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    Searched refs:hidden_state_out (Results 1 - 8 of 8) sorted by null

  /frameworks/ml/nn/runtime/test/specs/V1_0/
rnn_state.mod.py 31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
105 hidden_state_out : [
rnn.mod.py 31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
195 hidden_state_out: [0 for x in range(batches * units)],
  /frameworks/ml/nn/runtime/test/specs/V1_1/
rnn_state_relaxed.mod.py 31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
106 hidden_state_out : [
rnn_relaxed.mod.py 31 hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units)) variable
35 activation_param).To([hidden_state_out, output])
196 hidden_state_out: [0 for x in range(batches * units)],
  /frameworks/ml/nn/runtime/test/generated/models/
rnn.model.cpp 16 auto hidden_state_out = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
rnn_relaxed.model.cpp 16 auto hidden_state_out = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
rnn_state.model.cpp 16 auto hidden_state_out = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});
rnn_state_relaxed.model.cpp 16 auto hidden_state_out = model->addOperand(&type4); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
25 {hidden_state_out, output});

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