| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_ResourceApplyAdadelta.pbtxt | 34 name: "epsilon"
55 update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
|
| api_def_ResourceApplyRMSProp.pbtxt | 34 name: "epsilon"
60 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
63 mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
|
| api_def_ResourceSparseApplyRMSProp.pbtxt | 34 name: "epsilon"
66 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
69 mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
|
| api_def_SparseApplyRMSProp.pbtxt | 34 name: "epsilon"
72 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
75 mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
|
| /external/eigen/Eigen/src/SparseCore/ |
| SparseView.h | 38 * entries smaller than \c reference * \c epsilon are removed.
55 const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
56 : m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
69 RealScalar epsilon() const { return m_epsilon; } function in class:Eigen::SparseView
117 while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon()))
186 while((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon()))
210 * \a reference * \a epsilon removed.
218 * S = D.sparseView(reference,epsilon);
221 * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
226 const typename NumTraits<Scalar>::Real& epsilon) cons
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/service/gpu/ |
| cudnn_batchnorm_rewriter.cc | 47 // cudnn defines CUDNN_BN_MIN_EPSILON = 1e-5 as the minimum acceptable epsilon
50 return batch_norm->epsilon() >= 1e-5;
69 HloInstruction* epsilon = computation_->AddInstruction( local
70 HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon())));
77 operands.push_back(epsilon);
104 HloInstruction* epsilon = computation_->AddInstruction( local
105 HloInstruction::CreateConstant(Literal::CreateR0(batch_norm->epsilon())));
112 operands.push_back(epsilon);
121 // {output, mean, rsqrt(variance + epsilon)},
134 variance_plus_epsilon, epsilon));
167 HloInstruction* epsilon = computation_->AddInstruction( local
[all...] |
| /art/test/053-wait-some/src/ |
| Main.java | 60 long epsilon = delay / 10; local
61 if (epsilon > 50) {
62 epsilon = 50;
65 long min = delay - epsilon;
66 long max = delay + epsilon;
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/linear/ |
| SimplexSolver.java | 39 protected final double epsilon; field in class:SimplexSolver
50 * @param epsilon the amount of error to accept in floating point comparisons
52 public SimplexSolver(final double epsilon) {
53 this.epsilon = epsilon;
65 if (MathUtils.compareTo(tableau.getEntry(0, i), minValue, epsilon) < 0) {
86 if (MathUtils.compareTo(entry, 0, epsilon) > 0) {
88 if (MathUtils.equals(ratio, minRatio, epsilon)) {
106 if (MathUtils.equals(tableau.getEntry(row, column), 1, epsilon) &&
165 if (!MathUtils.equals(tableau.getEntry(0, tableau.getRhsOffset()), 0, epsilon)) {
[all...] |
| /cts/tests/tests/animation/src/android/animation/cts/ |
| InterpolatorTest.java | 36 private static final float EPSILON = 0.00001f;
57 assertEquals(0f, interpolator.getInterpolation(0), EPSILON);
58 assertEquals(1f, interpolator.getInterpolation(1), EPSILON);
62 assertEquals(turningPointY, interpolator.getInterpolation(turningPointX), EPSILON);
66 for (float fraction = EPSILON; fraction < turningPointX; fraction += 0.05f) {
73 for (float fraction = turningPointX + EPSILON; fraction < 1f; fraction += 0.05f) {
79 assertEquals(turningPointY, interpolator.getInterpolation(turningPointX), EPSILON);
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| fused_batchnorm_test.py | 33 def _reference_training(self, x, scale, offset, epsilon, data_format):
42 normalized = (x - mean) / np.sqrt(var + epsilon)
45 def _reference_grad(self, x, grad_y, scale, mean, var, epsilon, data_format):
48 # sum(grad_y * (x - mean)) * rsqrt(var + epsilon)
53 # 1/N * scale * rsqrt(var + epsilon) * (N * grad_y - sum(grad_y) -
54 # (x - mean) * sum(grad_y * (x - mean)) / (var + epsilon))
60 (var + epsilon)) / np.sqrt(var + epsilon)
62 grad_y * (x - mean) / np.sqrt(var + epsilon), axis=(0, 1, 2))
81 epsilon = 0.00
[all...] |
| /external/tensorflow/tensorflow/python/layers/ |
| normalization_test.py | 314 epsilon = 1e-3
316 axis=1, epsilon=epsilon, momentum=0.9)
334 normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
351 normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
356 epsilon = 1e-3
358 axis=2, epsilon=epsilon, momentum=0.9)
374 normed_np_output = ((np_output - epsilon) * np_gamma) + np_beta
391 normed_np_output = ((np_output - epsilon) * np_gamma) + np_bet
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/special/ |
| Beta.java | 66 * @param epsilon When the absolute value of the nth item in the
67 * series is less than epsilon the approximation ceases
73 double epsilon) throws MathException
75 return regularizedBeta(x, a, b, epsilon, Integer.MAX_VALUE);
110 * @param epsilon When the absolute value of the nth item in the
111 * series is less than epsilon the approximation ceases
118 final double b, double epsilon, int maxIterations) throws MathException
127 ret = 1.0 - regularizedBeta(1.0 - x, b, a, epsilon, maxIterations);
153 FastMath.log(a) - logBeta(a, b, epsilon, maxIterations)) *
154 1.0 / fraction.evaluate(x, epsilon, maxIterations)
[all...] |
| /external/vulkan-validation-layers/libs/glm/gtx/ |
| vector_query.hpp | 58 GLM_FUNC_DECL bool areCollinear(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
63 GLM_FUNC_DECL bool areOrthogonal(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
68 GLM_FUNC_DECL bool isNormalized(vecType<T, P> const & v, T const & epsilon);
73 GLM_FUNC_DECL bool isNull(vecType<T, P> const & v, T const & epsilon);
78 GLM_FUNC_DECL vecType<bool, P> isCompNull(vecType<T, P> const & v, T const & epsilon);
83 GLM_FUNC_DECL bool areOrthonormal(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
|
| epsilon.hpp | 25 # pragma message("GLM: GLM_GTX_epsilon extension is deprecated, include GLM_GTC_epsilon (glm/gtc/epsilon) instead")
29 #include "../gtc/epsilon.hpp"
|
| /packages/apps/Messaging/src/com/android/messaging/util/ |
| CubicBezierInterpolator.java | 75 final float epsilon = 1e-6f; local
91 double derivative = (getX(t + epsilon) - value) / epsilon;
92 if (Math.abs(value - x) < epsilon) {
94 } else if (Math.abs(derivative) < epsilon) {
108 for (int i = 0; Math.abs(value - x) > epsilon && i < iterations; i++) {
|
| /prebuilts/ndk/r16/sources/third_party/vulkan/src/libs/glm/gtx/ |
| vector_query.hpp | 58 GLM_FUNC_DECL bool areCollinear(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
63 GLM_FUNC_DECL bool areOrthogonal(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
68 GLM_FUNC_DECL bool isNormalized(vecType<T, P> const & v, T const & epsilon);
73 GLM_FUNC_DECL bool isNull(vecType<T, P> const & v, T const & epsilon);
78 GLM_FUNC_DECL vecType<bool, P> isCompNull(vecType<T, P> const & v, T const & epsilon);
83 GLM_FUNC_DECL bool areOrthonormal(vecType<T, P> const & v0, vecType<T, P> const & v1, T const & epsilon);
|
| epsilon.hpp | 25 # pragma message("GLM: GLM_GTX_epsilon extension is deprecated, include GLM_GTC_epsilon (glm/gtc/epsilon) instead")
29 #include "../gtc/epsilon.hpp"
|
| /external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
| chkder.h | 25 const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
26 const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon();
56 if (temp > NumTraits<Scalar>::epsilon() && temp < eps)
|
| /packages/screensavers/PhotoTable/src/com/android/dreams/phototable/ |
| SoftLandingInterpolator.java | 42 final float epsilon = Math.min(mI / 2f, (1f - mI) / 2f); local
43 bottom = mI - epsilon;
44 top = mI + epsilon;
|
| /external/mesa3d/docs/ |
| conform.html | 114 Epsilon Report.
115 zero error epsilon = 0.000122.
116 RGBA error epsilon = 0.0324, 0.016, 0.0324, 0.000122.
117 Depth buffer error epsilon = 0.000137.
118 Stencil plane error epsilon = 0.00404.
119 Accumulation error epsilon = 0.000137, 0.000137, 0.000137, 0.000137.
227 Epsilon Report.
228 zero error epsilon = 0.000122.
229 RGBA error epsilon = 0.0324, 0.016, 0.0324, 0.000122.
230 Depth buffer error epsilon = 0.000137
[all...] |
| /external/replicaisland/src/com/replica/replicaisland/ |
| Utils.java | 21 private static final float EPSILON = 0.0001f;
24 return close(a, b, EPSILON);
27 public final static boolean close(float a, float b, float epsilon) {
28 return Math.abs(a - b) < epsilon;
|
| /external/eigen/test/ |
| bicgstab.cpp | 20 bicgstab_colmajor_diag.setTolerance(NumTraits<T>::epsilon()*4);
21 bicgstab_colmajor_ilut.setTolerance(NumTraits<T>::epsilon()*4);
|
| /external/tensorflow/tensorflow/core/kernels/ |
| fused_batch_norm_op.h | 33 // in v5, y = bnScale * (x - mean) / sqrt(variance + epsilon) + bnBias
38 void operator()(const Eigen::GpuDevice& d, const T* variance, double epsilon,
48 void operator()(const Eigen::GpuDevice& d, double epsilon, int sample_size,
67 const Tensor& pop_variance_input, U epsilon,
99 // scale_backprop = y_backprop * ((x - pop_mean) * rsqrt(pop_var + epsilon))
100 // x_backprop = y_backprop * (scale * rsqrt(pop_var + epsilon))
108 // scratch1 = rsqrt(pop_var + epsilon)
109 scratch1.device(d) = (pop_var + pop_var.constant(epsilon)).rsqrt();
|
| /external/valgrind/docs/internals/ |
| SPEC-notes.txt | 22 (std::numeric_limits<double>::epsilon() / 100),
24 double>::epsilon() *5));
|
| /external/protobuf/js/binary/ |
| decoder_test.js | 54 * @param {number} epsilon
60 writeValue, epsilon, upperLimit, filter) {
65 writeValue.call(encoder, filter(epsilon));
69 for (var cursor = epsilon; cursor < upperLimit; cursor *= 1.1) {
77 assertEquals(filter(epsilon), readValue.call(decoder));
81 for (var cursor = epsilon; cursor < upperLimit; cursor *= 1.1) {
95 * @param {number} epsilon
102 writeValue, epsilon, lowerLimit, upperLimit, filter) {
107 writeValue.call(encoder, filter(-epsilon));
109 writeValue.call(encoder, filter(epsilon));
[all...] |
