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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
```
- - -
#### `tf.contrib.rnn.GRUBlockCell.__call__(x, h_prev, scope=None)` {#GRUBlockCell.__call__}
GRU cell.
- - -
#### `tf.contrib.rnn.GRUBlockCell.__init__(cell_size)` {#GRUBlockCell.__init__}
Initialize the Block GRU cell.
##### Args:
* <b>`cell_size`</b>: int, GRU cell size.
- - -
#### `tf.contrib.rnn.GRUBlockCell.output_size` {#GRUBlockCell.output_size}
- - -
#### `tf.contrib.rnn.GRUBlockCell.state_size` {#GRUBlockCell.state_size}
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#### `tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype)` {#GRUBlockCell.zero_state}
Return zero-filled state tensor(s).
##### Args:
* <b>`batch_size`</b>: int, float, or unit Tensor representing the batch size.
* <b>`dtype`</b>: 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`.
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