| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| rnn_state.mod.py | 25 recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
62 recurrent_weights: [
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| rnn.mod.py | 25 recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
62 recurrent_weights: [
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| rnn_state_relaxed.mod.py | 25 recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
63 recurrent_weights: [
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| rnn_relaxed.mod.py | 25 recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units)) variable
34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
63 recurrent_weights: [
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| rnn.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
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| rnn_relaxed.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
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| rnn_state.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
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| rnn_state_relaxed.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
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| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| basic_rnn.cc | 49 TfLiteTensor* recurrent_weights = local
59 TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[0], bias->dims->data[0]);
60 TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[1], bias->dims->data[0]);
92 TfLiteTensor* recurrent_weights = local
111 // Initialize input_weights and recurrent_weights.
113 const float* recurrent_weights_ptr = recurrent_weights->data.f;
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| bidirectional_sequence_rnn_test.cc | 636 constexpr std::initializer_list<float> recurrent_weights = { member in namespace:tflite::__anon39246
[all...] |
| unidirectional_sequence_rnn.cc | 49 TfLiteTensor* recurrent_weights = local
64 TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[0], bias->dims->data[0]);
65 TF_LITE_ASSERT_EQ(recurrent_weights->dims->data[1], bias->dims->data[0]);
98 TfLiteTensor* recurrent_weights = local
116 // Initialize input_weights and recurrent_weights.
118 const float* recurrent_weights_ptr = recurrent_weights->data.f;
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| /frameworks/ml/nn/common/operations/ |
| RNN.cpp | 53 const RunTimeOperandInfo *recurrent_weights = local
64 NN_CHECK_EQ(SizeOfDimension(recurrent_weights, 0), SizeOfDimension(bias, 0));
65 NN_CHECK_EQ(SizeOfDimension(recurrent_weights, 1), SizeOfDimension(bias, 0));
102 // Initialize input_weights and recurrent_weights.
120 // Output += recurrent_weights * hidden_state
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