from keras.layers import Dense, Flatten
from keras.models import Model
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator

from glob import glob
import matplotlib.pyplot as plt

# re-size all the images to this
IMAGE_SIZE = [224, 224]

train_path = 'data/training_set'
valid_path = 'data/test_set'

# add preprocessing layer to the front of VGG
vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)

# don't train existing weights
for layer in vgg.layers:
  layer.trainable = False
  

#useful for getting number of classes
folders = glob('data/training_set/*')
  

# our layers - you can add more if you want
x = Flatten()(vgg.output)

#add the sigmoid as the activation function
prediction = Dense(1, activation='sigmoid')(x)

# create a model object
model = Model(inputs=vgg.input, outputs=prediction)

# view the structure of the model
model.summary()

# tell the model what cost and optimization method to use
model.compile(
  loss='binary_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)


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('data/training_set',
                                                 target_size = (224, 224),
                                                 batch_size = 64,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('data/test_set',
                                            target_size = (224, 224),
                                            batch_size = 64,
                                            class_mode = 'binary')

# see which class represents 1 and which represents 0
training_set.class_indices

# fit the model
r = model.fit_generator(
  training_set,
  validation_data=test_set,
  epochs=4,
  steps_per_epoch=len(training_set),
  validation_steps=len(test_set)
)
# loss plots
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='val loss')
plt.legend()
plt.show()
plt.savefig('LossVal_loss')

# accuracy plots
plt.plot(r.history['accuracy'], label='train acc')
plt.plot(r.history['val_accuracy'], label='val acc')
plt.legend()
plt.show()
plt.savefig('AccVal_acc')

#save our model in order to use it in web development
model.save('pet_prediction.h5')

