Searched refs:num_units (Results 1 – 6 of 6) sorted by relevance
/frameworks/ml/nn/runtime/test/specs/V1_2/ |
D | unidirectional_sequence_rnn.mod.py | 47 num_units = 16 variable 144 num_units, input_size)), 146 "{{{}, {}}}".format(num_units, num_units)), 147 bias=Input("bias", "TENSOR_FLOAT32", "{{{}}}".format(num_units)), 149 num_batches, num_units)), 151 num_batches, max_time, num_units)), 158 hidden_state_data=[0] * num_batches * num_units, 166 num_units, input_size)), 168 "{{{}, {}}}".format(num_units, num_units)), 169 bias=Input("bias", "TENSOR_FLOAT32", "{{{}}}".format(num_units)), [all …]
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/frameworks/ml/nn/common/operations/ |
D | RNN.cpp | 66 const uint32_t num_units = SizeOfDimension(input_weights, 0); in Prepare() local 76 hiddenStateShape->dimensions = { batch_size, num_units }; in Prepare() 80 outputShape->dimensions = { batch_size, num_units }; in Prepare() 150 const uint32_t num_units = weightsShape.dimensions[0]; in RNNStep() local 167 const T* hidden_state_in_ptr_batch = hiddenStateInputData + b * num_units; in RNNStep() 183 for (uint32_t o = 0; o < num_units; o++) { in RNNStep() 188 for (uint32_t o = 0; o < num_units; o++) { in RNNStep() 197 for (uint32_t o = 0; o < num_units; o++) { in RNNStep() 206 for (uint32_t o = 0; o < num_units; o++) { in RNNStep() 207 for (uint32_t h = 0; h < num_units; h++) { in RNNStep() [all …]
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D | SVDF.cpp | 69 const uint32_t num_units = num_filters / rank; in Prepare() local 77 NN_CHECK_EQ(SizeOfDimension(bias, 0), num_units); in Prepare() 89 outputShape->dimensions = { batch_size, num_units }; in Prepare() 157 const int num_units = num_filters / rank; in EvalFloat32() local 191 tflite::tensor_utils::VectorBatchVectorAssign(biasData, num_units, batch_size, outputData); in EvalFloat32() 193 tflite::tensor_utils::ZeroVector(outputData, batch_size * num_units); in EvalFloat32() 198 float* output_ptr_batch = outputData + b * num_units; in EvalFloat32() 200 tflite::tensor_utils::ReductionSumVector(scratch_ptr_batch, output_ptr_batch, num_units, in EvalFloat32() 206 float* output_ptr_batch = outputData + b * num_units; in EvalFloat32() 207 tflite::tensor_utils::ApplyActivationToVector(output_ptr_batch, num_units, in EvalFloat32()
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D | FullyConnected.cpp | 211 uint32_t num_units = getSizeOfDimension(weights, 0); in prepare() local 214 NN_RET_CHECK_GT(num_units, 0); in prepare() 217 NN_RET_CHECK_EQ(getSizeOfDimension(bias, 0), num_units); in prepare() 222 output.dimensions = {batch_size, num_units}; in prepare()
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D | RNNTest.cpp | 212 uint32_t num_units() const { return units_; } in num_units() function in android::nn::wrapper::BasicRNNOpModel 325 float* golden_start = rnn_golden_output + i * rnn.num_units(); in TEST() 326 float* golden_end = golden_start + rnn.num_units(); in TEST()
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D | SVDFTest.cpp | 318 int num_units() const { return units_; } in num_units() function in android::nn::wrapper::SVDFOpModel 363 const int svdf_num_units = svdf.num_units(); in TEST() 425 const int svdf_num_units = svdf.num_units(); in TEST()
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