{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2.0.8'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import keras\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 - Using convnets with small datasets\n",
    "\n",
    "This notebook contains the code sample found in Chapter 5, Section 2 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\n",
    "\n",
    "## Training a convnet from scratch on a small dataset\n",
    "\n",
    "Having to train an image classification model using only very little data is a common situation, which you likely encounter yourself in \n",
    "practice if you ever do computer vision in a professional context.\n",
    "\n",
    "Having \"few\" samples can mean anywhere from a few hundreds to a few tens of thousands of images. As a practical example, we will focus on \n",
    "classifying images as \"dogs\" or \"cats\", in a dataset containing 4000 pictures of cats and dogs (2000 cats, 2000 dogs). We will use 2000 \n",
    "pictures for training, 1000 for validation, and finally 1000 for testing.\n",
    "\n",
    "In this section, we will review one basic strategy to tackle this problem: training a new model from scratch on what little data we have. We \n",
    "will start by naively training a small convnet on our 2000 training samples, without any regularization, to set a baseline for what can be \n",
    "achieved. This will get us to a classification accuracy of 71%. At that point, our main issue will be overfitting. Then we will introduce \n",
    "*data augmentation*, a powerful technique for mitigating overfitting in computer vision. By leveraging data augmentation, we will improve \n",
    "our network to reach an accuracy of 82%.\n",
    "\n",
    "In the next section, we will review two more essential techniques for applying deep learning to small datasets: *doing feature extraction \n",
    "with a pre-trained network* (this will get us to an accuracy of 90% to 93%), and *fine-tuning a pre-trained network* (this will get us to \n",
    "our final accuracy of 95%). Together, these three strategies -- training a small model from scratch, doing feature extracting using a \n",
    "pre-trained model, and fine-tuning a pre-trained model -- will constitute your future toolbox for tackling the problem of doing computer \n",
    "vision with small datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The relevance of deep learning for small-data problems\n",
    "\n",
    "You will sometimes hear that deep learning only works when lots of data is available. This is in part a valid point: one fundamental \n",
    "characteristic of deep learning is that it is able to find interesting features in the training data on its own, without any need for manual \n",
    "feature engineering, and this can only be achieved when lots of training examples are available. This is especially true for problems where \n",
    "the input samples are very high-dimensional, like images.\n",
    "\n",
    "However, what constitutes \"lots\" of samples is relative -- relative to the size and depth of the network you are trying to train, for \n",
    "starters. It isn't possible to train a convnet to solve a complex problem with just a few tens of samples, but a few hundreds can \n",
    "potentially suffice if the model is small and well-regularized and if the task is simple. \n",
    "Because convnets learn local, translation-invariant features, they are very \n",
    "data-efficient on perceptual problems. Training a convnet from scratch on a very small image dataset will still yield reasonable results \n",
    "despite a relative lack of data, without the need for any custom feature engineering. You will see this in action in this section.\n",
    "\n",
    "But what's more, deep learning models are by nature highly repurposable: you can take, say, an image classification or speech-to-text model \n",
    "trained on a large-scale dataset then reuse it on a significantly different problem with only minor changes. Specifically, in the case of \n",
    "computer vision, many pre-trained models (usually trained on the ImageNet dataset) are now publicly available for download and can be used \n",
    "to bootstrap powerful vision models out of very little data. That's what we will do in the next section.\n",
    "\n",
    "For now, let's get started by getting our hands on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading the data\n",
    "\n",
    "The cats vs. dogs dataset that we will use isn't packaged with Keras. It was made available by Kaggle.com as part of a computer vision \n",
    "competition in late 2013, back when convnets weren't quite mainstream. You can download the original dataset at: \n",
    "`https://www.kaggle.com/c/dogs-vs-cats/data` (you will need to create a Kaggle account if you don't already have one -- don't worry, the \n",
    "process is painless).\n",
    "\n",
    "The pictures are medium-resolution color JPEGs. They look like this:\n",
    "\n",
    "![cats_vs_dogs_samples](https://s3.amazonaws.com/book.keras.io/img/ch5/cats_vs_dogs_samples.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, the cats vs. dogs Kaggle competition in 2013 was won by entrants who used convnets. The best entries could achieve up to \n",
    "95% accuracy. In our own example, we will get fairly close to this accuracy (in the next section), even though we will be training our \n",
    "models on less than 10% of the data that was available to the competitors.\n",
    "This original dataset contains 25,000 images of dogs and cats (12,500 from each class) and is 543MB large (compressed). After downloading \n",
    "and uncompressing it, we will create a new dataset containing three subsets: a training set with 1000 samples of each class, a validation \n",
    "set with 500 samples of each class, and finally a test set with 500 samples of each class.\n",
    "\n",
    "Here are a few lines of code to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The path to the directory where the original\n",
    "# dataset was uncompressed\n",
    "original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'\n",
    "\n",
    "# The directory where we will\n",
    "# store our smaller dataset\n",
    "base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'\n",
    "os.mkdir(base_dir)\n",
    "\n",
    "# Directories for our training,\n",
    "# validation and test splits\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)\n",
    "\n",
    "# Directory with our training cat pictures\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "os.mkdir(train_cats_dir)\n",
    "\n",
    "# Directory with our training dog pictures\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)\n",
    "\n",
    "# Directory with our validation cat pictures\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)\n",
    "\n",
    "# Directory with our validation dog pictures\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)\n",
    "\n",
    "# Directory with our validation cat pictures\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)\n",
    "\n",
    "# Directory with our validation dog pictures\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)\n",
    "\n",
    "# Copy first 1000 cat images to train_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "# Copy next 500 cat images to validation_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy next 500 cat images to test_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy first 1000 dog images to train_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy next 500 dog images to validation_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy next 500 dog images to test_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, let's count how many pictures we have in each training split (train/validation/test):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training cat images: 1000\n"
     ]
    }
   ],
   "source": [
    "print('total training cat images:', len(os.listdir(train_cats_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training dog images: 1000\n"
     ]
    }
   ],
   "source": [
    "print('total training dog images:', len(os.listdir(train_dogs_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total validation cat images: 500\n"
     ]
    }
   ],
   "source": [
    "print('total validation cat images:', len(os.listdir(validation_cats_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total validation dog images: 500\n"
     ]
    }
   ],
   "source": [
    "print('total validation dog images:', len(os.listdir(validation_dogs_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total test cat images: 500\n"
     ]
    }
   ],
   "source": [
    "print('total test cat images:', len(os.listdir(test_cats_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total test dog images: 500\n"
     ]
    }
   ],
   "source": [
    "print('total test dog images:', len(os.listdir(test_dogs_dir)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "So we have indeed 2000 training images, and then 1000 validation images and 1000 test images. In each split, there is the same number of \n",
    "samples from each class: this is a balanced binary classification problem, which means that classification accuracy will be an appropriate \n",
    "measure of success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building our network\n",
    "\n",
    "We've already built a small convnet for MNIST in the previous example, so you should be familiar with them. We will reuse the same \n",
    "general structure: our convnet will be a stack of alternated `Conv2D` (with `relu` activation) and `MaxPooling2D` layers.\n",
    "\n",
    "However, since we are dealing with bigger images and a more complex problem, we will make our network accordingly larger: it will have one \n",
    "more `Conv2D` + `MaxPooling2D` stage. This serves both to augment the capacity of the network, and to further reduce the size of the \n",
    "feature maps, so that they aren't overly large when we reach the `Flatten` layer. Here, since we start from inputs of size 150x150 (a \n",
    "somewhat arbitrary choice), we end up with feature maps of size 7x7 right before the `Flatten` layer.\n",
    "\n",
    "Note that the depth of the feature maps is progressively increasing in the network (from 32 to 128), while the size of the feature maps is \n",
    "decreasing (from 148x148 to 7x7). This is a pattern that you will see in almost all convnets.\n",
    "\n",
    "Since we are attacking a binary classification problem, we are ending the network with a single unit (a `Dense` layer of size 1) and a \n",
    "`sigmoid` activation. This unit will encode the probability that the network is looking at one class or the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at how the dimensions of the feature maps change with every successive layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our compilation step, we'll go with the `RMSprop` optimizer as usual. Since we ended our network with a single sigmoid unit, we will \n",
    "use binary crossentropy as our loss (as a reminder, check out the table in Chapter 4, section 5 for a cheatsheet on what loss function to \n",
    "use in various situations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    "As you already know by now, data should be formatted into appropriately pre-processed floating point tensors before being fed into our \n",
    "network. Currently, our data sits on a drive as JPEG files, so the steps for getting it into our network are roughly:\n",
    "\n",
    "* Read the picture files.\n",
    "* Decode the JPEG content to RBG grids of pixels.\n",
    "* Convert these into floating point tensors.\n",
    "* Rescale the pixel values (between 0 and 255) to the [0, 1] interval (as you know, neural networks prefer to deal with small input values).\n",
    "\n",
    "It may seem a bit daunting, but thankfully Keras has utilities to take care of these steps automatically. Keras has a module with image \n",
    "processing helper tools, located at `keras.preprocessing.image`. In particular, it contains the class `ImageDataGenerator` which allows to \n",
    "quickly set up Python generators that can automatically turn image files on disk into batches of pre-processed tensors. This is what we \n",
    "will use here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# All images will be rescaled by 1./255\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the output of one of these generators: it yields batches of 150x150 RGB images (shape `(20, 150, 150, 3)`) and binary \n",
    "labels (shape `(20,)`). 20 is the number of samples in each batch (the batch size). Note that the generator yields these batches \n",
    "indefinitely: it just loops endlessly over the images present in the target folder. For this reason, we need to `break` the iteration loop \n",
    "at some point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data batch shape: (20, 150, 150, 3)\n",
      "labels batch shape: (20,)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit our model to the data using the generator. We do it using the `fit_generator` method, the equivalent of `fit` for data generators \n",
    "like ours. It expects as first argument a Python generator that will yield batches of inputs and targets indefinitely, like ours does. \n",
    "Because the data is being generated endlessly, the generator needs to know example how many samples to draw from the generator before \n",
    "declaring an epoch over. This is the role of the `steps_per_epoch` argument: after having drawn `steps_per_epoch` batches from the \n",
    "generator, i.e. after having run for `steps_per_epoch` gradient descent steps, the fitting process will go to the next epoch. In our case, \n",
    "batches are 20-sample large, so it will take 100 batches until we see our target of 2000 samples.\n",
    "\n",
    "When using `fit_generator`, one may pass a `validation_data` argument, much like with the `fit` method. Importantly, this argument is \n",
    "allowed to be a data generator itself, but it could be a tuple of Numpy arrays as well. If you pass a generator as `validation_data`, then \n",
    "this generator is expected to yield batches of validation data endlessly, and thus you should also specify the `validation_steps` argument, \n",
    "which tells the process how many batches to draw from the validation generator for evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 9s - loss: 0.6898 - acc: 0.5285 - val_loss: 0.6724 - val_acc: 0.5950\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 8s - loss: 0.6543 - acc: 0.6340 - val_loss: 0.6565 - val_acc: 0.5950\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 8s - loss: 0.6143 - acc: 0.6690 - val_loss: 0.6116 - val_acc: 0.6650\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 8s - loss: 0.5626 - acc: 0.7125 - val_loss: 0.5774 - val_acc: 0.6970\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 8s - loss: 0.5266 - acc: 0.7335 - val_loss: 0.5726 - val_acc: 0.6960\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 8s - loss: 0.5007 - acc: 0.7550 - val_loss: 0.6075 - val_acc: 0.6580\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 8s - loss: 0.4723 - acc: 0.7840 - val_loss: 0.5516 - val_acc: 0.7060\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 8s - loss: 0.4521 - acc: 0.7875 - val_loss: 0.5724 - val_acc: 0.6980\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 8s - loss: 0.4163 - acc: 0.8095 - val_loss: 0.5653 - val_acc: 0.7140\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 8s - loss: 0.3988 - acc: 0.8185 - val_loss: 0.5508 - val_acc: 0.7180\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 8s - loss: 0.3694 - acc: 0.8385 - val_loss: 0.5712 - val_acc: 0.7300\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 8s - loss: 0.3385 - acc: 0.8465 - val_loss: 0.6097 - val_acc: 0.7110\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 8s - loss: 0.3229 - acc: 0.8565 - val_loss: 0.5827 - val_acc: 0.7150\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 8s - loss: 0.2962 - acc: 0.8720 - val_loss: 0.5928 - val_acc: 0.7190\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 8s - loss: 0.2684 - acc: 0.9005 - val_loss: 0.5921 - val_acc: 0.7190\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 8s - loss: 0.2509 - acc: 0.8980 - val_loss: 0.6148 - val_acc: 0.7250\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 8s - loss: 0.2221 - acc: 0.9110 - val_loss: 0.6487 - val_acc: 0.7010\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 8s - loss: 0.2021 - acc: 0.9250 - val_loss: 0.6185 - val_acc: 0.7300\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 8s - loss: 0.1824 - acc: 0.9310 - val_loss: 0.7713 - val_acc: 0.7020\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 8s - loss: 0.1579 - acc: 0.9425 - val_loss: 0.6657 - val_acc: 0.7260\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 8s - loss: 0.1355 - acc: 0.9550 - val_loss: 0.8077 - val_acc: 0.7040\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 8s - loss: 0.1247 - acc: 0.9545 - val_loss: 0.7726 - val_acc: 0.7080\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 8s - loss: 0.1111 - acc: 0.9585 - val_loss: 0.7387 - val_acc: 0.7220\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 8s - loss: 0.0932 - acc: 0.9710 - val_loss: 0.8196 - val_acc: 0.7050\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 8s - loss: 0.0707 - acc: 0.9790 - val_loss: 0.9012 - val_acc: 0.7190\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 8s - loss: 0.0625 - acc: 0.9855 - val_loss: 1.0437 - val_acc: 0.6970\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 8s - loss: 0.0611 - acc: 0.9820 - val_loss: 0.9831 - val_acc: 0.7060\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 8s - loss: 0.0488 - acc: 0.9865 - val_loss: 0.9721 - val_acc: 0.7310\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 8s - loss: 0.0375 - acc: 0.9915 - val_loss: 0.9987 - val_acc: 0.7100\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 8s - loss: 0.0387 - acc: 0.9895 - val_loss: 1.0139 - val_acc: 0.7240\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=30,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It is good practice to always save your models after training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_1.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the loss and accuracy of the model over the training and validation data during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55JOQ8tljD/d33olW1yhKSkLgPuCAUN9TT3WfN2/H+3PmuPfrt+MoJdnfy7Rp\nYWdVegTVvXsIjsnuUKMaNy5sz6OPdt+0Kdpnfvvb8Jl+/XZNN5aUhKO0zp3DOpx5pvuCBamrr4J7\niuXmlt/qy81Nd82qb+nSHSmW0kfLlu79+7tfeGFolb3yivvnn9fsMDJeSYn7Aw+EFvz3vuc+c2aY\nvn69+4cfuv/5z6GFc8opIXiY7ahbTo77wQeHHcMtt4Qd67RplbeeasOmTaGFZ+b+2GOVl12/PgSo\nNm3c586tet533hnWddSoysv98Y+h3AknVN7S/PJL927d3Js2DQGnpqZP33EE16OH+1tvlV9u61b3\nX/wibLPOnaPtXOLTF+3bh3Xcti0cQQ4eHJZ53301X4d4zz4b6njkkcn/jl55JbTK999/x+/4o49C\negxCY2jSpNTW113BPeXig0z8wyzdNUteSUlo3bZuHVrkP/+5+5tvhpN7dXUybPLkkDLYfXf3Tp12\n/k6bNg0nr845x/3220OO+9NPy09npMvmzTvyrw8/XH6ZbdtCmcaNQ9olipIS90suCfN99NHyyzz0\nkJed9CsvN5xozZodJ/J+85to9Ui0bJn7xRfvCLyPPBLWryoffOB+0EFh2dddV359i4rCEWFp+uKG\nG3Zt7W/ZEn4PpfOJmu+uzAsvhG0zcODOKa1kTJ8ejo5atgwtdAjnlh5/PHUNokQK7imWLS33hQtD\n2gVCC2z+/PTVZfly9x//2P38893vvjvku+fNixY06oMtW0JKoqKgec014b1ke1eU7hRyctxffXXn\n9+67z8vSWsnk5jdv3hF8opwM3L49pJNefdX91ltD8CoNvMme9N24MZychnAUMX16mJ6Yvhg6tPLf\n4/bt7tdeG8oOH16znf3LL4fAfuih4eiqJpYsce/bN5xTufnm6u8oolJwT7H6kHP/5BP3G28Mh7jJ\nBsDiYvcHHwx1btUqHPKmovXT0H33nftZZ4Xfw//+747pv/99mHb99dWb74YN4SRvixahC5172AGC\n+9lnh7RHsoqLdwTZwsJQ923bwg71pZfC/M8/Pyw38bd++uk1bwi88UY4Wmvc2P2mm3akL3r3jn5O\noKQkfM8QcuTJ5vNXrQppwSZN3H/wg9Sc4HQP32VddT1NaXAHhgDzgAXATeW8/xtgRuzxGbCuqnlm\nWnB3T29vmZKSHf8MEA6NL700/MNU9Y8+Z477YYeFz518cs36U8uutm0LLcnS/uuvvx5Otp12Ws0O\nzZctC7+4GnPxAAANr0lEQVSzvfcOPVHA/bzzanZkU1ISUiAQAm3TpjsH8f33D3n8n/wkHHG8915q\ng9bq1e7nnhuWtdde4ZxFdb6jp54KO4levcK5o8ps2RJ63px22o4T8UceWXddLFMtZcEdyAE+Bw4A\nmgIzgW6VlL8GeKKq+WZicE+n118PW+v++8MPdfjwcKhc2jXxggvcJ07cOae5dWsINk2buu+5Z/iH\nyOQLTOqzbdtCL5fScwZ9+oTWd03NmRO2L4QUVqryuGPHhjTIjTeGk9gffljz9EQyPvig5st7883w\nP9C5c/ie4pWUuP/rX+6XXebetm34/vbZJ6SVSk9+ZqpUBvfDgDfjXt8M3FxJ+X8Dx1c1XwX36EpK\n3AsKQisuPs+6eXMI6BdcsCMAtGwZAv/jj4fDXQgnolasSFv1G4ziYvfLLw+9gL76KnXznTo15PSV\nRtvVtGnhCGDPPUMw//zzcN3DgQeG3/7uu4cU1BtvZM65nKpEDe4WylbMzIYBQ9z9ktjrHwED3P3q\ncsrmAh8Andx9eznvjwRGAnTu3Lnf4sWLK112fbN1K1x5ZRg7pXnz8GjRouK/3brBgAE1X+7EiXD6\n6fCnP8HFF1dct0mTwrgVL74I33wD++4Lf/gDnHFGzesg0bmHcYWkbixcGEbYXLQojAdkBkcfDT/6\nURi3p1WrdNcwtcxsmrsXVFkuxcH9vwmB/ZqqFlxQUOBTp06tqli9cs018PvfhwGEvvsu3IRj06bw\nt/SRaPLkMLJcdZWUQH5+WM7cudA4wr2ziothxgw46CBo3br6yxbJFKtWwW23haG1Cwth//3TXaPa\nEzW4R7nN3lIg/qvqFJtWnuHAVRHmmXHGjw+B/brr4IEHyi9TUhJGj9u0KQy9evzxYQTAmTOhWbPq\nLfeFF8Lnn3oqWmCHUK6gyk0vkj06dAhHqbJDlCF/pwAHmVkXM2tKCOATEwuZ2SFAW+A/qa1i+s2d\nG4L04YfDr39dcblGjUJKpkOHMK756NHhhhV33lm95W7fHlojXbvCiBHVm4eINExVBnd3LwauBt4E\n5gIT3H22md1hZqfFFR0OjPeq8jwZZuPGkLdr3hwmTIAmTaJ/9vjj4aKLwg5hxozkl/3MMzBnDtx+\nO+TkJP95EWm4qsy515ZMyLm7h/zdM8+EGzUce2zy81i7NrS8O3aEDz+MnlopLg4nZHffHT7+OBwV\niIhEzbkrZFTikUfCPS7vuKN6gR2gbdtwy67p0yvO1Zfnqadg/nz45S8V2EUkeWq5V+Cjj+CII0Jq\n5ZVXah5gzzoLXnstnBz9/vcrL7t1a7htXbt2MGWKutWJyA5qudfA6tVw9tmw336hBZ2KlvPvfx96\nzFx6aehVU5k//zn02b3zTgV2EakeBfcEJSXh4oevv4bnnoM990zNfPfdF+6/P/R7f+yxistt2QK/\n+hUcdhgMGZKaZYtIw9Pgg/uYMZCXF1rneXlw7rnw+uvw4IOp7yt+0UUhd/+zn8GSJeWXeeyx8J5a\n7SJSEw065z5mDIwcueuVpYcfDu+/XzvBdeFC6NEjBPmJE3deRlERHHhgyLdPmqTgLiK7Us49glGj\nyh8y4Kuvai+wHnAA3HUX/O1voYtlvEceCekgtdpFpKYadMu9UaPQlz2RWdUnPWti+/ZwdLBwYbj6\ntX37cLFUly7Qt2/oUy8iUh613CPo3Dm56amSkxNGeFy/Hn7ykzDtd78LIzlWd6gCEZF4DTq433VX\nGFYgXvPmYXpt69EDbr455P3HjYN774Uf/jA1QwSLiDTo4F5YCD//+Y7XublhsK/CwrpZ/i23hCEG\nCgvDMAV33FE3yxWR7BdxpJPstWFDyL0vWwZ77123y95tt5CeOfxwGDo0jNsuIpIKDTq4u8PYsXDc\ncXUf2EsdemgYYqCqIQlERJLRoNMyH3wQLvM/77z01qNfv+y7FZiIpFeDDu5jx4bUyNCh6a6JiEhq\nNdjgXlwcbr5x6qmwxx7pro2ISGo12OD+zjuwcmX6UzIiIrWhwQb3sWOhdWs46aR010REJPUaZHDf\nvBleeCHcQKNZs3TXRkQk9bI2uCcO5TtmzI73Xn019G9XSkZEslVW9nNPHMp38eLwGsLVoGPHwj77\nwODBaauiiEitysqWe3lD+RYVhenr1oWW+7nnhgG8RESyUVYG9y+/rHj6Cy+EG1ArJSMi2Swrg3tl\nQ/mOHRvudvSDH9RtnURE6lJWBveKhvK94YbQv/2883SnIxHJblkZ3AsLw9C9ubkhiJcO5bt9exgs\nbMSIdNdQRKR2Najb7A0YANu2wfTpdbpYEZGU0W32EixYAB99pBOpItIwNJjgPm5cSNEMH57umoiI\n1L4GEdzdw4VNRx4JnTqluzYiIrWvQQT3GTNg3jylZESk4WgQwX3sWGjcOAwUJiLSEGR9cC8pgfHj\nYcgQaNcu3bUREakbkYK7mQ0xs3lmtsDMbqqgzDlmNsfMZpvZ2NRWs/refx+WLFFKRkQalipHhTSz\nHOBh4HhgCTDFzCa6+5y4MgcBNwMD3X2tme1VWxVO1tix4erU005Ld01EROpOlJZ7f2CBuy90963A\neOD0hDKXAg+7+1oAd1+Z2mpWz9at8OyzcMYZ0KJFumsjIlJ3ogT3jsBXca+XxKbF+z7wfTP7l5l9\nYGZDypuRmY00s6lmNnXVqlXVq3ES3noL1qxRSkZEGp5U3ayjMXAQMBjoBEw2s57uvi6+kLuPBkZD\nGH6gOgtaujTcfCOKRx8NJ1FPOKE6SxIRyVxRgvtSYP+4151i0+ItAT50923AF2b2GSHYT0lJLeOM\nHQs33hi9/JVXQpMmqa6FiEj9FiW4TwEOMrMuhKA+HEhMdLwEjAD+bGbtCWmahamsaKmzz4bevaOV\nNYPDDquNWoiI1G9VBnd3Lzazq4E3gRzgCXefbWZ3AFPdfWLsvRPMbA6wHfiZu6+ujQrn5YWHiIhU\nrEEN+Ssikuk05K+ISAOm4C4ikoUU3EVEspCCu4hIFlJwFxHJQgruIiJZSMFdRCQLKbiLiGQhBXcR\nkSyk4C4ikoUU3EVEspCCu4hIFlJwFxHJQgruIiJZSMFdRCQLKbiLiGQhBXcRkSyk4C4ikoUU3EVE\nspCCu4hIFlJwFxHJQgruIiJZKKOC+5gxkJcHjRqFv2PGpLtGIiL1U+N0VyCqMWNg5EgoKgqvFy8O\nrwEKC9NXLxGR+ihjWu6jRu0I7KWKisJ0ERHZWcYE9y+/TG66iEhDljHBvXPn5KaLiDRkGRPc77oL\nmjffeVrz5mG6iIjsLGOCe2EhjB4NublgFv6OHq2TqSIi5cmY3jIQArmCuYhI1TKm5S4iItEpuIuI\nZCEFdxGRLKTgLiKShRTcRUSykLl7ehZstgpYXM2Ptwe+SWF16oNsW6dsWx/IvnXKtvWB7Fun8tYn\n1907VPXBtAX3mjCzqe5ekO56pFK2rVO2rQ9k3zpl2/pA9q1TTdZHaRkRkSyk4C4ikoUyNbiPTncF\nakG2rVO2rQ9k3zpl2/pA9q1TtdcnI3PuIiJSuUxtuYuISCUU3EVEslDGBXczG2Jm88xsgZndlO76\n1JSZLTKzT8xshplNTXd9qsPMnjCzlWb2ady0Pc3s72Y2P/a3bTrrmIwK1ud2M1sa204zzOzkdNYx\nWWa2v5lNMrM5ZjbbzK6NTc/I7VTJ+mTsdjKzZmb2kZnNjK3TL2PTu5jZh7GY94yZNY00v0zKuZtZ\nDvAZcDywBJgCjHD3OWmtWA2Y2SKgwN0z9sILMzsS2Ag86e49YtP+F1jj7v8T2wm3dff/Tmc9o6pg\nfW4HNrr7femsW3WZ2b7Avu4+3cxaAdOAM4ALycDtVMn6nEOGbiczM6CFu280sybA+8C1wE+BF9x9\nvJn9EZjp7o9UNb9Ma7n3Bxa4+0J33wqMB05Pc50aPHefDKxJmHw68NfY878S/vEyQgXrk9Hcfbm7\nT4893wDMBTqSodupkvXJWB5sjL1sEns4cAzwXGx65G2UacG9I/BV3OslZPgGJWy8t8xsmpmNTHdl\nUmhvd18ee/41sHc6K5MiV5vZrFjaJiPSF+UxszygL/AhWbCdEtYHMng7mVmOmc0AVgJ/Bz4H1rl7\ncaxI5JiXacE9Gx3h7vnAScBVsZRAVvGQ+8uc/F/5HgEOBPoAy4H701ud6jGzlsDzwE/c/dv49zJx\nO5WzPhm9ndx9u7v3AToRMhWHVHdemRbclwL7x73uFJuWsdx9aezvSuBFwgbNBitiedHS/OjKNNen\nRtx9RewfrwR4jAzcTrE87vPAGHd/ITY5Y7dTeeuTDdsJwN3XAZOAw4A2ZlZ6S9TIMS/TgvsU4KDY\n2eOmwHBgYprrVG1m1iJ2MggzawGcAHxa+acyxkTgx7HnPwZeTmNdaqw0AMYMJcO2U+xk3Z+Aue7+\nQNxbGbmdKlqfTN5OZtbBzNrEnu9O6DgylxDkh8WKRd5GGdVbBiDWtelBIAd4wt3vSnOVqs3MDiC0\n1iHcrHxsJq6PmY0DBhOGJ10B3Aa8BEwAOhOGdj7H3TPiJGUF6zOYcKjvwCLgsrhcdb1nZkcA7wGf\nACWxybcQ8tQZt50qWZ8RZOh2MrNehBOmOYSG9wR3vyMWJ8YDewIfA+e7+3dVzi/TgruIiFQt09Iy\nIiISgYK7iEgWUnAXEclCCu4iIllIwV1EJAspuIuIZCEFdxGRLPT/bbDF0FCLfDUAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7aa0432b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aSPrlF3jgATeXy0cfBfearCyXb7/sMpdaNCZc4i64Q/7lkVWquMuNGRNu770H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\nY0wc+n+desdoPzWBogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7aa22d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "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": [
    "These plots are characteristic of overfitting. Our training accuracy increases linearly over time, until it reaches nearly 100%, while our \n",
    "validation accuracy stalls at 70-72%. Our validation loss reaches its minimum after only five epochs then stalls, while the training loss \n",
    "keeps decreasing linearly until it reaches nearly 0.\n",
    "\n",
    "Because we only have relatively few training samples (2000), overfitting is going to be our number one concern. You already know about a \n",
    "number of techniques that can help mitigate overfitting, such as dropout and weight decay (L2 regularization). We are now going to \n",
    "introduce a new one, specific to computer vision, and used almost universally when processing images with deep learning models: *data \n",
    "augmentation*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using data augmentation\n",
    "\n",
    "Overfitting is caused by having too few samples to learn from, rendering us unable to train a model able to generalize to new data. \n",
    "Given infinite data, our model would be exposed to every possible aspect of the data distribution at hand: we would never overfit. Data \n",
    "augmentation takes the approach of generating more training data from existing training samples, by \"augmenting\" the samples via a number \n",
    "of random transformations that yield believable-looking images. The goal is that at training time, our model would never see the exact same \n",
    "picture twice. This helps the model get exposed to more aspects of the data and generalize better.\n",
    "\n",
    "In Keras, this can be done by configuring a number of random transformations to be performed on the images read by our `ImageDataGenerator` \n",
    "instance. Let's get started with an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are just a few of the options available (for more, see the Keras documentation). Let's quickly go over what we just wrote:\n",
    "\n",
    "* `rotation_range` is a value in degrees (0-180), a range within which to randomly rotate pictures.\n",
    "* `width_shift` and `height_shift` are ranges (as a fraction of total width or height) within which to randomly translate pictures \n",
    "vertically or horizontally.\n",
    "* `shear_range` is for randomly applying shearing transformations.\n",
    "* `zoom_range` is for randomly zooming inside pictures.\n",
    "* `horizontal_flip` is for randomly flipping half of the images horizontally -- relevant when there are no assumptions of horizontal \n",
    "asymmetry (e.g. real-world pictures).\n",
    "* `fill_mode` is the strategy used for filling in newly created pixels, which can appear after a rotation or a width/height shift.\n",
    "\n",
    "Let's take a look at our augmented images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U9DLHNfGFPElw4zqZ4vkc1w9tuEynKL/7h0d4/4OPAAC3blzHG3s7+metyvwgjExU2vEt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\np78Tm9dFa7WNTQ9n5ikvHXNES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QCyAoYx9iGA5zjn24yxrwP4u4yxr0EEGAcPIp5wFJnp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\nyP8HsjhNFCM2YYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7aa005b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NoxmK3IOquq7DeOIZkZK8g3Up2o5KarXD4ZGHayRSgm97t6Y6OcGYwvLMKAoO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\nSxRLbhc/xQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7a9f96240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Swt8A8LcB/Jgx9k/Ud/8pJDP4XxljfwfAXQC/ddWDGHv6ZrxMcmgaE7Jq2wc0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xhyt67VasErcQ6DGDAtmQE2eetvDh1Tnsn3qlUEQQCTC4QuL87jKm/RvX79OnkKJ/t7qNaY12NZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7a9f24630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VyMV1mZlvQi7elpn5MnKxHUe4jTKo5XXchwL4D7XW3wb+SeDfVkp9G/hV4De01h8AvyH/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\nUpjJTGYyJTOlMJOZzGRK/j+IatmKbOuBTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7a9f6f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is module with image preprocessing utilities\n",
    "from keras.preprocessing import image\n",
    "\n",
    "fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\n",
    "\n",
    "# We pick one image to \"augment\"\n",
    "img_path = fnames[3]\n",
    "\n",
    "# Read the image and resize it\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "\n",
    "# Convert it to a Numpy array with shape (150, 150, 3)\n",
    "x = image.img_to_array(img)\n",
    "\n",
    "# Reshape it to (1, 150, 150, 3)\n",
    "x = x.reshape((1,) + x.shape)\n",
    "\n",
    "# The .flow() command below generates batches of randomly transformed images.\n",
    "# It will loop indefinitely, so we need to `break` the loop at some point!\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    plt.figure(i)\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we train a new network using this data augmentation configuration, our network will never see twice the same input. However, the inputs \n",
    "that it sees are still heavily intercorrelated, since they come from a small number of original images -- we cannot produce new information, \n",
    "we can only remix existing information. As such, this might not be quite enough to completely get rid of overfitting. To further fight \n",
    "overfitting, we will also add a Dropout layer to our model, right before the densely-connected classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train our network using data augmentation and dropout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/100\n",
      "100/100 [==============================] - 24s - loss: 0.6857 - acc: 0.5447 - val_loss: 0.6620 - val_acc: 0.5888\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 23s - loss: 0.6710 - acc: 0.5675 - val_loss: 0.6606 - val_acc: 0.5825\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 22s - loss: 0.6609 - acc: 0.5913 - val_loss: 0.6663 - val_acc: 0.5711.594 - ETA: 7s - loss: 0.6655 - ETA: 5s - los - ETA: 1s - loss: 0.6620 - acc: \n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 22s - loss: 0.6446 - acc: 0.6178 - val_loss: 0.6200 - val_acc: 0.6379\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 22s - loss: 0.6267 - acc: 0.6325 - val_loss: 0.6280 - val_acc: 0.5996\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 22s - loss: 0.6080 - acc: 0.6631 - val_loss: 0.6841 - val_acc: 0.5490\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 22s - loss: 0.5992 - acc: 0.6700 - val_loss: 0.5717 - val_acc: 0.6946\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 22s - loss: 0.5908 - acc: 0.6819 - val_loss: 0.5858 - val_acc: 0.6764\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 22s - loss: 0.5869 - acc: 0.6856 - val_loss: 0.5658 - val_acc: 0.6785\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 23s - loss: 0.5692 - acc: 0.6934 - val_loss: 0.5409 - val_acc: 0.7170\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 22s - loss: 0.5708 - acc: 0.6897 - val_loss: 0.5325 - val_acc: 0.7274\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 23s - loss: 0.5583 - acc: 0.7047 - val_loss: 0.5683 - val_acc: 0.7126\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 22s - loss: 0.5602 - acc: 0.7069 - val_loss: 0.6010 - val_acc: 0.6593\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 22s - loss: 0.5510 - acc: 0.7231 - val_loss: 0.5387 - val_acc: 0.7229\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 23s - loss: 0.5527 - acc: 0.7175 - val_loss: 0.5204 - val_acc: 0.7322\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 23s - loss: 0.5426 - acc: 0.7181 - val_loss: 0.5083 - val_acc: 0.7410\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 23s - loss: 0.5399 - acc: 0.7344 - val_loss: 0.5103 - val_acc: 0.7468\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 23s - loss: 0.5375 - acc: 0.7312 - val_loss: 0.5133 - val_acc: 0.7430\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 22s - loss: 0.5308 - acc: 0.7338 - val_loss: 0.4936 - val_acc: 0.7610\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 22s - loss: 0.5225 - acc: 0.7387 - val_loss: 0.4952 - val_acc: 0.7563\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 22s - loss: 0.5180 - acc: 0.7491 - val_loss: 0.4999 - val_acc: 0.7481\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 23s - loss: 0.5118 - acc: 0.7538 - val_loss: 0.4770 - val_acc: 0.7764\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 22s - loss: 0.5245 - acc: 0.7378 - val_loss: 0.4929 - val_acc: 0.7671\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 22s - loss: 0.5136 - acc: 0.7503 - val_loss: 0.4709 - val_acc: 0.7732\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 22s - loss: 0.4980 - acc: 0.7512 - val_loss: 0.4775 - val_acc: 0.7684\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 22s - loss: 0.4875 - acc: 0.7622 - val_loss: 0.4745 - val_acc: 0.7790\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 22s - loss: 0.5044 - acc: 0.7578 - val_loss: 0.5000 - val_acc: 0.7403\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 22s - loss: 0.4948 - acc: 0.7603 - val_loss: 0.4619 - val_acc: 0.7754\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 22s - loss: 0.4898 - acc: 0.7578 - val_loss: 0.4730 - val_acc: 0.7726\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 22s - loss: 0.4808 - acc: 0.7691 - val_loss: 0.4599 - val_acc: 0.7716\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 22s - loss: 0.4792 - acc: 0.7678 - val_loss: 0.4671 - val_acc: 0.7790\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 22s - loss: 0.4723 - acc: 0.7716 - val_loss: 0.4451 - val_acc: 0.7849\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 22s - loss: 0.4750 - acc: 0.7694 - val_loss: 0.4827 - val_acc: 0.7665\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 22s - loss: 0.4816 - acc: 0.7647 - val_loss: 0.4953 - val_acc: 0.7513\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 22s - loss: 0.4598 - acc: 0.7813 - val_loss: 0.4426 - val_acc: 0.7843\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 23s - loss: 0.4643 - acc: 0.7781 - val_loss: 0.4692 - val_acc: 0.7680\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 22s - loss: 0.4675 - acc: 0.7778 - val_loss: 0.4849 - val_acc: 0.7633\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 22s - loss: 0.4658 - acc: 0.7737 - val_loss: 0.4632 - val_acc: 0.7760\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 22s - loss: 0.4581 - acc: 0.7866 - val_loss: 0.4489 - val_acc: 0.7880\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 23s - loss: 0.4485 - acc: 0.7856 - val_loss: 0.4479 - val_acc: 0.7931\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 22s - loss: 0.4637 - acc: 0.7759 - val_loss: 0.4453 - val_acc: 0.7990\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 22s - loss: 0.4528 - acc: 0.7841 - val_loss: 0.4758 - val_acc: 0.7868\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 22s - loss: 0.4481 - acc: 0.7856 - val_loss: 0.4472 - val_acc: 0.7893\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 22s - loss: 0.4540 - acc: 0.7953 - val_loss: 0.4366 - val_acc: 0.7867A: 6s - loss: 0.4523 - acc: - ETA: \n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 22s - loss: 0.4411 - acc: 0.7919 - val_loss: 0.4708 - val_acc: 0.7697\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 22s - loss: 0.4493 - acc: 0.7869 - val_loss: 0.4366 - val_acc: 0.7829\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 22s - loss: 0.4436 - acc: 0.7916 - val_loss: 0.4307 - val_acc: 0.8090\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 22s - loss: 0.4391 - acc: 0.7928 - val_loss: 0.4203 - val_acc: 0.8065\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 23s - loss: 0.4284 - acc: 0.8053 - val_loss: 0.4422 - val_acc: 0.8041\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 22s - loss: 0.4492 - acc: 0.7906 - val_loss: 0.5422 - val_acc: 0.7437\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 22s - loss: 0.4292 - acc: 0.7953 - val_loss: 0.4446 - val_acc: 0.7932\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 22s - loss: 0.4275 - acc: 0.8037 - val_loss: 0.4287 - val_acc: 0.7989\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 22s - loss: 0.4297 - acc: 0.7975 - val_loss: 0.4091 - val_acc: 0.8046\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 23s - loss: 0.4198 - acc: 0.7978 - val_loss: 0.4413 - val_acc: 0.7964\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 23s - loss: 0.4195 - acc: 0.8019 - val_loss: 0.4265 - val_acc: 0.8001\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 22s - loss: 0.4081 - acc: 0.8056 - val_loss: 0.4374 - val_acc: 0.7957\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 22s - loss: 0.4214 - acc: 0.8006 - val_loss: 0.4228 - val_acc: 0.8020\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 22s - loss: 0.4050 - acc: 0.8097 - val_loss: 0.4332 - val_acc: 0.7900\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 22s - loss: 0.4162 - acc: 0.8134 - val_loss: 0.4088 - val_acc: 0.8099\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 22s - loss: 0.4042 - acc: 0.8141 - val_loss: 0.4436 - val_acc: 0.7957\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 23s - loss: 0.4016 - acc: 0.8212 - val_loss: 0.4082 - val_acc: 0.8189\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 22s - loss: 0.4167 - acc: 0.8097 - val_loss: 0.3935 - val_acc: 0.8236\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 23s - loss: 0.4052 - acc: 0.8138 - val_loss: 0.4509 - val_acc: 0.7824\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 22s - loss: 0.4011 - acc: 0.8209 - val_loss: 0.3874 - val_acc: 0.8299\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 22s - loss: 0.3966 - acc: 0.8131 - val_loss: 0.4328 - val_acc: 0.7970\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 23s - loss: 0.3889 - acc: 0.8163 - val_loss: 0.4766 - val_acc: 0.7719\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 22s - loss: 0.3960 - acc: 0.8163 - val_loss: 0.3859 - val_acc: 0.8325\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 22s - loss: 0.3893 - acc: 0.8231 - val_loss: 0.4172 - val_acc: 0.8128\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 23s - loss: 0.3828 - acc: 0.8219 - val_loss: 0.4023 - val_acc: 0.8215 loss: 0.3881 - acc:\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 22s - loss: 0.3909 - acc: 0.8275 - val_loss: 0.4275 - val_acc: 0.8008\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 22s - loss: 0.3826 - acc: 0.8244 - val_loss: 0.3815 - val_acc: 0.8177\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 22s - loss: 0.3837 - acc: 0.8272 - val_loss: 0.4040 - val_acc: 0.8287\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 23s - loss: 0.3812 - acc: 0.8222 - val_loss: 0.4039 - val_acc: 0.8058\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 22s - loss: 0.3829 - acc: 0.8281 - val_loss: 0.4204 - val_acc: 0.8015\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 22s - loss: 0.3708 - acc: 0.8350 - val_loss: 0.4083 - val_acc: 0.8204\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 22s - loss: 0.3831 - acc: 0.8216 - val_loss: 0.3899 - val_acc: 0.8215\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 22s - loss: 0.3695 - acc: 0.8375 - val_loss: 0.3963 - val_acc: 0.8293\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 22s - loss: 0.3809 - acc: 0.8234 - val_loss: 0.4046 - val_acc: 0.8236\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 22s - loss: 0.3637 - acc: 0.8362 - val_loss: 0.3990 - val_acc: 0.8325\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 22s - loss: 0.3596 - acc: 0.8400 - val_loss: 0.3925 - val_acc: 0.8350\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 22s - loss: 0.3762 - acc: 0.8303 - val_loss: 0.3813 - val_acc: 0.8331\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 23s - loss: 0.3672 - acc: 0.8347 - val_loss: 0.4539 - val_acc: 0.7931\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 22s - loss: 0.3636 - acc: 0.8353 - val_loss: 0.3988 - val_acc: 0.8261\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 22s - loss: 0.3503 - acc: 0.8453 - val_loss: 0.3987 - val_acc: 0.8325\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 22s - loss: 0.3586 - acc: 0.8437 - val_loss: 0.3842 - val_acc: 0.8306\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 22s - loss: 0.3624 - acc: 0.8353 - val_loss: 0.4100 - val_acc: 0.8196.834\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 22s - loss: 0.3596 - acc: 0.8422 - val_loss: 0.3814 - val_acc: 0.8331\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 22s - loss: 0.3487 - acc: 0.8494 - val_loss: 0.4266 - val_acc: 0.8109\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 22s - loss: 0.3598 - acc: 0.8400 - val_loss: 0.4076 - val_acc: 0.8325\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 22s - loss: 0.3510 - acc: 0.8450 - val_loss: 0.3762 - val_acc: 0.8388\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 22s - loss: 0.3458 - acc: 0.8450 - val_loss: 0.4684 - val_acc: 0.8015\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 22s - loss: 0.3454 - acc: 0.8441 - val_loss: 0.4017 - val_acc: 0.8204\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 22s - loss: 0.3402 - acc: 0.8487 - val_loss: 0.3928 - val_acc: 0.8204\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 22s - loss: 0.3569 - acc: 0.8394 - val_loss: 0.4005 - val_acc: 0.8338\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 22s - loss: 0.3425 - acc: 0.8494 - val_loss: 0.3641 - val_acc: 0.8439\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 22s - loss: 0.3335 - acc: 0.8531 - val_loss: 0.3811 - val_acc: 0.8363\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 22s - loss: 0.3204 - acc: 0.8581 - val_loss: 0.3786 - val_acc: 0.8331\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 22s - loss: 0.3250 - acc: 0.8606 - val_loss: 0.4205 - val_acc: 0.8236\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 22s - loss: 0.3255 - acc: 0.8581 - val_loss: 0.3518 - val_acc: 0.8460\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 22s - loss: 0.3280 - acc: 0.8491 - val_loss: 0.3776 - val_acc: 0.8439\n"
     ]
    }
   ],
   "source": [
    "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",
    "\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=32,\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=32,\n",
    "        class_mode='binary')\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": "markdown",
   "metadata": {},
   "source": [
    "Let's save our model -- we will be using it in the section on convnet visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot our results again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7aa096438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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APBja1lVVC4FzgDtE5JDEnVV1mqoWqmphhw4dstSk6uOoo2wZ9uuvXWtumj59\nytefPLn8dIthRMy37526juNUN5mI/sdA2HLvHCvbi6puVtWdsdV7gSGhbR/HliuBl4FBVWhvraCg\nAPbbr6xff+lSW/btW75+URGMGxdfz8+3D5jgB24i79R1HKe6yUT05wI9RaS7iDQBxgFlonBE5MDQ\n6unAklh5WxFpGvveHhgOLM5Gw2uShg3hiCPKWvpLltgyytIHOOUUWy5aZLNybdpkfv+ofPznnuth\nnbWJpUvh5z8vf68cpy6SVvRVtQS4HJiDifljqrpIRCaJSBCNc4WILBKRd4ErgPNj5X2B4lj5S8AU\nVa3zog/m4nnvvXg6hqVLbWrE/fePrh81QXqqzlsP66w9PPUU3HqrT4Tj5AYZ+fRVdbaq9lLVQ1R1\ncqzsBlWdFft+jar2U9XDVXWUqi6Nlb+uqv1j5f1V9b7qu5R9y/nnW7rl73/fOm6XLDHXTpBxM5Fg\ngvT16+NlFem89bDOmmPrVltu21az7XCcbOAjcitJ165w223w0kvwhz+YpZ/MtQOWarlFC3j11XjZ\n5MkWy58pHtZZMwRiH4i/49RlXPSrwEUX2dy5P/+5vfpHdeIGNGsGJ50ETz8dn0ylqMhi+dMN3grw\nsM6aIRB7F30nF3DRrwIicO+9JuiQ2tIHOOMMWLfOErYFFBVZv8BDD6W2+j2ss+ZwS9/JJVz0q0in\nTvCnP0GbNjBkSOq6p55qkT9PP11+W9jqF/GwztqEW/pOLuGinwW++1347DM48MDU9dq1s3l2n3oq\nentg9ZeWpg/rTNWpG0zT6OGe2cEtfSeXcNHPEsmidhI54wxYvBiWL8+sfrLO22Sunhkz4tM0erhn\ndnBL38klXPT3MWNiqeqiXDxRpOq8jRL0666zN4EwHu5ZNQJL30M2nVzARX8f07UrDByY3MWTSLqw\nzkRBT/Zm4OGelUPVLX0nt3DRrwHGjLEUDp9+mr5uJmGdYVdPsjcDD/esHF9/HZ/32EXfyQVc9GuA\nM84wC/LJJzOrH3TwphP+iRMtx0/im0Fenr0xOBUnLPQu+k4u4KJfAxx+uH3uvDM+UCsTMnH1zJ5d\nNvQzPJmLU3HCfnwXfScXcNGvAUTgqqssX88zz2S+X6aunuuuswdEaaktr7vOwzcrSyD0zZu76Du5\ngWgtyxdbWFioxeEhqzlKSQn06mVZOf/738xDPgO6dTOBT0ZeHkyYYHP0hqN58vLc8q8I//oXnHAC\n9O4NW7bIOi+IAAAgAElEQVTAhg013SLHiUZE5sUmrEqJW/o1RKNGlrPnzTfhlVfKbispgfvvtyRt\nV1wRvX8mrp5p0zx8s6oE1v3BB3vIppMbuOjXIOefb5b+zTfb+ldfwcMPQ//+cOGFZlU+9BDs2VN+\n30xcPVH7gYdvVoRA6Lt0sfuze3fNtsdxqoqLfg3SvDlceSU89xx85zv2ACgqMlfP3/8ODzwAn39u\nbwNRpIvqadgwutzDNzMnbOmH1x2nruKiX8P84AeWk+eFFyyHz7/+BQsXwplnwoknmnA/91zqY0S5\nevLyLIQz0/BNz9cTTWDpu+g7uYKLfg3Tpo3l4dmwwdI0H3dc3EJv2xaOPNLCMFORmKEzCNP8wx8y\nC9/0fD3J2brVHpRBxlMXfaeuk5Hoi8hoEVkmIitE5OqI7eeLyEYRmR/7XBTaNkFElsc+E7LZ+Fyh\nXbt4Tv5ETjkF3n47fdRIOEPnqlVxYU9WHsbz9SRn2zbYbz9o3drWXfSduk5a0ReRhsBU4GSgABgv\nIgURVR9V1YGxz72xfdsBvwSGAUOBX4pI26y1vh5w8sm2fP75qh8rmQvH8/UkZ+tWE/z99rN1j+Bx\n6jqZWPpDgRWqulJVdwEzgTEZHv8k4AVV/UxVPwdeAEZXrqn1k8MPtzz9US4eVXMJ/fa36Y+TyoXj\n+XqS45a+k2tkIvqdgI9C62tjZYl8W0QWiMjjInJwRfYVkYkiUiwixRs3bsyw6fUDEXPx/N//xRN/\nAezaZaJ98cVw/fXpxSiVC2fyZGjatPx5fXrGuKXvou/kCtnqyH0G6KaqAzBr/sGK7Kyq01S1UFUL\nO3TokKUm5Q4nn2xi89//2vpHH8Hxx5uVf/rp5q9/9dXUx0jlwikqsgneA7I5PeO2bfDxx5Xbtzbg\nlr6Ta2Qi+h8DB4fWO8fK9qKqm1V1Z2z1XmBIpvs66TnhBBvBe9VVMHiwuV3mzYOZM+3TpAm8/HLq\nY6Rz4WzfbstmzaKnZzz33MpZ/UVF1v66SmDpN2liv42LvlPXyUT05wI9RaS7iDQBxgGzwhVEJDw7\n7OnAktj3OcCJItI21oF7YqzMqQCtW8M3vwlvvQUtW8JvfgMLFlhcf/PmcMQR8O9/l91n1ix7QHz1\nla1HxfI3bmxiLwIvvmhlX3+dvB0VtfoXLYJnn4UVKyqWTbQ2EVj6YPfBRd+p66QVfVUtAS7HxHoJ\n8JiqLhKRSSJyeqzaFSKySETeBa4Azo/t+xlwE/bgmAtMipU5FeTvf7fJ1195Ba65Bg49NL5txAiz\n/MORJbffDu+8E3cJJcby5+fbcvNm266aWdK3ioRy3nabLUtKMpswprZRWgpffOGi7+QWGfn0VXW2\nqvZS1UNUdXKs7AZVnRX7fo2q9lPVw1V1lKouDe17v6oeGvs8UD2Xkfs0axYXn0RGjjSBeu01W//o\no7jlH1jwUDZmv2VL6wwOE7h1ko0ZCMgklDPIG9S9u63XRb/+9u32mwT+/NatPWTTqfv4iNwc4Igj\nzFUT+PUfecTEqmvXsqIfJpVwX3xx6kRumYRy/v73lpzsN7+x9bVr0+9T2wis+uBhu99+buk7dR8X\n/RwgLw+GDYtb9zNm2IOgqMj6Ab74ovw+qYR7//3tjeChh8r3A2QSyvnll5YCYsgQSx8N8P3vJ6+/\nZg3cdFPt8/sHVn3Y0nfRd+o6Lvo5wogRUFxsPvwFC0zwjzvO0isn5uuH5Ena8vPh/fdtPTF9c2Io\n53nnWVniA+Avf7HsoAsXxi38zz5L3gn829/CDTfA3LlV+QWyT6Kl76Lv5AIu+jnCyJEm8D/8oSVs\nO/tsOOooG3QV5eIJBD0YFtGxo60PHBgX/aBekL45MZQzWSz/3/9u7qadO8vWj+oE3rULHn3Uvv/r\nX5W58urDLX0nF3HRzxGOPNJi+efNg5NOMhdN8+Ym/Mn8+kVFcNFFtt/KlbbeqxcsW1Ze4NN13oYF\nfdmy5JONJB5n9mx7K2jWrPaJfpSlv3178slpHKcu4KKfI7RoAUOH2vdwJs3jjoP58+OhmevXW8jn\ne+/Z+n//a/l9AldPr142F2xQPyCTzts1a+C++1JH6iQeZ/p0e0BNnGjRR8G4gtpAlKUfLq9r+MPK\nARf9nOLUU80nPyaUDu+442z58stmpZ56KkyZAgMG2MNh7lx7Gwjo1cuWYRcPpJ+TF+zt4KKLkm9v\n3rzsBC6ff26Dt8aPt7eTnTvjYaeZogq/+EX8IZZNoix9qJuiv3ix/f5Ll6av6+Q2Lvo5xFVXmZum\nRYt42Te+YesvvGDiumCBzcP7v/8LTz5pkTZHHhmvH4j+smXxso0bbfRvYqduRbnjjrJvIX/7m/n0\nzzsPjj3W3EwVdfGsWgW//rW1Ldts22bX2bKlrQfiXxf9+sXF5nJLfJg79Q8X/RyiYcPyA7gaNzZB\nnTbNrOrf/97Ef8oU+OADuOce+Pa34/W7dTPxDcRh3Tro0cOia4JOXVVzy6SK5Y9i+PCy69OnQ9++\nli6iZUsLO62o6L/7ri3ffrti+2XC1q3QqpXNPwB1O+naypW23LKlZtvh1Dwu+vWA4483of7Zz+DS\nS+PlBx5o7pgmTeJljRrBIYfERX/KFHML/fGP9lYQEDwAMrH4O3a0ZXiA1ocfWmbQc8+NH2P//c3d\nFBUGmoz58235zjupfdbf/765gSrCtm1xoYe6LfoffmhLF33HRb8ecPHF5tK5+ebM6vfqZaL/0Ufw\n5z+bi2jLFhuslUi6Dt68PLg6NsFm0ME7Y4ZZ9wC/+x20b29C/+ST8f0yTe4WiP6OHWVdUmFU4Ykn\n4OmnUx8rka1by745uejXL1avhp/+NPc6wF306wH77WcunQYZ3u1evWyy9l//2gTzb38zkb7rrngo\n544d1gE8cGDyDt5gIvYf/MDW166Nz+AViE9UpFBAsuRu4Wkfn30WOne28nnzoo/z8cdmtS9bVnYi\nmnS4pV+/efZZSxq4YkVNtyS7uOg75ejd2yJp7rnH3D9du8IVV1gEyIsvmvBfcomFey5cWLaDF2DC\nBKsTTMTepIm5btaujZ7BKxWJcf2J0z7u2QOffGLnSCb6ixfbctcu68fIlFyx9HfujL9luehnTvBb\n1cUMsalw0XfKEUTwNGkC115r37/7XRu9e9dd5t9/6CHo1886CIcPN4F/6SWrG47QCejc2YSnopOt\nq5b170c9NHbvNvdQMtFftCj+PXgAZEKipd+smXWM17WQzeABCS76FSH4rXJtBlcXfaccffqY6+SS\nS+Kuk2bNbP2ZZ+BHP7J5ex95xLYFYh90/vbuXf6YqhY2mjjSNxPC/v1kD42dO60zNypp2+LFcYu9\nIqKfaOmL1M1Mm4Frp3FjF/2K4Ja+U2/o2BFef718x+9ll1lYaOfOZukfdpi5bYI0D8uW2QCg4EER\nMGOGuYGSpWYIkywaKPDvJ+s4zs+36KKoOPRFi2DQIHNBha3+dCRa+lA38+8Eon/YYS76FSG4zy76\nTr1g2LDyk6kcdJAJ/MsvQ9u2JtCjRpmlr2qi37Nn+Q7j665L3oGanx+fxatrV4vdT8aaNdEjg8Mz\ngN11ly2Dzl4ReOMNC0Xt1y9zS3/3bksJkTjuoa6KfpMmUFDgol8RctW906imG+DULY45puz6ccdZ\nlszly83KHjiw/D7JXDIisGlT+fJrr43ep0uXeH9BuE7YZXTPPbZ88MG471/V0kt/85uWhmDPHntj\nSUVi3p2Auir6XbtCu3Yu+hWhXrt3RGS0iCwTkRUicnWKet8WERWRwth6NxH5SkTmxz5/ylbDndpB\nkNvn+eetUzfoBA6TzCWTrPw3vzHLPExeXjxvT1GRhdNFUVJi0URRnb1vvmm+/2B0aioCYV+0KB4e\n2q2bTUhT10R/5UqbtrJNG2t7bZusJlP27IHvfMfu476g3oq+iDQEpgInAwXAeBEpiKjXCrgSSLwl\nH6jqwNjn0sT9nLrNIYeYD//ee+2fMqoTd/Lk8q6iZs3KJl8LU1RkYwQCuna1MNDrrouL7x/+kLxN\nyQbTBC6g4cPjx0k2+Cuw9P/yl3j0y+rVNhhs3brk564J9uyx5HXJ+PDDuOiXltoI67rIhg3w+OOW\njntfUJ99+kOBFaq6UlV3ATOBMRH1bgJuBr7OYvucWo6IWfsLF9p6lKVfVAR33x1fb9DA3DBRoZ0B\nV11lieD69LEHwIMPlhXfe++teFvbtbPlxo3x4yQb9Rv8wydOHp9OYGuC++6z/EiJk9aAPbw++8y2\nt2ljZXXVxRO4AlOl7s4Wqrnr089E9DsBH4XW18bK9iIig4GDVfUfEft3F5F3ROTfInJMxHZEZKKI\nFItI8cZc+4XrAYGLB6JFH2yQV9Ap+s1vWs6ddFx8sfngJ0wo764pKTG/fLp0zwF5edGRQTt2WFsS\nrf5UsfilpZULPa0uFi0ygfrkk/LbgsidwNKHui/6++JN6+uv7YHftKmdN5dSMVQ5ekdEGgC3AT+N\n2Lwe6KKqg4CfAA+LyH6JlVR1mqoWqmphh2D+PqfOMGqULTt0sKieZAShnMOGZXbcCRPMik3mg96z\np/xo4CiaNrVQ0mTpHqC81Z/Ob1+bXCSBCLroZ4/g/h96qD3gU/3t1DUyEf2PgYND651jZQGtgMOA\nl0VkFXAEMEtEClV1p6puBlDVecAHQBJb0KmrdOli/xx9+6au1yn2fhjM8JWOBg0sO2YyUW/Xruwc\nvskoLc3snzac6yf4p2/evGydICPpvhLOrVujI5zCrF9vyyjfcy6K/r5w7wS/UfDmmkt+/UxEfy7Q\nU0S6i0gTYBwwK9ioqltVtb2qdlPVbsAbwOmqWiwiHWIdwYhID6AnkEHshFPXePRRmDo1dZ3A0s9U\n9AOiYvObNrVkWOnqQGaDwgJWrzZXzyuv2PrUqfZACcYRBBlDg1HI1c3FF8Npp6WuE4h+lKW/cqW5\n1dq1yx3R37Qpuv8imwS/Uc+etswlr3Na0VfVEuByYA6wBHhMVReJyCQROT3N7scCC0RkPvA4cKmq\nflbVRju1j8GDbcRnKr77XbjySnMDVYSiorgbJxDf++4z909UnYDCwoqdJ2D1avj7383KP/98e5Mo\nLbXljTemz/W/ebN1nGbjwVBcnHquANXUoh9E7ojkjuiDRfJUJ4min0uWfkaDs1R1NjA7oeyGJHVH\nhr4/ATxRhfY5OcRJJ9mnMhQVpY72Cdf54gsLJX3tNXMRRfUJBG8FyTJ+lpRYrprEzl8ROOccm1xm\nwwY44IDy+771lont7bfH+zsqw5dfxmcqW7XKrimRrVvjk8knE/3ARRF0pOeC6H/8ccVnbqsIgXuv\nvrp3HKdO0aoVXH+9fT/00Oi0DQMHpu8EDsQ0kaIie5A8+mj09iB8dfbsshbpzp0werTFmmfCsmXx\nKKElS6LrBFY+lBf94GHRvbutN2pkv01dFv1gvuLq7swNfqMePcxwqFfuHcepi1xyiXUsf/vb5V1D\n3/iGzanbqVPqTuADD4wuLyiwh0YyF8/ChTYZ/Z49ZXMJ3XcfzJljI44zISz0S5dG1wlEv2HD8qL/\n6af2JhOIPpiLpy6L/oAB9n1fiX67dpYbyi19x6nlNG0KCxZYB28Q4RP45Z991iy4b33LJoKJ6gSG\n8llGwzN2rVpl8/kuX15+26OPmitm+HC4/36zuHfssEFmzZubj/6dd8oee/p0S2QXZvFiE/P8/OSW\nfiB+ffqUF/0g3USPHvGyui76vXpZBFV1R/Bs3WruvebNLZOsi77j1AEaNYoekNWhA/zzn2bJn3yy\nvRGEXT0NGpiwTJgQ77RNnLErHNLXvr2Flgbbdu82wS4oMAv9jTds4pn162HmTHsg3XdfvD1LlliH\n8Q0JvWSLF1tHYv/+6d07AweWF6ZVq2zZrVu8rK6KvqqJfocOlu11X1j6bdrY34+LvuPkAAceCP/6\nl2XNPOMMOOssE8nf/c7eCHbtKpuq4cork3f6bt5cPl1DSYkloWvRAu680zp+TzwRTj/dXE4zZsT7\nDK691s751ltlQxGXLLEHR9++9j1qFPD69XaOQw+1doRTWEeJfuvWdVP0d+ywUbLt2+870Q8yrO6/\nv/v0HScn6NIF/vpX+OgjE2aA3/62fL0dOyo3InPtWssK+eijZqXedJOVX3ihicqTT1qE0VNPwRFH\nmOC//bbV2bnTJuTu29c+ydIsrFtnD7COHe2hEBanVavMMm7RIl5WEUv/qacyy0i6Lwgid9q3t76Y\n6nbvBJY+uKXvODnFiBE2+Om3vzVh+SyLo0i6dCk7i9jZZ5uFP3KkifGFF8LRR5vf/uyzrc5rr9ly\n+XLrCA4sfYh28axfHxd9KPtgWLWqrJUPmYv+9u0wdizcemsGF7oPCIt+Niz9226zSKpkbN0aF/0O\nHew3S3ybq6u46Dv1nilTTOQmTSo/61dAfn7myd3AfMGrV5dNER24ii6/3ETk61g+2j17LMS0Y0d4\n9VUrCwS+oMA6acNlYdavNxHcf39bz0T0M8mpP3eutStwEdU0iaL/xRf2qSz//KdFUgUd8YkkWvqQ\nOy4eF32n3lNQYFlA777bxDDIrxOQl2fun3Bnb2IHcaNG9mAISJaFc8cOO05iaogdO+zB89prtu/i\nxXaO3r3NndGqVcUs/dLSeEqJMJnm1H/9dVuuXp263r4iUfShatZ+8DBLNhlPok8fXPQdJ6e48ca4\n7/t3vysb1z9tWny0bzBCdvr0svH9F10UT26WjmQpFb780sStc2drT8OGlg5CxKz9RNH/4gsT7yjR\n/+QT6xeIEn1I7+L5739tGVxvJvzzn2WjkrJJok8fKi/6waA1gGeeia4TZelny6//pz9ZEEFN4aLv\nOJhwTpliHa9XXFE2rj8q/UPwANizxwR3yxZ4773MzpVsft7AsgzErKQknu45iOCB+JiAIK3Chx/a\nm0CzZnFhiorcgcxEv7TURL9x44p1Yv/oRzYorjpcQps2meutTZuqW/qffmqRU/n58J//lP8tdu+2\n6w779IP9qoqqJe374x+rfqzK4qLvODEuvxwee6xi+zRoYLH+c+bYVIrpyMszIU/sH8jLi34YBOme\n+/Y1kbvnnvh4gYAHHoCHH7aHT2DpZyr6O3ZYZ3KQVRRsgvvPPrPrCh8rFe+9Z5O57NkD/+//pa9f\nUTZtMpFu0CAu+pWN4Amu56KL7ME6Z07Z7UHenepw73z+uR2/Jl1FLvqOU0VOPdX+me+5x6ztxBz8\ngf8/cBX94Q/lU0NMm5Z8GsY1a+IRPDfcUH68wM6d9mCIEv3EFBOJov/OO9aP8LvfxesErp3x48se\nKxWPPGIPrdNOMxdPtkVt0yZz7YC91bRqVXlLP7iecePsmIl+/eC3CX6r1q3trScblv4HH9jSRd9x\n6jDf/KYJ3jvv2MjYe+4pK+jTp9tr/eTJ8cndr7vO1sMupC5doo/fpUtc9JOlFF6zxizSsOgnxuhD\nedFfsMCWs2fHR/e+/rrVCzKipuvMVbWRxscfb6krvv667JzI2SAs+lC1sM1A9A85BE45xa49PKgt\nsPSD3yqbo3Jd9B0nB2jd2lwkYCkTEnP9FBWVT+MQNSn75MnxiV8C8vKsvEcPiyrar9xko0aXLuUt\n/UTXDkSLfrNm1tYgOdx//2uT0rdta9eWztIvLrZBXOPG2cPpjDPg97+vWkhlIlGiXxX3Tn6+vS18\n61vmygrebqC8pQ/2AM2G6AeD3TZvrrl5d130HScLnHKKLfv3j95+3XXl3TLh6RnBHg7hDr5w5FCj\nRpaHJypVdPBg6NjRLMjgYRMl+oGfOhC2d9+1OYuPPtqSw23ZYr75o46KtyGdpT9zprk/zjzT1q+6\nKu7uyhaVtfQvugh+8IOyZeHf5qST7LcNu3iC3yb4rSB7qRgCS181uwMBK4KLvuNkgbFjTSCPOy56\n+5o1ycvDGTp/9StLH/yd75R1B3XrZq6azZvhz3+O++obNbI+hPPOswfGnj0mkFEx+kH9li1N2EpL\nLQ30gAFwwQWWv//2263ekUfaslu31JZ+MK/AySfHLeNhw2zymDvuSD8ILBOCZGth0e/UyUQ/VThp\naSn87W8W9hquFxb9/fazUdnh0M0oSz/b7h2oORePi77jZIEePUxM+vWL3p7MX9+uXXm3z/Ll5lpJ\nLH/rLVv+7Gf2QDjoINu2ebMtg47gjh2tczeZSAWpGFatsjj/4CHTooX55Bs0iM9jHFj6ycT1tdfM\nzTJuXNnyCy6wnEbFxal+tczmL962zXzuiZb+rl2pw0mXLLF9P/kk3l8RxOiHH4gnnGB1A7FP9OlD\n9kR/5cp4ao6ayueTkeiLyGgRWSYiK0Tk6hT1vi0iKiKFobJrYvstE5FKTpbnOHWbqJz9yaZs/Oor\ni4BJltXzk0/sgbB+fWq/8MyZ0RO9tGljD4h337X1AQPMv/2d79jDon9/WwcTx23bysay33abCeWJ\nJ5r7pHnz8pO3n3qqdW4//XTy9t13n7XliTQTqoYHZgVkEqv/xhvx7/Pm2fKTT6yjOSz6hx9uy2DG\nsy1b7MEXzNIFJvpffpn8nmTCzp2WhG/YMFuvtZa+iDQEpgInAwXAeBEpiKjXCrgSeDNUVgCMA/oB\no4E/xI7nOPWKqMndp01L7tdN18m3Y0f6kbJBKGcigaW/YIG1JZjQ/vvft2Xgz4e4Gynw6+/ZYy6o\nZcvsYdC6tZ0jLJBgbzDHHJNc9FeutHTVu3fDd79rbphkVEX099vPrjEQ/ajxC8FsXEEk05Yttl84\nD1MwQKsqQv3hh3bPjjii6seqCplY+kOBFaq6UlV3ATOBMRH1bgJuBr4OlY0BZqrqTlX9EFgRO57j\n1DuionqSuX2SjdqtKIFvP2zxh0W/Z8/4G8fRR9s4gMsui9cNxDEQy3feMbG/5RYT1bfein6wAIwZ\nY53CYT822PVfcIFd4/z51n8wfnzyOYejRD9IxZAqgueNN2z2sj594imrg+sITyF50EH2kApEP5xh\nMyBIevfww2XLV6+2/ovFi5O3IyCI3AlcZ7VZ9DsBH4XW18bK9iIig4GDVfUfFd03tv9EESkWkeKN\nuZLVyHEyIJnbJ2rUbhSJIZ5RJIaHfv65uTL+/ndzNwTlImbFhyOQEi39l16y5ciR6c87JmYaJlr7\nd91lI4DvvNOS3T33nInzOefArFnljxMl+sH8xT/8oVnhBxwAU6fGt2/dag+cI46AIUPKW/rhQWsi\nZu0H7q5w3p2Ao46yyW9+9at4Zs7du+1h9fLL8H//l/73CB5+vXtbn01tFv2UiEgD4Dbgp5U9hqpO\nU9VCVS3sELxHOU49IJnbJzxqF6KnfQRLzJauDsTDQ2fMgDffjLuPduwoP14gTH6+dfAGYvnyy2b1\nJps0Pkz37iamYdFfsgSuucbi4ydMsLKWLW2A1JAhJqKBQAdEiX7TphatdOGF1hfRrp3NiRBc19y5\ncVfKkCHmBtqwwa6jffvy7qgBA+xBWFpaNsNmmLvvtjENl1xix77xRovvb9gw+cT1YT74wH7L/fev\n4dm4VDXlBzgSmBNavwa4JrTeGtgErIp9vgbWAYURdecAR6Y635AhQ9RxnLI89JBq166qIqqdO6s2\nbKgKqhs3lq3TpImVR31E7BhR27p2TX6+xo1VhwxR3b1btVUr1Usvzbzdv/iFaoMG1s7161W7d1ft\n0EF13brydTdsUO3WTfWAA1RXrYqXX321taG0NPl5nnjCruMf/7D1m26ytm/Zovrvf9u2Z59VPekk\n1cLC8vvfd5/VWb5cdcAA1TFjos/z5z9bvQsvtONfeKHqkUeqjhiR/rc47TTV/v3t+9FHq44cmX6f\nigAUaxo9V2t+WtFvBKwEugNNgHeBfinqvwwUxr73i9VvGtt/JdAw1flc9B0nPWeeqdqsWXkhHD06\nuegHD4pkn65dTewfekg1L6/8A+PGG+37o49m3s7iYtvn7rtVBw2y4771VvL6ixertmmjWlCgunWr\nlV10keqBB6Y+z86dqvvvb7+Lquqpp9oxVFW3bbP2T5qk2ru36tix5fefO9fa+cQT9jtMmBB9nj17\nVI891ur27au6fbvqBReoduyYun2q1p4zzrDvZ56p2q9f+n0qQqain9a9o6olwOUxK30J8JiqLhKR\nSSJyepp9FwGPAYuB54H/UdUaGnzsOLnD739vo0gTXTpBXv3Gjcvvky4iKNUk8Ko22Aoy8+cHDB5s\ncelXXmkdpY8/Dt/4RvL6fftaX8PixfEkcIkDs6Jo0sTcRc88Y26cN96IR8m0agW9etmYgWSD1goK\nLFpnwYJon35AgwZw77021eJjj5m7pk8fCwVNljAPzG20cqXl+wHrh6jVPn1Vna2qvVT1EFWdHCu7\nQVXLdbuo6khVLQ6tT47t11tVn8te0x2n/nLQQZbgLJFA9K+4It5PUJFIoFT587dsMXEMUg1ngojl\n4iktNbEM0jWnYtQomzP4jjtM8DMRfTD/fkmJRSBt3hwXfbCHz4svlo/RD8jLs0im+fPjoajJ6NnT\nOp+DUNcgsmfZsuT7bNhg5+7Rw9Y7dLA2ZmPEckXxEbmOk0MEgnzBBfHw0GwKy0cfJe/0jWLGjHhH\n7o03Zr7vL39pg6FuvTVz0e/d28JOg5w/QSoJsM7cYIrIcLhmmAED4tNVJrP0o8hE9IPInbClv2dP\n6reD6sJF33FyiDFjLM6+d+94WbKxAKmifZLxxReW50ekfPx/IkFm0Y9iQdtRmUWTUVBgkTx3321h\npZmIPtgIYTCXTpCOGkz0A6IsfTDRDyKFKiL63bubOy1VBE+U6EPNuHhc9B0nhzj0UAv3bNQoXhY1\nFqBxY3sbyGQsQCLBSODVq+MPgPbt7RMkh5sxI3Vm0XCSuWQPjxtuMJfI9u2Zi/7YsTaadujQsm6t\nQYPi3xMnlgkI0jFAxUS/cWP73VOJ/sqVdq3BAzjb8+5WBBd9x8lxosYCPPCA5b4JjwWoDMEDYPPm\neER7UCoAAAtiSURBVOK3wKJPlpI5vD3Z3AJgbyvnnWffMxX9Fi2sM/fOO8uWt25twhw1sUxAkI4h\nqF8R+vQpL/oLFsTnN/jgAxP8Jk1s3S19x3GqlagUEOHyZMJf2XQQO3Yk37dhw/RzCwQMGGAW8pVX\npncnBRx7bHS207POskRxyejSJT5JTUUsfTDRX7EinjV0wwZzKXXpYlFF8+bFXTvgou84Tg1T1XQQ\nUezZE33MZKGjiXMOzJgBv/hFvCO6In0CUdx8c/ncOWGCdAxQOdEvKYnn13niCVs/+2z7vmyZvWkE\nBG8uYdF/8kkLV61uXPQdx6lyOogogmMkHjPZW0Vih3Mms41lm0D0K+PegbiL57HHrDN6+nTriL73\nXvjf/43Xb9LEzhEW/VtuiY+FqFYyGcG1Lz8+Itdxai9BeoZglG6y0b15eVY3032j6ic7vkj1Xd+r\nr6qOG2cjbyvCli3WtilTLMVEMII5FYceaudSVf36a0uh8fOfV67dqlkckes4jhMQ9AGomhUbWPH5\n+fYJvgdTOAZ++PDE8GD7B28N4bmAwyQLNU1Wng2GD4dHHimbSz8TWre2JHRLl5o7R9USwaUiPNn6\nO+/YTGDhAWXVhYu+4ziVItw5HIycnT7dZv5KjORJltqha9eyHcthovoZRKLnCKgNBBE8jz1mHckF\n5aaaKks402Ywy5eLvuM4dYpkfvhkqR2STRgPZfsZwAQ/PEagKp261UGfPpaT/9VXrQM3HeH8O2+8\nYW8wwYxg1YmLvuM4WSOViEfRpUvqgVrhkNJA8AN27IBzz609Vn+fPvaWk4lrB0z0N22y+uEEcdWN\ni77jOFkjmb89Pz86fPOUUzIbqJXqYZJsn0xG/WaTIPXFYYeVTQGRjA4dLKxzyRK7Bhd9x3HqHMni\n/e+8Mzp8c/bsaHfQhAllxTpd521iKGe44zh4mGSaM6iyBAPCvvvdzOoHA7SeecaW4QRx1UomIT77\n8uMhm45TtwnPuhVMzJKMVGGf4XDOyy4rP7FLVChnOCw03TGDCWMybWsm13rDDRZ+mQnPP29tOeoo\nC9fMdL9kkK2Zs/b1x0XfceoPmQh0eFavVPXz89M/GNLVjxovkIyoGcYqsv+8efGH1bBhlfwBQ2Qq\n+u7ecRynxohyB0WxZk28U/ehh6JdSFDeVZSKzZurNuK3qiOGA/eO6j507eA+fcdxapDE9A/JkrSF\nffrJUkZ89ll22pRpBFKyepnuH4g+7LtOXMhQ9EVktIgsE5EVInJ1xPZLRWShiMwXkVdFpCBW3k1E\nvoqVzxeRP2X7AhzHqduEB3k9+GC0FT95cvJ9Jk8261oTQjrDJOYMysuziKIoVDPr7K3qiOFmzWyy\nF9i3op/e6Q8NgQ+AHkAT4F2gIKHOfqHvpwPPx753A97LxM8UfNyn7zj1m4p0rkb51dN12Obn26cy\n+YPSnbsiPn1V1R49VA84QLW0NPN9kkEWffpDgRWqulJVdwEzgTEJD45todUWQIpnruM4TnKS5f6P\nIsqvHhDO6RMcM5wmAsrmAEok3SxfydxMqdqbyLBhcOaZlZu6srKIpnonAkRkLDBaVS+KrZ8HDFPV\nyxPq/Q/wE+xt4DhVXS4i3YBFwPvANuB6Vf1PxDkmAhMBunTpMmR1sil3HMdxQjRoEO3WEYmeEL5b\nt+QzeiUjnP4BzDVUUXHfF4jIPFUtTFcvax25qjpVVQ8BrgKujxWvB7qo6iDsgfCwiOwXse80VS1U\n1cIO4d4Nx3GcFFTUr17RNBEQnf6hOnP6VzeZiP7HwMGh9c6xsmTMBM4AUNWdqro59n0e1jfQq3JN\ndRzHKUuyEcCJHb8BFUkTkYqoh8e+TvtQWTIR/blATxHpLiJNgHHArHAFEekZWj0VWB4r7yAiDWPf\newA9gZXZaLjjOE5F/eqZpInIhAYNyop7VNqH2pYFNCCtTx9ARE4B7sAiee5X1ckiMgnrLZ4lIncC\nJwC7gc+By1V1kYh8G5gUKy8Ffqmqz6Q6V2FhoRYXF1fpohzHcZIxY4a5Z9asMct/8uSyD4mK+v3z\n8mzSmKj00cF8AfuCTH36GYn+vsRF33GcmiSw2sNRQUFnbsOGySd2jyJZh3J1sM87ch3HcXKBKJfR\n9Okm+hUV8EwHeu1LGtV0AxzHcWobQWx/Il26RLt+8vMt/j9qzEDg3w+OW9O4pe84jpMhle0Irk1h\nni76juM4GZIqWigY9ZtsdG2qMQL7MtzTRd9xHKcCpEsTkWrAWJS47+twT4/ecRzHySJR0T95eTYF\n5IMPli/PVrinR+84juPUAMlcQMnmA44SfKhcyohMcNF3HMfJMlH5/iua6C3TvPwVxUM2Hcdxqoko\nV08mpMofVFXc0nccx6kmUuX7T0Zl8vJXBLf0Hcd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7a9dede48>"
      ]
     },
     "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": [
    "Thanks to data augmentation and dropout, we are no longer overfitting: the training curves are rather closely tracking the validation \n",
    "curves. We are now able to reach an accuracy of 82%, a 15% relative improvement over the non-regularized model.\n",
    "\n",
    "By leveraging regularization techniques even further and by tuning the network's parameters (such as the number of filters per convolution \n",
    "layer, or the number of layers in the network), we may be able to get an even better accuracy, likely up to 86-87%. However, it would prove \n",
    "very difficult to go any higher just by training our own convnet from scratch, simply because we have so little data to work with. As a \n",
    "next step to improve our accuracy on this problem, we will have to leverage a pre-trained model, which will be the focus of the next two \n",
    "sections."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
