7xWU\nTTrfOMLsK93pDKLsOxPXJJvXOlPnLx3HkE7RbwJgfawh1jTkDnMtc6OrIffZ2O9hrobc9cXakKso\nhUIhVYj5SL6ev7CiT7JsMET0VQCPQCJ5/sDMM4loRmwnLxFRCwBPAhgFYDeAycy8PrbunQC+BaAO\nwHeZ+bWgfVVUVHBlZWXCMimKoigORLSImSsSLhdG9LOJir6iKEp0wop+UQyMriiKooRDRV9RFKWE\nUNFXFEUpIVT0FUVRSoi8a8gloh0ANqWwiU6QfgKlRCkeM1Cax12KxwyU5nFHPea+zNw50UJ5J/qp\nQkSVYVqwi4lSPGagNI+7FI8ZKM3jztQxq3tHURSlhFDRVxRFKSGKUfRn57oAOaAUjxkozeMuxWMG\nSvO4M3LMRefTVxRFUfwpRktfURRF8UFFX1EUpYQoGtEnovFEtJqI1hLR9FyXJ1MQUW8iWkBEK4lo\nBRH9d2x+RyJ6nYjWxL475Lqs6YaIGhPREiL6a2y6PxF9ELvmzxBRs1yXMd0QUXsiep6IPiaiVUR0\nWrFfayK6JXZvf0RETxNRi2K81kT0ByL6nIg+suZ5XlsSHo0d/3IiGp3sfotC9ImoMYBZAC6ADMZ+\nKRENzW2pMkYdgFuZeSiAUwHcGDvW6QDeYObjAbwRmy42/hvAKmv6fgA/Y+aBAPYAuCYnpcosPwfw\nN2YeDOAkyPEX7bUmop4AbgZQwcwnQtK5T0ZxXus/Ahjvmud3bS8AcHzsMw3Ar5PdaVGIPoCTAaxl\n5vXMfATAPAATclymjMDMW5l5cez3fogI9IQc7xOxxZ4A8P/lpoSZgYh6AbgQwO9i0wTgXADPxxYp\nxmNuB+AsyDCjYOYjzFyNIr/WkIGbWhJREwCtAGxFEV5rZn4bMv6Ijd+1nQDgf2PjpfwLQHsi6p7M\nfotF9HsC2GxNV8XmFTVE1A8ycM0HALoy89bYX9sAdM1RsTLFIwD+H4D62HQ5gGpmrotNF+M17w9g\nB4DHY26t3xFRGYr4WjPzFgAPAfgUIvZ7ASxC8V9rg9+1TZvGFYvolxxE1BrAC5DRyPbZ/8WGTiua\nWFwi+hqAz5l5Ua7LkmWaABgN4NfMPArAAbhcOUV4rTtArNr+AHoAKENDF0hJkKlrWyyivwVAb2u6\nV2xeUUJETSGCP5eZ/xSbvd287sW+P89V+TLAWAAXE9FGiOvuXIivu33MBQAU5zWvAlDFzB/Epp+H\nVALFfK3PB7CBmXcwcy2AP0Guf7Ffa4PftU2bxhWL6C8EcHyshb8ZpOHnpRyXKSPEfNm/B7CKmR+2\n/noJwJWx31cC+Eu2y5YpmPl2Zu7FzP0g1/ZNZp4CYAGASbHFiuqYAYCZtwHYTESDYrPOA7ASRXyt\nIW6dU4moVexeN8dc1Nfawu/avgTgilgUz6kA9lpuoGiEGT29ED4AvgrgEwDrANyZ6/Jk8DjPgLzy\nLQewNPb5KsTH/QaANQD+DqBjrsuaoeM/G8BfY78HAPg3gLUAngPQPNfly8DxjgRQGbveLwLoUOzX\nGsCPAHwM4CMATwJoXozXGsDTkHaLWshb3TV+1xYAQSIU1wH4EBLdlNR+NQ2DoihKCVEs7h1FURQl\nBCr6iqIoJYSKvqIoSgmhoq8oilJCqOgriqKUECr6iqIoJYSKvqIoSgnx/wPH0mEJh/S4gwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd53ce2a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "These curves look very noisy. To make them more readable, we can smooth them by replacing every loss and accuracy with exponential moving \n",
    "averages of these quantities. Here's a trivial utility function to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BIeh02vWRBQRj0kw6Tn8NbuqLxYsXl70eMGAABQUF/Pe//6VPnz4cffTRHH/8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\noWHut+1zMNmqGrcTtl4FhKoQkYIgt26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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd53cc076d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FXn9u27Zto0WLFsVeT6RY0A+DzEzo3RsWLIBHH4VffnFv2k6b5trFj4+PdA5N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\ny6VyEc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd53cbf15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def smooth_curve(points, factor=0.8):\n",
    "  smoothed_points = []\n",
    "  for point in points:\n",
    "    if smoothed_points:\n",
    "      previous = smoothed_points[-1]\n",
    "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "    else:\n",
    "      smoothed_points.append(point)\n",
    "  return smoothed_points\n",
    "\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(acc), 'bo', label='Smoothed training acc')\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(val_acc), 'b', label='Smoothed validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(loss), 'bo', label='Smoothed training loss')\n",
    "plt.plot(epochs,\n",
    "         smooth_curve(val_loss), 'b', label='Smoothed validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "These curves look much cleaner and more stable. We are seeing a nice 1% absolute improvement.\n",
    "\n",
    "Note that the loss curve does not show any real improvement (in fact, it is deteriorating). You may wonder, how could accuracy improve if the \n",
    "loss isn't decreasing? The answer is simple: what we display is an average of pointwise loss values, but what actually matters for accuracy \n",
    "is the distribution of the loss values, not their average, since accuracy is the result of a binary thresholding of the class probability \n",
    "predicted by the model. The model may still be improving even if this isn't reflected in the average loss.\n",
    "\n",
    "We can now finally evaluate this model on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n",
      "test acc: 0.967999992371\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')\n",
    "\n",
    "test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n",
    "print('test acc:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Here we get a test accuracy of 97%. In the original Kaggle competition around this dataset, this would have been one of the top results. \n",
    "However, using modern deep learning techniques, we managed to reach this result using only a very small fraction of the training data \n",
    "available (about 10%). There is a huge difference between being able to train on 20,000 samples compared to 2,000 samples!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take-aways: using convnets with small datasets\n",
    "\n",
    "Here's what you should take away from the exercises of these past two sections:\n",
    "\n",
    "* Convnets are the best type of machine learning models for computer vision tasks. It is possible to train one from scratch even on a very \n",
    "small dataset, with decent results.\n",
    "* On a small dataset, overfitting will be the main issue. Data augmentation is a powerful way to fight overfitting when working with image \n",
    "data.\n",
    "* It is easy to reuse an existing convnet on a new dataset, via feature extraction. This is a very valuable technique for working with \n",
    "small image datasets.\n",
    "* As a complement to feature extraction, one may use fine-tuning, which adapts to a new problem some of the representations previously \n",
    "learned by an existing model. This pushes performance a bit further.\n",
    "\n",
    "Now you have a solid set of tools for dealing with image classification problems, in particular with small datasets."
   ]
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    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دانشگاه تربیت دبیر شهید رجایی<br>مباحث ویژه - آشنایی با بینایی کامپیوتر<br>علیرضا اخوان پور<br>96-97<br>\n",
    "</div>\n",
    "<a href=\"https://www.srttu.edu/\">SRTTU.edu</a> - <a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a>\n",
    "</div>"
   ]
  }
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