tf.contrib.rnn.GRUBlockCell

Class GRUBlockCell

Block GRU cell implementation.

Inherits From: LayerRNNCell

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

Deprecated: use GRUBlockCellV2 instead.

The implementation is based on: http://arxiv.org/abs/1406.1078 Computes the GRU 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

__init__

__init__(
    num_units=None,
    cell_size=None,
    reuse=None,
    name='gru_cell'
)

Initialize the Block GRU cell. (deprecated arguments)

Args:

  • num_units: int, The number of units in the GRU cell.
  • cell_size: int, The old (deprecated) name for num_units.
  • reuse: (optional) boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.
  • name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with tf.compat.v1.nn.rnn_cell.GRUCell.

Raises:

  • ValueError: if both cell_size and num_units are not None; or both are None.

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

Methods

get_initial_state

get_initial_state(
    inputs=None,
    batch_size=None,
    dtype=None
)

zero_state

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, 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, s] for each s in state_size.