# Artificial Neural Network

# Installing Theano

# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow

# Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html

# pip install tensorflow

# Installing Keras

# pip install --upgrade keras

# Part 1 - Data Preprocessing

# Importing the Keras libraries and packages

from keras.models import Sequential

from keras.layers import Convolution2D

from keras.layers import MaxPool2D

from keras.layers import Flatten

from keras.layers import Dense

# Initialize the CNN

classifier = Sequential()

# Step 1: Convolution

classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

# Step 2: Pooling

classifier.add(MaxPool2D(pool_size = (2,2)))

# Add a second convolutional layer

classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))

classifier.add(MaxPool2D(pool_size = (2,2)))

# Step 3: Flattening

classifier.add(Flatten())

# Step 4: Full connection

classifier.add(Dense(output_dim = 128, activation = 'relu'))

classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))

# Compile the CNN

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2: Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)

training_set = train_datagen.flow_from_directory('dataset/training_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')

test_set = train_datagen.flow_from_directory('dataset/test_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')

classifier.fit_generator(training_set, samples_per_epoch = 4000, nb_epoch = 10, validation_data = test_set, nb_val_samples = 1000)