import tensorflow as tf

				inputs=embed_inputs,
				dtype=tf.float32,
				sequence_length=self.seq_len,
				)       ## (batch_size, seq_len, num_hidden)

			# rnn_outputs = tf.transpose(rnn_outputs, perm=[1,0,2]) ## (seq_len, batch_size, num_hidden) NOT NEEDED ANY MORE
			last_outputs = self.last_relevant(rnn_outputs, self.seq_len) ## (batch_size, num_hidden)

		with tf.variable_scope('output', reuse=forward_only):
			with tf.variable_scope('softmax'):
				W = tf.get_variable('W', [self.num_hidden, self.num_classes],
									# initializer=tf.random_uniform_initializer(-0.003, 0.003))
									initializer=tf.contrib.layers.xavier_initializer())
									# initializer=tf.truncated_normal_initializer(stddev=0.1))
				b = tf.get_variable('b', [self.num_classes], initializer=tf.constant_initializer(0.1))
			logits = tf.matmul(last_outputs, W) + b
