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