| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| loss_test.py | 55 weights = [
59 weights = array_ops.stack(weights, axis=1)
62 logits, targets, weights,
69 logits, targets, weights,
77 logits, targets, weights,
85 logits, targets, weights,
|
| /external/deqp/external/openglcts/modules/glesext/gpu_shader5/ |
| esextcGPUShader5PreciseQualifier.hpp | 54 * in vec4 weights;
75 * eval(positions, weights, weightedSum.x);
77 * weightedSum.y = eval(positions, weights);
81 * result = (positions.x* weights.x + positions.y* weights.y) +
82 * (positions.z* weights.z + positions.w* weights.w);
85 * weightedSum.w = (positions.x* weights.x + positions.y* weights.y) +
86 * (positions.z* weights.z + positions.w* weights.w)
[all...] |
| esextcGPUShader5PreciseQualifier.cpp | 60 "layout(location = 1) in vec4 weights;\n"
76 " eval(positions, weights, weightedSum.x);\n"
78 " weightedSum.y = eval(positions, weights);\n"
82 " result = (positions.x * weights.x + positions.y * weights.y) +\n"
83 " (positions.z * weights.z + positions.w * weights.w);\n"
85 " weightedSum.w = (positions.x * weights.x + positions.y * weights.y) + \n"
86 " (positions.z * weights.z + positions.w * weights.w);\n
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
| vgg16.py | 56 weights='imagenet',
63 Optionally loads weights pre-trained
69 The model and the weights are compatible with both
77 weights: one of `None` (random initialization),
79 or the path to the weights file to be loaded.
102 if no `weights` argument is specified.
108 ValueError: in case of invalid argument for `weights`,
111 if not (weights in {'imagenet', None} or os.path.exists(weights)):
112 raise ValueError('The `weights` argument should be either
[all...] |
| vgg19.py | 56 weights='imagenet',
63 Optionally loads weights pre-trained
69 The model and the weights are compatible with both
77 weights: one of `None` (random initialization),
79 or the path to the weights file to be loaded.
102 if no `weights` argument is specified.
108 ValueError: in case of invalid argument for `weights`,
111 if not (weights in {'imagenet', None} or os.path.exists(weights)):
112 raise ValueError('The `weights` argument should be either
[all...] |
| densenet.py | 129 weights='imagenet',
136 Optionally loads weights pre-trained
142 The model and the weights are compatible with
151 weights: one of `None` (random initialization),
153 or the path to the weights file to be loaded.
174 if no `weights` argument is specified.
180 ValueError: in case of invalid argument for `weights`,
183 if not (weights in {'imagenet', None} or os.path.exists(weights)):
184 raise ValueError('The `weights` argument should be either
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| target_column.py | 46 weights. It is used to down weight or boost examples during training. It
77 weights. It is used to down weight or boost examples during training. It
112 weights. It is used to down weight or boost examples during training. It
143 weights. It is used to down weight or boost examples during training. It
206 should respect the example weights when computing the gradient.
427 def _run_metrics(predictions, labels, metrics, weights):
431 if weights is not None:
432 result[name] = metric(predictions, labels, weights=weights)
475 def _float_weights_or_none(weights)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| sdca_internal.h | 63 // Logits for each class, using the previous weights.
99 const Eigen::Tensor<float, 1, Eigen::RowMajor> weights) const {
100 // Proximal step on the weights which is sign(w)*|w - shrinkage|+.
101 return weights.sign() * ((weights.abs() - weights.constant(shrinkage_))
102 .cwiseMax(weights.constant(0.0)));
107 const Eigen::Tensor<float, 2, Eigen::RowMajor> weights) const {
108 // Proximal step on the weights which is sign(w)*|w - shrinkage|+.
109 return weights.sign() * ((weights.abs() - weights.constant(shrinkage_)
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
| LoessInterpolator.java | 189 * @param weights point weights: coefficients by which the robustness weight of a point is multiplied
199 public final double[] smooth(final double[] xval, final double[] yval, final double[] weights)
214 checkAllFiniteReal(weights, LocalizedFormats.NON_REAL_FINITE_WEIGHT);
242 // starting with all robustness weights set to 1.
254 updateBandwidthInterval(xval, weights, i, bandwidthInterval);
270 // the product of robustness weights and the tricube
286 final double w = tricube(dist * denom) * robustnessWeights[k] * weights[k];
313 // No need to recompute the robustness weights at the last
319 // Recompute the robustness weights
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ |
| metric_spec_test.py | 35 def _fn0(predictions, labels, weights=None):
38 self.assertEqual("f2_value", weights)
41 def _fn1(predictions, targets, weights=None):
44 self.assertEqual("f2_value", weights)
222 def _fn(predictions, labels, weights=None):
223 del labels, predictions, weights
238 def _fn(labels, predictions, weights=None):
239 del labels, predictions, weights
254 def _fn(predictions, labels, weights=None):
257 self.assertEqual("f2_value", weights)
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/nonstiff/ |
| DormandPrince853Integrator.java | 69 /** Internal weights Butcher array. */
136 /** Propagation weights Butcher array. */
153 /** First error weights array, element 1. */
158 /** First error weights array, element 6. */
161 /** First error weights array, element 7. */
164 /** First error weights array, element 8. */
167 /** First error weights array, element 9. */
170 /** First error weights array, element 10. */
173 /** First error weights array, element 11. */
176 /** First error weights array, element 12. *
[all...] |
| /external/tensorflow/tensorflow/examples/android/jni/object_tracking/ |
| frame_pair.cc | 36 static float weights[kMaxKeypoints]; local
38 memset(weights, 0.0f, sizeof(*weights) * kMaxKeypoints);
42 FillWeights(resized_box, weights);
45 const Point2f translation = GetWeightedMedian(weights, deltas);
52 FillScales(old_center, translation, weights, deltas);
71 const float scale_factor = GetWeightedMedianScale(weights, deltas);
81 float* const weights) const {
95 weights[i] = 0.0f;
131 weights[i] = distance_score * intrinsic_score
[all...] |
| /frameworks/base/libs/hwui/utils/ |
| Blur.cpp | 61 void Blur::generateGaussianWeights(float* weights, float radius) {
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, const uint8_t* source, uint8_t* dest,
105 const float* gPtr = weights;
137 void Blur::vertical(float* weights, int32_t radius, const uint8_t* source, uint8_t* dest,
147 const float* gPtr = weights;
[all...] |
| /external/tensorflow/tensorflow/core/lib/random/ |
| distribution_sampler.cc | 25 const gtl::ArraySlice<float>& weights) {
26 DCHECK(!weights.empty());
27 int n = weights.size();
35 sum += weights[i];
45 // compute proportional weights
47 double p = (weights[i] * n) / sum;
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/integration/ |
| LegendreGaussIntegrator.java | 45 * Legendre polynomial. The weights a<sub>i</sub> of the quadrature formula
62 /** Weights for the 2 points method. */
75 /** Weights for the 3 points method. */
90 /** Weights for the 4 points method. */
107 /** Weights for the 5 points method. */
119 /** Weights for the current method. */
120 private final double[] weights; field in class:LegendreGaussIntegrator
135 weights = WEIGHTS_2;
139 weights = WEIGHTS_3;
143 weights = WEIGHTS_4
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| confusion_matrix_ops.py | 26 name=None, weights=None):
30 weights=weights)
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| constraints.py | 44 Constrains the weights incident to each hidden unit
48 m: the maximum norm for the incoming weights.
58 to constrain the weights of each filter tensor of size
78 """Constrains the weights to be non-negative.
87 """Constrains the weights incident to each hidden unit to have unit norm.
99 to constrain the weights of each filter tensor of size
118 Constrains the weights incident to each hidden unit
122 min_value: the minimum norm for the incoming weights.
123 max_value: the maximum norm for the incoming weights.
124 rate: rate for enforcing the constraint: weights will b
[all...] |
| testing_utils.py | 97 weights = layer.get_weights()
98 layer.set_weights(weights)
100 # test and instantiation from weights
101 if 'weights' in tf_inspect.getargspec(layer_cls.__init__):
102 kwargs['weights'] = weights
141 if model.weights:
142 weights = model.get_weights()
143 recovered_model.set_weights(weights)
178 if model.weights
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/proto/ |
| quantiles.proto | 20 // Rank is 0.0 for the absolute minimum and sum of the weights for the maximum
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| regression_test.py | 36 weights = 10 * rng.randn(n_weights)
37 y = np.dot(x, weights)
45 # Have to flatten weights since they come in (x, 1) shape.
46 self.assertAllClose(weights, regressor_weights.flatten(), rtol=0.01)
|
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| sampling_ops_test.py | 154 weights=self._weights(),
173 weights=self._weights(),
191 weights=self._weights(),
204 def _testCompareWithNN(self, weights, biases, partition_strategy):
207 weights=weights(),
220 weights=weights(),
254 weights = [[1., 2.], [3., 4.]] # two sampled classes
256 # Let w0, w1 = weights of sampled classes (biases set to 0 for simplicity
[all...] |
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| resample.py | 101 def weighted_resample(inputs, weights, overall_rate, scope=None,
106 selection is proportional to its value in `weights`, and the average
112 weights: A `[batch_size]`-shaped tensor with each batch member's weight.
123 # Algorithm: Just compute rates as weights/mean_weight *
134 initializer=math_ops.cast(0, weights.dtype),
135 dtype=weights.dtype)
137 batch_mean = math_ops.reduce_mean(weights)
141 # Then, normalize the weights into rates using the mean weight and
143 rates = weights * overall_rate / mean
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_SdcaShrinkL1.pbtxt | 4 name: "weights"
|
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist.py | 58 weights = tf.Variable(
61 name='weights')
64 hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
67 weights = tf.Variable(
70 name='weights')
73 hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
76 weights = tf.Variable(
79 name='weights')
82 logits = tf.matmul(hidden2, weights) + biases
|
| /frameworks/base/media/mca/filterpacks/java/android/filterpacks/imageproc/ |
| SaturateFilter.java | 45 "uniform vec3 weights;\n" +
49 " float kv = dot(color.rgb, weights) + shift;\n" +
57 "uniform vec3 weights;\n" +
62 " float de = dot(color.rgb, weights);\n" +
142 float weights[] = { 2f/8f, 5f/8f, 1f/8f}; local
144 mBenProgram.setHostValue("weights", weights);
147 mHerfProgram.setHostValue("weights", weights);
|
