Searched refs:numUnits (Results 1 – 5 of 5) sorted by relevance
/packages/modules/NeuralNetworks/common/cpu_operations/ |
D | QLSTM.cpp | 73 const uint32_t numUnits = getSizeOfDimension(inputToOutputShape, 0); in prepare() local 77 NN_RET_CHECK_EQ(getSizeOfDimension(recurrentToOutputShape, 0), numUnits); in prepare() 83 NN_RET_CHECK_EQ(getSizeOfDimension(inputToInputShape, 0), numUnits); in prepare() 89 NN_RET_CHECK_EQ(getSizeOfDimension(inputToForgetShape, 0), numUnits); in prepare() 93 NN_RET_CHECK_EQ(getSizeOfDimension(inputToCellShape, 0), numUnits); in prepare() 99 NN_RET_CHECK_EQ(getSizeOfDimension(recurrentToInputShape, 0), numUnits); in prepare() 105 NN_RET_CHECK_EQ(getSizeOfDimension(recurrentToForgetShape, 0), numUnits); in prepare() 109 NN_RET_CHECK_EQ(getSizeOfDimension(recurrentToCellShape, 0), numUnits); in prepare() 123 NN_RET_CHECK_EQ(getSizeOfDimension(cellToInputShape, 0), numUnits); in prepare() 129 NN_RET_CHECK_EQ(getSizeOfDimension(cellToForgetShape, 0), numUnits); in prepare() [all …]
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D | UnidirectionalSequenceRNN.cpp | 87 const uint32_t numUnits = getSizeOfDimension(weightsShape, 0); in executeTyped() local 100 output += batchSize * numUnits; in executeTyped() 111 std::copy(hiddenState, hiddenState + batchSize * numUnits, stateOutput); in executeTyped() 131 const uint32_t numUnits = getSizeOfDimension(weights, 0); in prepare() local 141 NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(bias, 0)); in prepare() 142 NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(recurrentWeights, 0)); in prepare() 143 NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(recurrentWeights, 1)); in prepare() 145 NN_RET_CHECK_EQ(numUnits, getSizeOfDimension(hiddenState, 1)); in prepare() 151 output.dimensions[2] = numUnits; in prepare() 158 outputStateShape.dimensions[1] = numUnits; in prepare()
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D | RNN.cpp | 123 uint32_t numUnits = weightsShape.dimensions[0]; in RNNStep() local 128 /*outputBatchStride=*/numUnits, /*outputBatchOffset=*/0, outputData); in RNNStep()
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/packages/modules/NeuralNetworks/runtime/test/ |
D | TestValidateOperations.cpp | 3967 const uint32_t numUnits = 5; in bidirectionlSequenceRNNTest() local 3970 uint32_t weightsDims[2] = {inputSize, numUnits}; in bidirectionlSequenceRNNTest() 3971 uint32_t recurrentWeightsDims[2] = {numUnits, numUnits}; in bidirectionlSequenceRNNTest() 3972 uint32_t biasDims[1] = {numUnits}; in bidirectionlSequenceRNNTest() 3973 uint32_t hiddenStateDims[2] = {batchSize, numUnits}; in bidirectionlSequenceRNNTest() 3974 uint32_t outputDims[2] = {batchSize, numUnits}; in bidirectionlSequenceRNNTest() 4043 const uint32_t numUnits = 5; in unidirectionlSequenceRNNTest() local 4046 uint32_t weightsDims[2] = {inputSize, numUnits}; in unidirectionlSequenceRNNTest() 4047 uint32_t recurrentWeightsDims[2] = {numUnits, numUnits}; in unidirectionlSequenceRNNTest() 4048 uint32_t biasDims[1] = {numUnits}; in unidirectionlSequenceRNNTest() [all …]
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/packages/modules/NeuralNetworks/tools/api/ |
D | types.spec | 6047 * A 2-D tensor of shape [numUnits, inputSize]. 6049 * A 2-D tensor of shape [numUnits, numUnits]. 6051 * A 1-D tensor of shape [numUnits]. 6053 * A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden 6065 * numUnits], otherwise the output has a shape [batchSize, maxTime, 6066 * numUnits]. 6068 * * 1: A tensor of shape [batchSize, numUnits] containing hidden state 6232 * Shape: [numUnits, inputSize] 6235 * Shape: [numUnits, inputSize] 6238 * Shape: [numUnits, inputSize] [all …]
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