| /external/tensorflow/tensorflow/tools/api/golden/ |
| tensorflow.keras.applications.vgg16.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.vgg19.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| tensorflow.keras.applications.xception.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
|
| /external/swiftshader/third_party/PowerVR_SDK/Examples/Advanced/ChameleonMan/ |
| ChameleonMan.txt | 10 Matrix Skinning is the act of animating a vertex over time given a set (palette) of matrices and a known set of blend weights assigned to those matrices. The ChameleonMan model has 19 bones and an animation cycle of 16 frames.
12 For each frame the Matrix Palette is recomputed based on time. For example to render the model at time point 5.25 the application would linearly blend between the matrices stored for frame 5 and 6 using weights of 0.75 and 0.25 respectively.
14 Per vertex, up to 3 matrices, from the palette, along with 3 weights are used by the vertex shader to update the vertex position to obtain the current animation frame position.
|
| /frameworks/base/libs/hwui/utils/ |
| Blur.h | 37 static void generateGaussianWeights(float* weights, float radius);
38 static void horizontal(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest,
40 static void vertical(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest,
|
| /frameworks/ml/nn/runtime/test/generated/models/ |
| rnn.model.cpp | 11 auto weights = model->addOperand(&type1); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
|
| rnn_relaxed.model.cpp | 11 auto weights = model->addOperand(&type1); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
|
| rnn_state.model.cpp | 11 auto weights = model->addOperand(&type1); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
|
| rnn_state_relaxed.model.cpp | 11 auto weights = model->addOperand(&type1); local
21 model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
24 {input, weights, recurrent_weights, bias, hidden_state_in},
|
| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| 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:
|
| 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],
|
| 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:
|
| 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],
|
| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| fully_connected_float_large_weights_as_inputs_relaxed.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)
29 weights:
|
| fully_connected_float_weights_as_inputs_relaxed.mod.py | 19 weights = Input("op2", "TENSOR_FLOAT32", "{1, 1}") variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
29 weights: [2],
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| metric_spec.py | 31 '`labels`, `predictions`, and optionally `weights`.')
56 _CANONICAL_WEIGHTS_ARG = 'weights'
111 and `weights`, and invokes `metric_fn` according to the following rules:
125 is_weights_required: Whether `weights` is a required arg.
128 Function accepting only named args `labels, `predictions`, and `weights`,
156 _sentinel=None, labels=None, predictions=None, weights=None):
162 if weights is not None:
163 kwargs[weights_arg] = weights
172 _sentinel=None, labels=None, predictions=None, weights=None):
177 if weights is None
[all...] |
| /external/tensorflow/tensorflow/compiler/tests/ |
| lstm.py | 41 """Returns the shape of the weights for a single LSTM cell."""
47 def LSTMCell(weights, m_prev, c_prev, x, pad):
51 weights: Weight matrix with shape LSTMCellWeightsShape.
62 # Apply weights to the input and previous hidden state.
65 xmw = math_ops.matmul(xm, weights)
85 def LSTMLayer(cell_name, weights, m, c, x_seq, pad_seq):
93 weights: Weight matrix with shape LSTMCellWeightsShape.
113 m, c = LSTMCell(weights, m, c, x_seq[seq], pad_seq[seq])
138 """Builds a single LSTM layer with random weights and inputs.
147 (out_seq, weights) pair. The out_seq is a list of per-sequence-ste
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 28 def per_example_logistic_loss(labels, weights, predictions):
29 """Logistic loss given labels, example weights and predictions.
33 weights: Rank 2 (N, 1) tensor of per-example weights.
43 return unweighted_loss * weights, control_flow_ops.no_op()
49 def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15):
57 weights: Rank 2 (N, 1) tensor of per-example weights.
104 if weights is None:
107 return unweighted_loss * weights, control_flow_ops.no_op(
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
| inception_resnet_v2_test.py | 30 model = keras.applications.InceptionResNetV2(weights=None)
34 model = keras.applications.InceptionResNetV2(weights=None,
39 model = keras.applications.InceptionResNetV2(weights=None,
46 keras.applications.InceptionResNetV2(weights='unknown',
49 keras.applications.InceptionResNetV2(weights='imagenet',
|
| inception_v3_test.py | 30 model = keras.applications.InceptionV3(weights=None)
34 model = keras.applications.InceptionV3(weights=None, include_top=False)
38 model = keras.applications.InceptionV3(weights=None,
45 keras.applications.InceptionV3(weights='unknown',
48 keras.applications.InceptionV3(weights='imagenet',
|
| resnet50_test.py | 28 model = keras.applications.ResNet50(weights=None)
32 model = keras.applications.ResNet50(weights=None, include_top=False)
36 model = keras.applications.ResNet50(weights=None,
43 keras.applications.ResNet50(weights='unknown',
47 keras.applications.ResNet50(weights='imagenet',
|
| vgg16_test.py | 28 model = keras.applications.VGG16(weights=None)
32 model = keras.applications.VGG16(weights=None, include_top=False)
36 model = keras.applications.VGG16(weights=None,
43 keras.applications.VGG16(weights='unknown',
46 keras.applications.VGG16(weights='imagenet',
|
| vgg19_test.py | 28 model = keras.applications.VGG19(weights=None)
32 model = keras.applications.VGG19(weights=None, include_top=False)
36 model = keras.applications.VGG19(weights=None,
43 keras.applications.VGG19(weights='unknown',
46 keras.applications.VGG19(weights='imagenet',
|
| xception_test.py | 30 model = keras.applications.Xception(weights=None)
34 model = keras.applications.Xception(weights=None, include_top=False)
38 model = keras.applications.Xception(weights=None,
45 keras.applications.Xception(weights='unknown',
48 keras.applications.Xception(weights='imagenet',
|
| /hardware/intel/common/utils/ituxd/src/com/intel/thermal/ |
| VirtualThermalZone.java | 89 Integer weights[] = sa.getWeights(); local
90 int m = weights[0];
213 Integer weights[], order[]; local
221 weights = sa.getWeights();
223 if (weights == null && order == null) return rawSensorTemp;
224 if (weights != null) {
227 return (weights[0] * rawSensorTemp) / 1000;
228 } else if (order != null && weights.length == order.length) {
230 // it should be of same size as weights array
232 for (int i = 0; i < weights.length; i++)
[all...] |
