tf.raw_ops.GRUBlockCell
Computes the GRU cell forward propagation for 1 time step.
tf.raw_ops.GRUBlockCell(
x, h_prev, w_ru, w_c, b_ru, b_c, name=None
)
Args x: Input to the GRU cell. h_prev: State input from the previous GRU cell. w_ru: Weight matrix for the reset and update gate. w_c: Weight matrix for the cell connection gate. b_ru: Bias vector for the reset and update gate. b_c: Bias vector for the cell connection gate.
Returns r: Output of the reset gate. u: Output of the update gate. c: Output of the cell connection gate. h: Current state of the GRU cell.
Note on notation of the variables:
Concatenation of a and b is represented by a_b Element-wise dot product of a and b is represented by ab Element-wise dot product is represented by \circ Matrix multiplication is represented by *
Biases are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0)
This kernel op implements the following mathematical equations:
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 | |
|---|---|
x |
A Tensor. Must be one of the following types: float32. |
h_prev |
A Tensor. Must have the same type as x. |
w_ru |
A Tensor. Must have the same type as x. |
w_c |
A Tensor. Must have the same type as x. |
b_ru |
A Tensor. Must have the same type as x. |
b_c |
A Tensor. Must have the same type as x. |
name |
A name for the operation (optional). |
| Returns | |
|---|---|
A tuple of Tensor objects (r, u, c, h). |
|
r |
A Tensor. Has the same type as x. |
u |
A Tensor. Has the same type as x. |
c |
A Tensor. Has the same type as x. |
h |
A Tensor. Has the same type as x. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/GRUBlockCell