| /frameworks/ml/nn/runtime/test/specs/ |
| lstm3.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 16 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
[all...] |
| lstm3_state3.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 16 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
[all...] |
| lstm2.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
142 input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
[all...] |
| lstm2_state2.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
138 output_state_out: [ 0 for x in range(n_batch * n_output) ],
[all...] |
| lstm3_state.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 16 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
[all...] |
| lstm3_state2.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 16 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
48 projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
[all...] |
| lstm_state2.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
146 output_state_out: [ 0 for x in range(n_batch * n_output) ],
[all...] |
| lstm.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
|
| lstm2_state.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
|
| lstm_state.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
|
| /frameworks/ml/nn/tools/test_generator/tests/P_lstm/ |
| lstm.mod.py | 23 # n_cell and n_output have the same size when there is no projection.
25 n_output = 4 variable
34 recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
35 recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
36 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
37 recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
51 output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
59 output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
61 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
155 output_state_out: [ 0 for x in range(n_batch * n_output) ],
[all...] |
| /frameworks/ml/nn/common/operations/ |
| LSTMTest.cpp | 77 uint32_t n_cell, uint32_t n_output, bool use_cifg,
82 n_cell_(n_cell), n_output_(n_output),
91 input_shapes.push_back({n_batch, n_output});
115 {n_batch, n_output},
117 {n_batch, n_output},
135 OutputStateIn_.insert(OutputStateIn_.end(), n_batch * n_output, 0.f);
278 // n_cell and n_output have the same size when there is no projection.
280 const int n_output = 4; local
282 LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
295 {n_cell, n_output}, // recurrent_to_input_weight tenso
391 const int n_output = 4; local
495 const int n_output = 16; local
[all...] |
| LSTM.cpp | 84 uint32_t n_input, uint32_t n_output, uint32_t n_cell) {
122 NN_CHECK_EQ(SizeOfDimension(recurrent_to_input_weights, 1), n_output); local
129 NN_CHECK_EQ(SizeOfDimension(recurrent_to_forget_weights, 1), n_output); local
135 NN_CHECK_EQ(SizeOfDimension(recurrent_to_cell_weights, 1), n_output); local
207 NN_CHECK_EQ(SizeOfDimension(projection_weights, 0), n_output); local
215 NN_CHECK_EQ(SizeOfDimension(projection_bias, 0), n_output); local
260 const uint32_t n_output = SizeOfDimension(recurrent_to_output_weights, 1); local
263 if (!CheckInputTensorDimensions(operation, operands, n_input, n_output, n_cell)) {
271 outputShape->dimensions = { n_batch, n_output };
276 outputStateShape->dimensions = { n_batch, n_output };
307 const uint32_t n_output = recurrent_to_output_weights_->shape().dimensions[1]; local
361 GetBuffer<float>(recurrent_to_input_weights_), n_cell, n_output, local
365 GetBuffer<float>(recurrent_to_forget_weights_), n_cell, n_output, local
368 GetBuffer<float>(recurrent_to_cell_weights_), n_cell, n_output, local
371 GetBuffer<float>(recurrent_to_output_weights_), n_cell, n_output, local
435 tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(projection_bias_), n_output, local
438 tensor_utils::ZeroVector(GetBuffer<float>(output_), n_batch * n_output); local
441 GetBuffer<float>(projection_weights_), n_output, n_cell, local
445 tensor_utils::ClipVector(GetBuffer<float>(output_), n_batch * n_output, local
452 tensor_utils::CopyVector(GetBuffer<float>(output_), n_batch * n_output, local
[all...] |
| LSTM.h | 69 // Recurrent weight tensors of size {n_cell, n_output}
86 // Projection weight tensor of size {n_output, n_cell}
88 // Projection bias tensor of size {n_output}
108 uint32_t n_output, uint32_t n_cell);
|
| /external/libxml2/ |
| testapi.c | 6958 int n_output; local
6998 int n_output; local
7044 int n_output; local
7090 int n_output; local
7129 int n_output; local
7168 int n_output; local
7207 int n_output; local
7246 int n_output; local
7292 int n_output; local
7338 int n_output; local
7384 int n_output; local
7457 int n_output; local
17277 int n_output; local
17316 int n_output; local
33862 int n_output; local
34166 int n_output; local
47478 int n_output; local
47524 int n_output; local
[all...] |
