| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| H A D | rnn_state.mod.py | 29 activation_param = Int32Scalar("activation_param", 1) # Relu variable
35 activation_param).To([hidden_state_out, output])
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| H A D | svdf_state.mod.py | 30 activation_param = Int32Scalar("activation_param", 0) variable
35 rank_param, activation_param).To([state_out, output])
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| H A D | rnn.mod.py | 29 activation_param = Int32Scalar("activation_param", 1) # Relu variable
35 activation_param).To([hidden_state_out, output])
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| H A D | svdf.mod.py | 32 activation_param = Int32Scalar("activation_param", 0) variable
37 rank_param, activation_param).To([state_out, output])
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| H A D | svdf2.mod.py | 32 activation_param = Int32Scalar("activation_param", 0) variable
37 rank_param, activation_param).To([state_out, output])
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| H A D | lstm2_state.mod.py | 54 activation_param = Int32Scalar("activation_param", 4) # Tanh variable
91 activation_param,
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| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| H A D | rnn_state_relaxed.mod.py | 29 activation_param = Int32Scalar("activation_param", 1) # Relu variable
35 activation_param).To([hidden_state_out, output])
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| H A D | svdf_state_relaxed.mod.py | 30 activation_param = Int32Scalar("activation_param", 0) variable
35 rank_param, activation_param).To([state_out, output])
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| H A D | rnn_relaxed.mod.py | 29 activation_param = Int32Scalar("activation_param", 1) # Relu variable
35 activation_param).To([hidden_state_out, output])
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| H A D | svdf2_relaxed.mod.py | 32 activation_param = Int32Scalar("activation_param", 0) variable
37 rank_param, activation_param).To([state_out, output])
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| H A D | svdf_relaxed.mod.py | 32 activation_param = Int32Scalar("activation_param", 0) variable
37 rank_param, activation_param).To([state_out, output])
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| H A D | rnn.model.cpp | 15 auto activation_param = model->addOperand(&type5); local
20 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
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| H A D | rnn_relaxed.model.cpp | 15 auto activation_param = model->addOperand(&type5); local
20 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
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| H A D | rnn_state.model.cpp | 15 auto activation_param = model->addOperand(&type5); local
20 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
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| H A D | rnn_state_relaxed.model.cpp | 15 auto activation_param = model->addOperand(&type5); local
20 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
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| H A D | svdf.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | svdf2.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | svdf2_relaxed.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | svdf_relaxed.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | svdf_state.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | svdf_state_relaxed.model.cpp | 17 auto activation_param = model->addOperand(&type5); local
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
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| H A D | lstm.model.cpp | 34 auto activation_param = model->addOperand(&type7); local
43 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
48 model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
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| H A D | lstm2.model.cpp | 34 auto activation_param = model->addOperand(&type7); local
43 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
48 model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
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| H A D | lstm2_relaxed.model.cpp | 34 auto activation_param = model->addOperand(&type7); local
43 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
48 model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
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| /frameworks/ml/nn/tools/test_generator/tests/P_lstm/ |
| H A D | lstm.mod.py | 54 activation_param = Input("activation_param", "TENSOR_INT32", "{1}") variable
91 activation_param,
134 activation_param: [4], # Tanh
|