# Convolutional Neural Network

# Part 1 - Building the CNN

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(64, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(64, (3, 3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 512, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling 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)

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

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

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

# Part 3 - Making new predictions


from keras.models import load_model

classifier.save('dogs_cats.h5')


import numpy as np
from keras.preprocessing import image

test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (1).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
print training_set.class_indices
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction


test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (2).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction

test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (3).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction

test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (4).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction

test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (5).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction

test_image = image.load_img('dataset/single_prediction/cat_or_dog_ (6).jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
if result[0][0] == 1:
    prediction = 'dog'
    print prediction
else:
    prediction = 'cat'
    print prediction
