| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | recurrent_v2.py | 446 last_output, outputs, states = K.rnn(
460 last_output, outputs, runtime, states = self._defun_gru_call(
470 output = last_output
503 (last_output, outputs, new_h,
531 last_output, outputs, new_h, runtime = gpu_gru(**gpu_gru_kwargs)
533 last_output, outputs, new_h, runtime = standard_gru(
536 last_output, outputs, new_h, runtime = gru_with_backend_selection(
540 return last_output, outputs, runtime, states
613 last_output, outputs, new_states = K.rnn(
624 return last_output, outputs, new_states[0], _runtime(_RUNTIME_CPU)
[all …]
|
| D | convolutional_recurrent.py | 325 last_output, outputs, states = K.rnn(step,
342 output = last_output
|
| D | recurrent.py | 806 last_output, outputs, states = K.rnn(
828 output = last_output
|
| /external/selinux/python/sepolgen/src/sepolgen/ |
| D | module.py | 125 self.last_output = ""
132 self.last_output = str
189 raise RuntimeError("compilation failed:\n%s" % self.last_output)
203 raise RuntimeError("compilation failed:\n%s" % self.last_output)
214 raise RuntimeError("packaging failed [%s]" % self.last_output)
|
| /external/XNNPACK/src/x32-zip/ |
| D | xm-wasmsimd.c | 29 float* last_output = (float*) ((uintptr_t) output + (m * 4 - 4 * sizeof(uint32_t))); in xnn_x32_zip_xm_ukernel__wasmsimd() local
112 if (o > last_output) { in xnn_x32_zip_xm_ukernel__wasmsimd()
113 o = last_output; in xnn_x32_zip_xm_ukernel__wasmsimd()
|
| D | xm-neon.c | 28 uint32_t* last_output = (uint32_t*) ((uintptr_t) output + (m * 4 - 16)); in xnn_x32_zip_xm_ukernel__neon() local
97 if (output > last_output) { in xnn_x32_zip_xm_ukernel__neon()
98 output = last_output; in xnn_x32_zip_xm_ukernel__neon()
|
| D | xm-sse2.c | 28 uint32_t* last_output = (uint32_t*) ((uintptr_t) output + (m * 4 - 16)); in xnn_x32_zip_xm_ukernel__sse2() local
111 if (output > last_output) { in xnn_x32_zip_xm_ukernel__sse2()
112 output = last_output; in xnn_x32_zip_xm_ukernel__sse2()
|
| /external/XNNPACK/src/x8-zip/ |
| D | xm-neon.c | 24 uint8_t* last_output = (uint8_t*) ((uintptr_t) output + (m - 4)); in xnn_x8_zip_xm_ukernel__neon() local
127 if (output > last_output) { in xnn_x8_zip_xm_ukernel__neon()
128 output = last_output; in xnn_x8_zip_xm_ukernel__neon()
|
| D | xm-sse2.c | 24 uint8_t* last_output = (uint8_t*) ((uintptr_t) output + (m - 4)); in xnn_x8_zip_xm_ukernel__sse2() local
189 if (output > last_output) { in xnn_x8_zip_xm_ukernel__sse2()
190 output = last_output; in xnn_x8_zip_xm_ukernel__sse2()
|
| /external/XNNPACK/src/operators/ |
| D | unpooling-nhwc.c | 178 if (output == unpooling_op->last_output && in xnn_setup_unpooling2d_nhwc_x32()
229 unpooling_op->last_output = output; in xnn_setup_unpooling2d_nhwc_x32()
|
| D | deconvolution-nhwc.c | 634 if (any_size_change || output != deconvolution_op->last_output) { in setup_subconv2d_path()
651 deconvolution_op->last_output = output; in setup_subconv2d_path()
|
| /external/tensorflow/tensorflow/compiler/mlir/lite/utils/ |
| D | lstm_utils.cc | 794 auto last_output = CreateStridedSliceOp( in ConvertKerasLSTMLayer() local
807 outputs.push_back(last_output); in ConvertKerasLSTMLayer()
808 output_types.push_back(last_output.getType()); in ConvertKerasLSTMLayer()
|
| /external/XNNPACK/src/xnnpack/ |
| D | operator.h | 249 void* last_output; member
|
| /external/tensorflow/tensorflow/python/ops/ |
| D | rnn.py | 1497 last_output = outputs[-1]
1498 flat_last_output = nest.flatten(last_output)
1503 structure=last_output, flat_sequence=flat_last_output)
|
| /external/tensorflow/tensorflow/python/keras/ |
| D | backend.py | 4416 last_output = successive_outputs[-1]
4421 last_output = array_ops.where_v2(
4422 _expand_mask(mask_list[-1], last_output), last_output,
4423 zeros_like(last_output))
4434 last_output = successive_outputs[-1]
4616 last_output = tuple(o[-1] for o in outputs)
4619 last_output = nest.pack_sequence_as(output_time_zero, last_output)
4635 return last_output, outputs, new_states
|
| D | backend_test.py | 1247 last_output, outputs, new_states = backend.rnn(rnn_fn, inputs,
1250 self.assertEqual(last_output.shape.as_list(), [num_samples, output_dim])
1256 last_output_list[i].append(backend.eval(last_output))
1362 last_output, outputs, new_states = backend.rnn(rnn_fn, inputs,
1365 self.assertEqual(last_output.shape.as_list(), [num_samples, output_dim])
1375 last_output_list[i].append(backend.eval(last_output))
|
| /external/tensorflow/tensorflow/python/keras/layers/legacy_rnn/ |
| D | rnn_cell_impl.py | 335 last_output) = getattr(self, "_last_zero_state")
338 return last_output
|