| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| lstm.mod.py | 136 output_state = [0, 0, 0, 0] variable
146 input0[output_state_in] = output_state
|
| lstm_state.mod.py | 136 output_state = [-0.0297319, 0.122947, 0.208851, -0.153588] variable
146 input0[output_state_in] = output_state
|
| lstm_state2.mod.py | 136 output_state = [-0.0371611, 0.125073, 0.411934, -0.208605] variable
146 input0[output_state_in] = output_state
|
| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| lstm_relaxed.mod.py | 137 output_state = [0, 0, 0, 0] variable
147 input0[output_state_in] = output_state
|
| lstm_state2_relaxed.mod.py | 137 output_state = [-0.0371611, 0.125073, 0.411934, -0.208605] variable
147 input0[output_state_in] = output_state
|
| lstm_state_relaxed.mod.py | 137 output_state = [-0.0297319, 0.122947, 0.208851, -0.153588] variable
147 input0[output_state_in] = output_state
|
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| lstm.cc | 254 TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); local
260 // Resize the output and output_state tensors.
271 context, context->ResizeTensor(context, output_state, output_state_size));
281 output_state->allocation_type = kTfLiteArenaRwPersistent;
348 TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); local
408 // For each batch and cell: compute recurrent_weight * output_state.
412 output_state->data.f, n_batch, input_gate_scratch, /*result_stride=*/1);
416 output_state->data.f, n_batch, forget_gate_scratch, /*result_stride=*/1);
418 recurrent_to_cell_weights->data.f, n_cell, n_output, output_state->data.f,
422 output_state->data.f, n_batch, output_gate_scratch, /*result_stride=*/1)
[all...] |
| unidirectional_sequence_lstm.cc | 255 TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); local
261 // Resize the output and output_state tensors.
273 context, context->ResizeTensor(context, output_state, output_state_size));
283 output_state->allocation_type = kTfLiteArenaRwPersistent;
350 TfLiteTensor* output_state = GetOutput(context, node, kOutputStateTensor); local
413 // For each batch and cell: compute recurrent_weight * output_state.
417 output_state->data.f, n_batch, input_gate_scratch,
422 output_state->data.f, n_batch, forget_gate_scratch,
426 output_state->data.f, n_batch, cell_scratch, /*result_stride=*/1);
429 output_state->data.f, n_batch, output_gate_scratch
[all...] |
| /device/linaro/bootloader/arm-trusted-firmware/plat/arm/board/fvp/ |
| fvp_pm.c | 382 psci_power_state_t *output_state)
384 return arm_validate_power_state(power_state, output_state);
|
| /device/linaro/bootloader/arm-trusted-firmware/plat/arm/css/common/ |
| css_pm.c | 283 psci_power_state_t *output_state)
285 return arm_validate_power_state(power_state, output_state);
|
| /external/tensorflow/tensorflow/contrib/cudnn_rnn/python/kernel_tests/ |
| cudnn_rnn_test.py | 132 def _AddUp(self, outputs, output_state):
134 for s in output_state:
151 def output_state(self): member in class:CudnnTestModel
160 return self._AddUp(self.outputs, self.output_state)
209 outputs, output_state = self._rnn(
211 return self._AddUp(outputs, output_state)
225 [self.outputs, self.output_state],
843 output_state = array_ops.stack([s for s in states])
859 output_state = []
861 output_state.append(array_ops.stack([s_fw, s_bw])
[all...] |
| /external/tensorflow/tensorflow/core/grappler/costs/ |
| virtual_scheduler.cc | 780 auto& output_state = node_map_[output_node]; local
781 output_state.num_inputs_ready++;
785 if (output_state.num_inputs_ready == output_state.inputs.size() ||
788 output_state.time_ready = curr_time;
[all...] |
| /device/linaro/bootloader/arm-trusted-firmware/include/lib/psci/ |
| psci.h | 302 psci_power_state_t *output_state);
|
