tf.contrib.rnn.GRUBlockCell

class tf.contrib.rnn.GRUBlockCell

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Block GRU cell implementation.

The implementation is based on: http://arxiv.org/abs/1406.1078 Computes the LSTM cell forward propagation for 1 time step.

This kernel op implements the following mathematical equations:

Biases are initialized with:

  • b_ru - constant_initializer(1.0)
  • b_c - constant_initializer(0.0)
x_h_prev = [x, h_prev]

[r_bar u_bar] = x_h_prev * w_ru + b_ru

r = sigmoid(r_bar)
u = sigmoid(u_bar)

h_prevr = h_prev \circ r

x_h_prevr = [x h_prevr]

c_bar = x_h_prevr * w_c + b_c
c = tanh(c_bar)

h = (1-u) \circ c + u \circ h_prev

Properties

output_size

state_size

Methods

__init__(cell_size)

Initialize the Block GRU cell.

Args:

  • cell_size: int, GRU cell size.

zero_state(batch_size, dtype)

Return zero-filled state tensor(s).

Args:

  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size x s] for each s in state_size.

Defined in tensorflow/contrib/rnn/python/ops/gru_ops.py.