#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May  9 16:14:22 2018

@author: chinweihong
"""

# Part 1 - Building the CNN
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initializing 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(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, (3,3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

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

# Step 4 - Full connection
classifier.add(Dense(activation = 'relu', units = 128))
classifier.add(Dense(activation = 'sigmoid', units = 1))

# 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=7990,
        epochs=5,
        validation_data=test_set,
        validation_steps=1990)

# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image

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)
training_set.class_indices
if result[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'


#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May  9 16:14:22 2018

@author: chinweihong
"""

# Part 1 - Building the CNN
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initializing 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(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, (3,3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

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

# Step 4 - Full connection
classifier.add(Dense(activation = 'relu', units = 128))
classifier.add(Dense(activation = 'sigmoid', units = 1))

# 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=7990,
        epochs=5,
        validation_data=test_set,
        validation_steps=1990)

# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image

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)
training_set.class_indices
if result[0][0] == 1:
    prediction = 'dog'
else:
    prediction = 'cat'


#!/usr/bin/env python3 # -*- coding: utf-8 -*- Created on Wed May  9 16:14:22 2018  @author: chinweihong # Part 1 - Building the CNN # Initializing the CNN # Step 1 - Convolution # Step 2 - Pooling # Adding a second convolutional layer # Step 3 - Flattening # Step 4 - Full connection # Compiling the CNN # Part 2 - Fitting the CNN to the images # Part 3 - Making new predictions