/frameworks/ml/nn/runtime/test/specs/ |
H A D | fully_connected_float.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_float_large.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 5}", [2, 3, 4, 5, 6]) # num_units = 1, input_size = 5 variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_float_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 5}") # num_units = 1, input_size = 5 variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 28 weights:
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H A D | fully_connected_float_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 28 weights: [2],
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H A D | fully_connected_quant8.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0", [2]) variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_quant8_large.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0", [10, 20, 20, 20, 10]) # num_units = 1, input_size = 5 variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
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H A D | fully_connected_quant8_large_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0") # num_units = 1, input_size = 5 variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 28 weights:
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H A D | fully_connected_quant8_weights_as_inputs.mod.py | 19 weights = Input("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0") variable 23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0) 28 weights: [2],
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H A D | rnn_state.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable 34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in, 38 weights: [
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H A D | rnn.mod.py | 24 weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size)) variable 34 model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in, 38 weights: [
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/frameworks/ml/nn/runtime/test/generated/models/ |
H A D | rnn.model.cpp | 11 auto weights = model->addOperand(&type1); 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 | 11 auto weights = model->addOperand(&type1); 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/base/libs/hwui/utils/ |
H A D | Blur.cpp | 61 void Blur::generateGaussianWeights(float* weights, float radius) { argument 64 // Compute gaussian weights for the blur 83 weights[r + intRadius] = coeff1 * pow(e, floatR * floatR * coeff2); 84 normalizeFactor += weights[r + intRadius]; 87 //Now we need to normalize the weights because all our coefficients need to add up to one 90 weights[r + intRadius] *= normalizeFactor; 94 void Blur::horizontal(float* weights, int32_t radius, argument 106 const float* gPtr = weights; 138 void Blur::vertical(float* weights, int32_t radius, argument 148 const float* gPtr = weights; [all...] |
/frameworks/support/v17/leanback/src/android/support/v17/leanback/widget/ |
H A D | ParallaxEffect.java | 95 * @param weights A list of Float objects that represents weight associated with each variable 99 public final void setWeights(float... weights) { argument 100 for (float weight : weights) { 108 for (float weight : weights) { 119 * @param weights A list of Float objects that represents weight associated with each variable 124 public final ParallaxEffect weights(float... weights) { argument 125 setWeights(weights); 257 // use weights user defined
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/frameworks/ml/nn/common/ |
H A D | CpuExecutor.cpp | 935 RunTimeOperandInfo& weights = mOperands[ins[1]]; local 944 success = fullyConnectedPrepare(input.shape(), weights.shape(), bias.shape(), 949 reinterpret_cast<const float*>(weights.buffer), 950 weights.shape(), 957 success = fullyConnectedPrepare(input.shape(), weights.shape(), bias.shape(), 962 reinterpret_cast<const uint8_t*>(weights.buffer), 963 weights.shape(),
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/frameworks/rs/driver/runtime/ |
H A D | rs_sample.c | 268 getBilinearSample1D(const Allocation_t *alloc, float2 weights, argument 276 return getSample_RGBA(p, iPixel, next, weights.x, weights.y); 278 return getSample_A(p, iPixel, next, weights.x, weights.y); 281 return getSample_565(p, iPixel, next, weights.x, weights.y); 283 return getSample_RGB(p, iPixel, next, weights.x, weights.y); 285 return getSample_L(p, iPixel, next, weights 440 float2 weights; local [all...] |