adding model code

classes/model/pix2code2.py

@@ -0,0 +1,68 @@
+__author__ = 'Ferdiand John Briones, attempt at pix2code2 through pretrained autoencoders'
+
+from keras.layers import Input, Dense, Dropout, RepeatVector, LSTM, concatenate, Flatten
+from keras.models import Sequential, Model
+# from keras.optimizers import RMSprop
+from tensorflow.keras.optimizers import RMSprop
+from keras import *
+from .Config import *
+from .AModel import *
+from .autoencoder_image import *
+
+class pix2code2(AModel):
+	def __init__(self, input_shape, output_size, output_path):
+		AModel.__init__(self, input_shape, output_size, output_path)
+		self.name = "pix2code2"
+
+		visual_input = Input(shape=input_shape)
+
+		#Load the pre-trained autoencoder model
+		autoencoder_model = autoencoder_image(input_shape, input_shape, output_path)
+		autoencoder_model.load('autoencoder')
+		autoencoder_model.model.load_weights('../bin/autoencoder.h5')
+
+		#Get only the model up to the encoded part
+		hidden_layer_model_freeze = Model(inputs=autoencoder_model.model.input, outputs=autoencoder_model.model.get_layer('encoded_layer').output)
+		hidden_layer_input = hidden_layer_model_freeze(visual_input)
+		
+		#Additional layers before concatenation
+		hidden_layer_model = Flatten()(hidden_layer_input)
+		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
+		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
+		hidden_layer_model = Dense(1024, activation='relu')(hidden_layer_model)
+		hidden_layer_model = Dropout(0.3)(hidden_layer_model)
+		hidden_layer_result = RepeatVector(CONTEXT_LENGTH)(hidden_layer_model)
+
+		#Make sure the loaded hidden_layer_model_freeze will no longer be updated
+		for layer in hidden_layer_model_freeze.layers:
+			layer.trainable = False
+
+		#The same language model that of pix2code by Tony Beltramelli
+		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([hidden_layer_result, 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_generator(self, generator, steps_per_epoch):
+		self.model.summary()
+		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)