Searched refs:recurrent_weights (Results 1 - 5 of 5) sorted by relevance
| /frameworks/ml/nn/runtime/test/specs/ |
| H A D | 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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| H A D | 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/generated/models/ |
| H A D | rnn.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
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| H A D | rnn_state.model.cpp | 12 auto recurrent_weights = model->addOperand(&type2); local
19 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
22 {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
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| /frameworks/ml/nn/common/operations/ |
| H A D | 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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