cat-dog.py

from hack import hack
hack()

from keras import models
from keras import layers

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.Dropout(0.5))

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

model.summary()

from keras import optimizers

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

import os

base_dir = '../catsdogssmall'
train_dir = os.path.join(base_dir, 'train')
test_dir = os.path.join(base_dir, 'test')

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    height_shift_range=0.2,
    horizontal_flip=True,
    rescale=1./255,
    rotation_range=40,
    shear_range=0.2,
    width_shift_range=0.2,
    zoom_range=0.2)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=20,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=20,
    class_mode='binary')

for data_batch, labels_batch in train_generator:
    print('data batch shape:', data_batch.shape)
    print('labels batch shape:', labels_batch.shape)
    break

history = model.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=10,
    validation_data=validation_generator,
    validation_steps=50)

model.save('cats_and_dogs_small.h5')

import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()