import ptvsd

# Allow other computers to attach to ptvsd at this IP address and port.
ptvsd.enable_attach(address=('10.122.24.46', 3000), redirect_output=True)

# Pause the program until a remote debugger is attached
ptvsd.wait_for_attach()

import os
from keras import models
from keras import layers
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.applications import VGG16
import matplotlib.pyplot as plt

base_dir = './data/cats_and_dogs_small'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
conv_base = VGG16(
    weights='imagenet', include_top=False, input_shape=(150, 150, 3))
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
print(
    'This is the number of trainable weights '
    'before freezing the conv base:', len(model.trainable_weights))
conv_base.trainable = False
print(
    'This is the number of trainable weights '
    'after freezing the conv base:', len(model.trainable_weights))
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')
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(
    validation_dir, target_size=(150, 150), batch_size=20, class_mode='binary')
model.compile(
    loss='binary_crossentropy',
    optimizer=optimizers.RMSprop(lr=2e-5),
    metrics=['acc'])
history = model.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=30,
    validation_data=validation_generator,
    validation_steps=50)

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.savefig('./result/dog_vs_cat_res_4_acc.png')
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()
plt.savefig('./result/dog_vs_cat_res_4_loss.png')
