simple_rnn_1.py

hidden_states = tf.scan(fn=rnn_step,
                        elems=tf.transpose(embed, perm=[1, 0, 2]), # change batch_size*seq_len*dim --> seq_len*batch_size*dim
                        initializer=tf.zeros([batch_size, rnn_size]))

outputs = tf.transpose(hidden_states, perm=[1, 0, 2]) # convert to original shape --> batch_size*seq_len*dim
last_rnn_output = outputs[:, -1, :] # extract last output