| /external/tensorflow/tensorflow/core/kernels/ |
| batch_norm_op.h | 106 // dg = out_backprop * ((x - m) * rsqrt(v + epsilon))
109 // (-1/2) * (v + epsilon) ^ (-3/2)
111 // dm = sum_over_rest(out_backprop * gamma) * (-1 / rsqrt(v + epsilon))
113 // dx = out_backprop * (gamma * rsqrt(v + epsilon))
116 // scratch1 = rsqrt(v + epsilon)
141 // scratch1 = - 1/2 * (var + epsilon) ^ (-3/2)
|
| fused_batch_norm_op.cc | 53 const Tensor& estimated_variance_input, U epsilon,
120 auto scaling_factor = ((variance + epsilon).rsqrt() * scale)
137 U epsilon, Tensor* x_backprop_output,
155 // x_backprop = scale * rsqrt(variance + epsilon) *
157 // mean(y_backprop * (x - mean(x))) / (variance + epsilon)]
159 // (x - mean(x)) * rsqrt(variance + epsilon))
186 auto coef0 = (variance + epsilon).rsqrt();
224 const Tensor& estimated_variance, U epsilon, Tensor* y,
318 [d, epsilon, estimated_variance,
326 VarianceToInvVariance<U>()(d, variance, epsilon, channels, inv_variance)
506 float epsilon; local
583 float epsilon; local
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| normalization.py | 46 epsilon: Small float added to variance to avoid dividing by zero.
79 epsilon=1e-3,
95 epsilon=epsilon,
122 'epsilon': self.epsilon,
|
| /device/linaro/bootloader/edk2/BaseTools/Source/C/VfrCompile/Pccts/support/rexpr/ |
| rexpr.c | 15 * Each non-epsilon arc consumes one character from 's'. Backtracking is
162 if ( p->label == Epsilon )
493 ArcBetweenGraphNodes(A.right, B.left, Epsilon);
509 ArcBetweenGraphNodes(g.left, A.left, Epsilon);
510 ArcBetweenGraphNodes(g.left, B.left, Epsilon);
512 ArcBetweenGraphNodes(A.right, g.right, Epsilon);
513 ArcBetweenGraphNodes(B.right, g.right, Epsilon);
549 ArcBetweenGraphNodes(g.left, A.left, Epsilon);
550 ArcBetweenGraphNodes(g.left, g.right, Epsilon);
551 ArcBetweenGraphNodes(A.right, g.right, Epsilon);
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| PoissonDistributionImpl.java | 70 private double epsilon = DEFAULT_EPSILON; field in class:PoissonDistributionImpl
88 * @param epsilon the convergence criteria for cumulative probabilites
92 public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
94 this.epsilon = epsilon;
102 * @param epsilon the convergence criteria for cumulative probabilites
105 public PoissonDistributionImpl(double p, double epsilon) {
107 this.epsilon = epsilon;
219 return Gamma.regularizedGammaQ((double) x + 1, mean, epsilon, maxIterations)
[all...] |
| /external/tensorflow/tensorflow/examples/android/jni/object_tracking/ |
| frame_pair.cc | 67 // the comparison to EPSILON in FillScales (which I've updated to return the
173 if (((dist2_x > EPSILON && dist1_x > EPSILON) ||
174 (dist2_x < -EPSILON && dist1_x < -EPSILON)) &&
175 ((dist2_y > EPSILON && dist1_y > EPSILON) ||
176 (dist2_y < -EPSILON && dist1_y < -EPSILON))) {
204 if (num_items == 0 || sum < EPSILON) {
[all...] |
| /external/deqp/external/openglcts/modules/glesext/tessellation_shader/ |
| esextcTessellationShaderUtils.cpp | 1325 const float epsilon = 1e-5f; local
1383 const float epsilon = 1e-5f; local
[all...] |
| /external/deqp/external/openglcts/modules/glesext/draw_buffers_indexed/ |
| esextcDrawBuffersIndexedColorMasks.cpp | 206 tcu::RGBA epsilon = GetEpsilon(); local
230 if (!VerifyImg(textureLevel, expected, epsilon))
252 if (!VerifyImg(textureLevel, expected, epsilon))
274 if (!VerifyImg(textureLevel, expected, epsilon))
409 tcu::UVec4 epsilon; local
414 epsilon[i] = de::min(
418 return tcu::RGBA(epsilon.x(), epsilon.y(), epsilon.z(), epsilon.w())
[all...] |
| esextcDrawBuffersIndexedBlending.cpp | 236 tcu::RGBA epsilon = GetEpsilon(); local
245 if (!VerifyImg(textureLevel, expected[i % 4], epsilon))
375 tcu::UVec4 epsilon; local
380 epsilon[i] = de::min(
384 return tcu::RGBA(epsilon.x(), epsilon.y(), epsilon.z(), epsilon.w());
388 tcu::RGBA epsilon)
395 if (!tcu::compareThreshold(pixel, expectedColor, epsilon))
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/ops/ |
| quantile_ops.py | 46 epsilon,
56 epsilon: Error bound on the quantile computation.
64 self._epsilon = epsilon
75 epsilon=epsilon,
150 epsilon=self._epsilon / 2).sparse_summaries[0]
158 epsilon=self._epsilon / 2).dense_summaries[0]
|
| /external/tensorflow/tensorflow/python/training/ |
| adadelta.py | 36 def __init__(self, learning_rate=0.001, rho=0.95, epsilon=1e-8,
44 epsilon: A `Tensor` or a floating point value. A constant epsilon used
53 self._epsilon = epsilon
68 self._epsilon_t = ops.convert_to_tensor(self._epsilon, name="epsilon")
|
| rmsprop_test.py | 40 # learning_rate, decay, momentum, epsilon, centered, use_resource
60 epsilon, centered):
62 denom_t = rms_t + epsilon
73 lr, decay, momentum, epsilon, centered):
82 denom_t = rms_t[gindex] + epsilon
93 epsilon, centered, use_resource) in _TESTPARAMS:
113 epsilon=epsilon,
149 decay, momentum, epsilon, centered)
152 decay, momentum, epsilon, centered
[all...] |
| /kernel/tests/net/test/ |
| resilient_rs_test.py | 100 EPSILON = 0.1
102 MIN_EXP = 1.9 - EPSILON
104 MAX_EXP = 2.1 + EPSILON
113 MIN_LIN = SOLICITATION_INTERVAL * (0.9 - EPSILON)
114 MAX_LIN = SOLICITATION_INTERVAL * (1.1 + EPSILON)
|
| /cts/tests/tests/animation/src/android/animation/cts/ |
| AnimatorSetTest.java | 71 private static final float EPSILON = 0.001f;
714 assertEquals(100f, (Float) a1.getAnimatedValue(), EPSILON);
715 assertEquals(150f, (Float) a2.getAnimatedValue(), EPSILON);
716 assertEquals(250f, (Float) a3.getAnimatedValue(), EPSILON);
719 assertEquals(150f, (Float) a1.getAnimatedValue(), EPSILON);
720 assertEquals(250f, (Float) a2.getAnimatedValue(), EPSILON);
721 assertEquals(280f, (Float) a3.getAnimatedValue(), EPSILON);
727 assertEquals(150f, (Float) a1.getAnimatedValue(), EPSILON);
728 assertEquals(250f, (Float) a2.getAnimatedValue(), EPSILON);
729 assertEquals(300f, (Float) a3.getAnimatedValue(), EPSILON);
[all...] |
| /external/eigen/test/ |
| prec_inverse_4x4.cpp | 22 double error = double( (m*inv-MatrixType::Identity()).norm() / NumTraits<Scalar>::epsilon() );
42 } while(absdet < NumTraits<Scalar>::epsilon());
44 double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits<Scalar>::epsilon() );
|
| /external/vulkan-validation-layers/libs/glm/gtc/ |
| constants.hpp | 54 /// Return the epsilon constant for floating point types.
55 /// @todo Implement epsilon for half-precision floating point type.
58 GLM_FUNC_DECL genType epsilon();
|
| /frameworks/base/packages/SystemUI/src/com/android/systemui/classifier/ |
| HistoryEvaluator.java | 29 private static final float EPSILON = 1e-5f;
101 return x <= EPSILON && x >= -EPSILON;
|
| /packages/apps/Dialer/java/com/android/incallui/answer/impl/classifier/ |
| HistoryEvaluator.java | 28 private static final float EPSILON = 1e-5f;
98 return x <= EPSILON && x >= -EPSILON;
|
| /prebuilts/ndk/r16/sources/third_party/vulkan/src/libs/glm/gtc/ |
| constants.hpp | 54 /// Return the epsilon constant for floating point types.
55 /// @todo Implement epsilon for half-precision floating point type.
58 GLM_FUNC_DECL genType epsilon();
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/util/ |
| ContinuedFraction.java | 81 * @param epsilon maximum error allowed.
85 public double evaluate(double x, double epsilon) throws MathException {
86 return evaluate(x, epsilon, Integer.MAX_VALUE);
121 * @param epsilon maximum error allowed.
126 public double evaluate(double x, double epsilon, int maxIterations)
136 while (n < maxIterations && relativeError > epsilon) {
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| losses.py | 45 diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None))
52 first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
53 second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
118 y_true = K.clip(y_true, K.epsilon(), 1)
119 y_pred = K.clip(y_pred, K.epsilon(), 1)
125 return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| linalg_grad_test.py | 70 # Optimal stepsize for central difference is O(epsilon^{1/3}).
71 epsilon = np.finfo(dtype_).eps
72 delta = epsilon**(1.0 / 3.0)
117 # Optimal stepsize for central difference is O(epsilon^{1/3}).
118 epsilon = np.finfo(dtype_).eps
119 delta = epsilon**(1.0 / 3.0)
|
| /external/tensorflow/tensorflow/python/ops/ |
| nn_grad.py | 740 dx: Backprop for input, which is (grad * (g * rsqrt(v + epsilon)))
742 sum_over_rest(grad * g) * (-1 / rsqrt(v + epsilon))
744 sum_over_rest(grad * g * (x - m)) * (-1/2) * (v + epsilon) ^ (-3/2)
747 dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon)))
766 grad_x: gradient for x, which is scale * rsqrt(variance + epsilon) *
768 mean(grad_y * (x - mean(x))) / (variance + epsilon)]
769 in training mode; grad_y * scale * rsqrt(pop_variance + epsilon)
773 rsqrt(variance + epsilon)) in training mode;
774 sum(grad_y * (x - pop_mean) * rsqrt(pop_variance + epsilon))
783 epsilon = op.get_attr("epsilon"
[all...] |
| /external/antlr/antlr-3.4/tool/src/main/java/org/antlr/analysis/ |
| Label.java | 36 * tokens. It can be an epsilon transition. It can be a semantic predicate
37 * (which assumes an epsilon transition) or a tree of predicates (in a DFA).
45 public static final int EPSILON = -5;
47 public static final String EPSILON_STR = "<EPSILON>";
49 /** label is a semantic predicate; implies label is epsilon also */
75 /** We have labels like EPSILON that are below 0; it's hard to
194 return label==EPSILON;
283 // labels must be the same even if epsilon or set or sempred etc...
|
| /external/eigen/unsupported/test/ |
| mpreal_support.cpp | 17 std::cerr << "epsilon = " << NumTraits<mpreal>::epsilon() << "\n";
|
