tf.raw_ops.BlockLSTM

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

tf.raw_ops.BlockLSTM(
    seq_len_max,
    x,
    cs_prev,
    h_prev,
    w,
    wci,
    wcf,
    wco,
    b,
    forget_bias=1,
    cell_clip=3,
    use_peephole=False,
    name=None
)

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).