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tf.raw_ops.BlockLSTMGradV2

Computes the LSTM cell backward propagation for the entire time sequence.

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Compat aliases for migration

tf.compat.v1.raw_ops.BlockLSTMGradV2

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.

Args
`seq_len_max`	A `Tensor` of type `int64`. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.
`x`	A `Tensor`. Must be one of the following types: `half`, `float32`. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
`cs_prev`	A `Tensor`. Must have the same type as `x`. Value of the initial cell state.
`h_prev`	A `Tensor`. Must have the same type as `x`. Initial output of cell (to be used for peephole).
`w`	A `Tensor`. Must have the same type as `x`. The weight matrix.
`wci`	A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection.
`wcf`	A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection.
`wco`	A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection.
`b`	A `Tensor`. Must have the same type as `x`. The bias vector.
`i`	A `Tensor`. Must have the same type as `x`. The input gate over the whole time sequence.
`cs`	A `Tensor`. Must have the same type as `x`. The cell state before the tanh over the whole time sequence.
`f`	A `Tensor`. Must have the same type as `x`. The forget gate over the whole time sequence.
`o`	A `Tensor`. Must have the same type as `x`. The output gate over the whole time sequence.
`ci`	A `Tensor`. Must have the same type as `x`. The cell input over the whole time sequence.
`co`	A `Tensor`. Must have the same type as `x`. The cell after the tanh over the whole time sequence.
`h`	A `Tensor`. Must have the same type as `x`. The output h vector over the whole time sequence.
`cs_grad`	A `Tensor`. Must have the same type as `x`. The current gradient of cs.
`h_grad`	A `Tensor`. Must have the same type as `x`. The gradient of h vector.
`use_peephole`	A `bool`. Whether to use peephole weights.
`name`	A name for the operation (optional).

Returns
A tuple of `Tensor` objects (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad).
`x_grad`	A `Tensor`. Has the same type as `x`.
`cs_prev_grad`	A `Tensor`. Has the same type as `x`.
`h_prev_grad`	A `Tensor`. Has the same type as `x`.
`w_grad`	A `Tensor`. Has the same type as `x`.
`wci_grad`	A `Tensor`. Has the same type as `x`.
`wcf_grad`	A `Tensor`. Has the same type as `x`.
`wco_grad`	A `Tensor`. Has the same type as `x`.
`b_grad`	A `Tensor`. Has the same type as `x`.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/raw_ops/BlockLSTMGradV2