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??H??3H?5?3??L??HE??\???[L??A\]?@H?5r3?L???rank_ != kUnknownRankT: {half, float}use_peephole: bool = falsecell_clip: float = 3.0forget_bias: float = 1.0h: Tco: Tci: To: Tf: Tcs: Ti: Tb: Twco: Twcf: Twci: Tw: Th_prev: Tcs_prev: Tx: Tuse_peephole: boolwco_grad: Twcf_grad: Twci_grad: Tdicfo: Tcs_prev_grad: Th_grad: Tcs_grad: Tseq_len_max: int64b_grad: Tw_grad: Th_prev_grad: Tx_grad: Tabsl::string_view::substrbasic_string::eraseLogic problem? szsrc = %zu\n\r\t\"\'\\\x\String cannot end with \Value of \ exceeds 0xffString cannot end with \xUnknown escape sequence: \nfinityanout != nullptrCheck %s failed: %struetyesy1falsenon0vector::reservelength > 0NULLbasic_string::insert[%s : %d] RAW: %s
clock_gettime() failedINFOFATALWARNINGERRORabsl::LogSeverity()RUNNING_ON_VALGRINDVALGRIND_SLOWDOWNN10tensorflow4core10RefCountedE6??P5???5??P5?? 6??P5???5??P5??P5??P5??P5???5??P5??P5??P5??P5??P6??P5??P5??P5??P5??P5??P5??P5??p6??`6??P5??6??P5??P5??P5???5??P5??P5???5??P5??P5??P5???5??P5??P5??P5??P5??P5??P5??P5??P5???5???6???5???5??@6??@5??P5??7???5???5??@5???5???5???5???5???6???6???6???5???6???6???6??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5???5??P5??P5??P5??P5??P5??6???5???5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5??P5???5???5???5??@5???5???5???5??@5??@5???5???5??@5???5???5??@5???5??6???5???5??P5??P5??06??tensorflow/contrib/rnn/kernels/lstm_ops.cccs_prev.dims(0) != batch_size: cs_prev.dims(1) != cell_size: h_prev.dims(0) != batch_size: w.dim_size(0) != input_size + cell_size: w.dim_size(1) != cell_size * 4: b.dim_size(0) != cell_size * 4: LSTMBlockCellOpLSTMBlockCellOpLSTMBlockCellGradOpLSTMBlockCellGradOpBlockLSTMOpBlockLSTMOpBlockLSTMGradOpBlockLSTMGradOpCheck failed: res.CopyFrom(t.Slice(pos, pos + 1), {t.dim_size(1), t.dim_size(2)}) ./tensorflow/core/framework/tensor.hnew_num_elements == NumElements()cs.dim_size(0) != batch_size: ci.dim_size(0) != batch_size: co.dim_size(0) != batch_size: cs_grad_tensor.dims(0) != batch_size: cs_grad_tensor.dims(1) != cell_size: h_grad_tensor.dims(0) != batch_size: h_grad_tensor.dims(1) != cell_size: Check failed: copy_out_.empty() Check failed: !entry.second.second . This is unnecessary copying. Consider using shapes with even Check failed: !found->second.second Check failed: aligned.shape().IsSameSize(t.shape()) ctx_->allocate_temp(t.dtype(), t.shape(), &aligned)w and b cell_size don't match: BlockLSTMOp is inefficient when both batch_size and input_size are odd. You are using: batch_size=cell_size are odd. You are using: batch_size=wci.dim_size(0) != cell_size: wcf.dim_size(0) != cell_size: wco.dim_size(0) != cell_size: ./tensorflow/core/framework/shape_inference.h
Computes the LSTM cell forward propagation for 1 time step.

This implementation uses 1 weight matrix and 1 bias vector, and there's an
optional peephole connection.

This kernel op implements the following mathematical equations:

```python
xh = [x, h_prev]
[i, f, ci, o] = xh * w + b
f = f + forget_bias

if not use_peephole:
  wci = wcf = wco = 0

i = sigmoid(cs_prev * wci + i)
f = sigmoid(cs_prev * wcf + f)
ci = tanh(ci)

cs = ci .* i + cs_prev .* f
cs = clip(cs, cell_clip)

o = sigmoid(cs * wco + o)
co = tanh(cs)
h = co .* o
```

cell_clip: Value to clip the 'cs' value to.
use_peephole: Whether to use peephole weights.
forget_bias: The forget gate bias.

x: The input to the LSTM cell, shape (batch_size, num_inputs).
cs_prev: Value of the cell state at previous time step.
h_prev: Output of the previous cell at previous time step.
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.

i: The input gate.
cs: The cell state before the tanh.
f: The forget gate.
o: The output gate.
ci: The cell input.
co: The cell after the tanh.
h: The output h vector.

Computes the LSTM cell backward propagation for 1 timestep.

This implementation is to be used in conjunction of LSTMBlockCell.

use_peephole: Whether the cell uses peephole connections.
x: The input to the LSTM cell, shape (batch_size, num_inputs).
cs_prev: The previous cell state.
h_prev: The previous h state.
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.
i: The input gate.
cs: The cell state before the tanh.
f: The forget gate.
o: The output gate.
ci: The cell input.
co: The cell after the tanh.
cs_grad: The current gradient of cs.
h_grad: The gradient of h vector.
cs_prev_grad: The gradient of cs to be back-propped.
dicfo: The derivative wrt to [i, cs, f, o].
wci_grad: The gradient for wci to be back-propped.
wcf_grad: The gradient for wcf to be back-propped.
wco_grad: The gradient for wco to be back-propped.

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

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

```python
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)
```

cell_clip: Value to clip the 'cs' value to.
use_peephole: Whether to use peephole weights.
forget_bias: The forget gate bias.

seq_len_max: Maximum time length actually used by this input. Outputs are padded
  with zeros beyond this length.
x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prev: Value of the initial cell state.
h_prev: Initial output of cell (to be used for peephole).
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.

i: The input gate over the whole time sequence.
cs: The cell state before the tanh over the whole time sequence.
f: The forget gate over the whole time sequence.
o: The output gate over the whole time sequence.
ci: The cell input over the whole time sequence.
co: The cell after the tanh over the whole time sequence.
h: The output h vector over the whole time sequence.

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

This implementation is to be used in conjunction of LSTMBlock.

use_peephole: Whether to use peephole weights.

seq_len_max: Maximum time length actually used by this input. Outputs are padded
  with zeros beyond this length.
x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
cs_prev: Value of the initial cell state.
h_prev: Initial output of cell (to be used for peephole).
w: The weight matrix.
wci: The weight matrix for input gate peephole connection.
wcf: The weight matrix for forget gate peephole connection.
wco: The weight matrix for output gate peephole connection.
b: The bias vector.
i: The input gate over the whole time sequence.
cs: The cell state before the tanh over the whole time sequence.
f: The forget gate over the whole time sequence.
o: The output gate over the whole time sequence.
ci: The cell input over the whole time sequence.
co: The cell after the tanh over the whole time sequence.
h: The output h vector over the whole time sequence.
cs_grad: The current gradient of cs.
h_grad: The gradient of h vector.

x_grad: The gradient of x to be back-propped.
cs_prev_grad: The gradient of cs_prev to be back-propped.
h_prev_grad: The gradient of h_prev to be back-propped.
w_grad: The gradient for w to be back-propped.
wci_grad: The gradient for wci to be back-propped.
wcf_grad: The gradient for wcf to be back-propped.
wco_grad: The gradient for wco to be back-propped.
b_grad: The gradient for w to be back-propped.
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