from __future__ import absolute_import

__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'

from keras.layers import Input, Dense, Dropout, \

RepeatVector, LSTM, concatenate, \

Conv2D, MaxPooling2D, Flatten

from keras.models import Sequential, Model

from keras.optimizers import RMSprop

from keras import *

from .Config import *

from .AModel import *

class pix2code(AModel):

def __init__(self, input_shape, output_size, output_path):

AModel.__init__(self, input_shape, output_size, output_path)

self.name = "pix2code"

image_model = Sequential()

image_model.add(Conv2D(32, (3, 3), padding='valid', activation='relu', input_shape=input_shape))

image_model.add(Conv2D(32, (3, 3), padding='valid', activation='relu'))

image_model.add(MaxPooling2D(pool_size=(2, 2)))

image_model.add(Dropout(0.25))

image_model.add(Conv2D(64, (3, 3), padding='valid', activation='relu'))

image_model.add(Conv2D(64, (3, 3), padding='valid', activation='relu'))

image_model.add(MaxPooling2D(pool_size=(2, 2)))

image_model.add(Dropout(0.25))

image_model.add(Conv2D(128, (3, 3), padding='valid', activation='relu'))

image_model.add(Conv2D(128, (3, 3), padding='valid', activation='relu'))

image_model.add(MaxPooling2D(pool_size=(2, 2)))

image_model.add(Dropout(0.25))

image_model.add(Flatten())

image_model.add(Dense(1024, activation='relu'))

image_model.add(Dropout(0.3))

image_model.add(Dense(1024, activation='relu'))

image_model.add(Dropout(0.3))

image_model.add(RepeatVector(CONTEXT_LENGTH))

visual_input = Input(shape=input_shape)

encoded_image = image_model(visual_input)

language_model = Sequential()

language_model.add(LSTM(128, return_sequences=True, input_shape=(CONTEXT_LENGTH, output_size)))

language_model.add(LSTM(128, return_sequences=True))

textual_input = Input(shape=(CONTEXT_LENGTH, output_size))

encoded_text = language_model(textual_input)

decoder = concatenate([encoded_image, encoded_text])

decoder = LSTM(512, return_sequences=True)(decoder)

decoder = LSTM(512, return_sequences=False)(decoder)

decoder = Dense(output_size, activation='softmax')(decoder)

self.model = Model(inputs=[visual_input, textual_input], outputs=decoder)

optimizer = RMSprop(lr=0.0001, clipvalue=1.0)

self.model.compile(loss='categorical_crossentropy', optimizer=optimizer)

def fit(self, images, partial_captions, next_words):

self.model.fit([images, partial_captions], next_words, shuffle=False, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=1)

self.save()

def fit_generator(self, generator, steps_per_epoch):

self.model.fit_generator(generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, verbose=1)

self.save()

def predict(self, image, partial_caption):

return self.model.predict([image, partial_caption], verbose=0)[0]

def predict_batch(self, images, partial_captions):

return self.model.predict([images, partial_captions], verbose=1)