import tensorflow as tf

		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])
		state_in = (c_in, h_in)
		rnn_in = tf.expand_dims(self.h3, [0])
		step_size = tf.shape(inputs)[:1]
		state_in = tf.nn.rnn_cell.LSTMStateTuple(c_in, h_in)
		lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
		lstm_cell, rnn_in, initial_state=state_in, sequence_length=step_size,
		time_major=False)
		lstm_c, lstm_h = lstm_state
		state_out = (lstm_c[:1, :], lstm_h[:1, :])
		self.rnn_out = tf.reshape(lstm_outputs, [-1, RNN_SIZE])

