RNN output 스텝

rnn_minist_03_outputs.py

# Weights for output layers
with tf.name_scope('linear_layer_weights') as scope:
    with tf.name_scope("W_linear"):
        Wl = tf.Variable(tf.truncated_normal([hidden_layer_size, num_classes],
                                             mean=0, stddev=.01))
        variable_summaries(Wl)
    with tf.name_scope("Bias_linear"):
        bl = tf.Variable(tf.truncated_normal([num_classes],
                                             mean=0, stddev=.01))
        variable_summaries(bl)


# Apply linear layer to state vector
def get_linear_layer(hidden_state):
    return tf.matmul(hidden_state, Wl) + bl


with tf.name_scope('outputs') as scope:
    # Iterate across time, apply linear layer to all RNN outputs
    all_outputs = tf.map_fn(get_linear_layer, all_hidden_states)
    # Get Last output -- h_28
    output = all_outputs[-1]
    tf.summary.histogram('outputs', output)