RNN States

rnn_minist_02_states.py

# Weights and bias for input and hidden layer
with tf.name_scope('rnn_weights'):
    with tf.name_scope("W_x"):
        Wx = tf.Variable(tf.zeros([element_size, hidden_layer_size]))
        variable_summaries(Wx)
    with tf.name_scope("W_h"):
        Wh = tf.Variable(tf.zeros([hidden_layer_size, hidden_layer_size]))
        variable_summaries(Wh)
    with tf.name_scope("Bias"):
        b_rnn = tf.Variable(tf.zeros([hidden_layer_size]))
        variable_summaries(b_rnn)

def rnn_step(previous_hidden_state, x):

        current_hidden_state = tf.tanh(
            tf.matmul(previous_hidden_state, Wh) +
            tf.matmul(x, Wx) + b_rnn)

        return current_hidden_state

initial_hidden = tf.zeros([batch_size, hidden_layer_size])
# Getting all state vectors across time
all_hidden_states = tf.scan(rnn_step,
                            processed_input,
                            initializer=initial_hidden,
                            name='states')