View source on GitHub |
Block GRU cell implementation.
Inherits From: LayerRNNCell
tf.contrib.rnn.GRUBlockCell(
num_units=None, cell_size=None, reuse=None, name='gru_cell'
)
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
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 |