tf.raw_ops.BlockLSTM

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Computes the LSTM cell forward propagation for all the time steps.

This is equivalent to applying LSTMBlockCell in a loop, like so:

for x1 in unpack(x):
  i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
    x1, cs_prev, h_prev, w, wci, wcf, wco, b)
  cs_prev = cs1
  h_prev = h1
  i.append(i1)
  cs.append(cs1)
  f.append(f1)
  o.append(o1)
  ci.append(ci1)
  co.append(co1)
  h.append(h1)
return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)

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.
forget_bias An optional float. Defaults to 1. The forget gate bias.
cell_clip An optional float. Defaults to 3. Value to clip the 'cs' value to.
use_peephole An optional bool. Defaults to False. Whether to use peephole weights.
name A name for the operation (optional).

A tuple of Tensor objects (i, cs, f, o, ci, co, h).
i A Tensor. Has the same type as x.
cs A Tensor. Has the same type as x.
f A Tensor. Has the same type as x.
o A Tensor. Has the same type as x.
ci A Tensor. Has the same type as x.
co A Tensor. Has the same type as x.
h A Tensor. Has the same type as x.