from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten


		# FC layers for goal_pos input
		# goal_layer1 = Dense(units=GOAL_SIZE)(goal_pos)
		# goal_layer2 = Dense(units=GOAL_SIZE)(goal_layer1)

		# FC layers to find next location
		loc_layer1  = Dense(units=loc_layer_size)(prev_loc)
		loc_layer2  = Dense(units=loc_layer_size)(loc_layer1)

		# Concatenationation of above layers, followed by FC layer
		concat   = tf.concat([flat1b, loc_layer2],1) # goal_layer2
		h1       = Dense(units=RNN_SIZE)(concat)
		h2       = Dense(units=RNN_SIZE)(h1)
		self.h3  = tf.nn.relu(h2+concat)

		#Recurrent network for temporal dependencies
		lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(RNN_SIZE,state_is_tuple=True)
		c_init = np.zeros((1, lstm_cell.state_size.c), np.float32)
		h_init = np.zeros((1, lstm_cell.state_size.h), np.float32)
		state_init = [c_init, h_init]
		c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
		h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
