tf.raw_ops.BlockLSTM

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_maxA Tensor of type int64. Maximum time length actually used by this input. Outputs are padded with zeros beyond this length.
xA Tensor. Must be one of the following types: half, float32. The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prevA Tensor. Must have the same type as x. Value of the initial cell state.
h_prevA Tensor. Must have the same type as x. Initial output of cell (to be used for peephole).
wA Tensor. Must have the same type as x. The weight matrix.
wciA Tensor. Must have the same type as x. The weight matrix for input gate peephole connection.
wcfA Tensor. Must have the same type as x. The weight matrix for forget gate peephole connection.
wcoA Tensor. Must have the same type as x. The weight matrix for output gate peephole connection.
bA Tensor. Must have the same type as x. The bias vector.
forget_biasAn optional float. Defaults to 1. The forget gate bias.
cell_clipAn optional float. Defaults to 3. Value to clip the 'cs' value to.
use_peepholeAn optional bool. Defaults to False. Whether to use peephole weights.
nameA name for the operation (optional).

A tuple of Tensor objects (i, cs, f, o, ci, co, h).
iA Tensor. Has the same type as x.
csA Tensor. Has the same type as x.
fA Tensor. Has the same type as x.
oA Tensor. Has the same type as x.
ciA Tensor. Has the same type as x.
coA Tensor. Has the same type as x.
hA Tensor. Has the same type as x.