Computes the LSTM cell backward propagation for the entire time sequence.
tf.raw_ops.BlockLSTMGradV2(
seq_len_max,
x,
cs_prev,
h_prev,
w,
wci,
wcf,
wco,
b,
i,
cs,
f,
o,
ci,
co,
h,
cs_grad,
h_grad,
use_peephole,
name=None
)
This implementation is to be used in conjunction of BlockLSTMV2.
seq_len_max
Tensor of type int64.
Maximum time length actually used by this input. Outputs are padded
with zeros beyond this length.
x
Tensor. Must be one of the following types: half, float32.
The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prev
Tensor. Must have the same type as x.
Value of the initial cell state.
h_prev
Tensor. Must have the same type as x.
Initial output of cell (to be used for peephole).
w
Tensor. Must have the same type as x. The weight matrix.
wci
Tensor. Must have the same type as x.
The weight matrix for input gate peephole connection.
wcf
Tensor. Must have the same type as x.
The weight matrix for forget gate peephole connection.
wco
Tensor. Must have the same type as x.
The weight matrix for output gate peephole connection.
b
Tensor. Must have the same type as x. The bias vector.
i
Tensor. Must have the same type as x.
The input gate over the whole time sequence.
cs
Tensor. Must have the same type as x.
The cell state before the tanh over the whole time sequence.
f
Tensor. Must have the same type as x.
The forget gate over the whole time sequence.
o
Tensor. Must have the same type as x.
The output gate over the whole time sequence.
ci
Tensor. Must have the same type as x.
The cell input over the whole time sequence.
co
Tensor. Must have the same type as x.
The cell after the tanh over the whole time sequence.
h
Tensor. Must have the same type as x.
The output h vector over the whole time sequence.
cs_grad
Tensor. Must have the same type as x.
The current gradient of cs.
h_grad
Tensor. Must have the same type as x.
The gradient of h vector.
use_peephole
bool. Whether to use peephole weights.
name
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Last updated 2024-04-26 UTC.