tf.contrib.rnn.GRUBlockCell

Block GRU cell implementation.

Inherits From: LayerRNNCell

tf.contrib.rnn.GRUBlockCell(
    num_units=None, cell_size=None, reuse=None, name='gru_cell'
)

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

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.

Attributes
graph	DEPRECATED FUNCTION
output_size	Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size	size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

get_initial_state

View source

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

zero_state

View source

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.