Benchmark.6.Cats.Vs.Dogs.Network.py

from keras import layers, models, optimizers
import NvidiaMixedPrec
from keras.preprocessing.image import ImageDataGenerator
import os, time

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])

# train_datagen = ImageDataGenerator(rescale=1./255)
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=.2, height_shift_range=.2, shear_range=.2, horizontal_flip=True, fill_mode='nearest') # Augmentation
val_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

working_dir = '/home/andrew/PycharmProjects/DeepLearning/CatDog/CatDogWorking'

train_dir = os.path.join(working_dir, 'train')
val_dir = os.path.join(working_dir, 'val')
test_dir = os.path.join(working_dir, 'test')

train_generator = train_datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=20, class_mode='binary')
val_generator = val_datagen.flow_from_directory(val_dir, target_size=(150, 150), batch_size=20, class_mode='binary')
test_generator = test_datagen.flow_from_directory(test_dir, target_size=(150, 150), batch_size=20, class_mode='binary')


start_time = time.time()
history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=30, validation_data=val_generator, validation_steps=50)
end_time = time.time()

Results = model.evaluate(test_generator)

print("Accuracy on Test data:", Results[1]*100, "%")
print("Time:", end_time-start_time, "seconds.")