<gh_stars>0
# 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(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(Conv2D(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(units = 128, 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

#No flip
#train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True)
#train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = False)
train_datagen = ImageDataGenerator(rescale = 1./255,zoom_range=[0.5, 1.5],fill_mode='constant', 
                                    cval=190, horizontal_flip = False)

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 = 800, epochs = 10, validation_data = test_set, validation_steps = 2000)

# serialize model to JSON
model_json = classifier.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)

# serialize weights to HDF5
classifier.save_weights("model.h5")
print("Saved model to disk")
# 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(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(Conv2D(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(units = 128, 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

#No flip
#train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True)
#train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = False)
train_datagen = ImageDataGenerator(rescale = 1./255,zoom_range=[0.5, 1.5],fill_mode='constant', 
                                    cval=190, horizontal_flip = False)

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 = 800, epochs = 10, validation_data = test_set, validation_steps = 2000)

# serialize model to JSON
model_json = classifier.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)

# serialize weights to HDF5
classifier.save_weights("model.h5")
print("Saved model to disk")
# Importing the Keras libraries and packages # Initialising 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 #No flip #train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True) #train_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = False) # serialize model to JSON # serialize weights to HDF5