architecture.py

import keras
import keras.backend as K
from keras import initializers


def Net():
	# define input
	x = keras.Input(shape=(55, 47, 3), name='input')
	# feature extraction
	conv_1 = keras.layers.Conv2D(20, (4, 4), activation='relu', name='conv_1')(x)
	pool_1 = keras.layers.MaxPooling2D((2, 2), name='pool_1')(conv_1)
	conv_2 = keras.layers.Conv2D(40, (3, 3), activation='relu', name='conv_2')(pool_1)
	pool_2 = keras.layers.MaxPooling2D((2, 2), name='pool_2')(conv_2)
	conv_3 = keras.layers.Conv2D(60, (3, 3), activation='relu', name='conv_3')(pool_2)
	pool_3 = keras.layers.MaxPooling2D((2, 2), name='pool_3')(conv_3)
	# first interpretation model
	flat_1 = keras.layers.Flatten()(pool_3)	
	fc_1 = keras.layers.Dense(160, name='fc_1')(flat_1)
	# second interpretation model
	conv_4 = keras.layers.Conv2D(80, (2, 2), activation='relu', name='conv_4')(pool_3)
	flat_2 = keras.layers.Flatten()(conv_4)
	fc_2 = keras.layers.Dense(160, name='fc_2')(flat_2)
	# merge interpretation
	merge = keras.layers.Add()([fc_1, fc_2])
	add_1 = keras.layers.Activation('relu')(merge)
	drop = keras.layers.Dropout(0.5)
	# output
	y_hat = keras.layers.Dense(1283, activation='softmax', name='output')(add_1)
	model = keras.Model(inputs=x, outputs=y_hat)
	# summarize layers
	#print(model.summary())
	# plot graph
	#plot_model(model, to_file='model_architecture.png')

	return model


K.clear_session()
model = Net()