"""GRU in TensorFlow."""
import tensorflow as tf
[docs]def gru(name,
gru_cell,
all_input_var,
step_input_var,
step_hidden_var,
output_nonlinearity_layer,
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False):
"""Gated Recurrent Unit (GRU).
Args:
name (str): Name of the variable scope.
gru_cell (tf.keras.layers.Layer): GRU cell used to generate
outputs.
all_input_var (tf.Tensor): Place holder for entire time-series inputs.
step_input_var (tf.Tensor): Place holder for step inputs.
step_hidden_var (tf.Tensor): Place holder for step hidden state.
output_nonlinearity_layer (callable): Activation function for output
dense layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
hidden_state_init (callable): Initializer function for the
initial hidden state. The functino should return a tf.Tensor.
hidden_state_init_trainable (bool): Bool for whether the initial
hidden state is trainable.
Return:
outputs (tf.Tensor): Entire time-series outputs.
output (tf.Tensor): Step output.
hidden (tf.Tensor): Step hidden state.
hidden_init_var (tf.Tensor): Initial hidden state.
"""
with tf.compat.v1.variable_scope(name):
hidden_dim = gru_cell.units
output, [hidden] = gru_cell(step_input_var, states=[step_hidden_var])
output = output_nonlinearity_layer(output)
hidden_init_var = tf.compat.v1.get_variable(
name='initial_hidden',
shape=(hidden_dim, ),
initializer=hidden_state_init,
trainable=hidden_state_init_trainable,
dtype=tf.float32)
hidden_init_var_b = tf.broadcast_to(
hidden_init_var, shape=[tf.shape(all_input_var)[0], hidden_dim])
def step(hprev, x):
_, [h] = gru_cell(x, states=[hprev])
return h
shuffled_input = tf.transpose(all_input_var, (1, 0, 2))
hs = tf.scan(step, elems=shuffled_input, initializer=hidden_init_var_b)
hs = tf.transpose(hs, (1, 0, 2))
outputs = output_nonlinearity_layer(hs)
return outputs, output, hidden, hidden_init_var