| /external/tensorflow/tensorflow/core/grappler/optimizers/ |
| debug_stripper_test.cc | 126 Tensor x_t(DT_FLOAT, TensorShape({}));
128 x_t.flat<float>()(0) = 1.0f;
131 EvaluateNodes(item.graph, {"z"}, {{"x", x_t}, {"y", y_t}});
133 EvaluateNodes(output, {"z"}, {{"x", x_t}, {"y", y_t}});
185 Tensor x_t(DT_FLOAT, TensorShape({}));
187 x_t.flat<float>()(0) = 1.0f;
190 EvaluateNodes(item.graph, {"z"}, {{"x", x_t}, {"y", y_t}});
192 EvaluateNodes(output, {"z"}, {{"x", x_t}, {"y", y_t}});
223 Tensor x_t(DT_FLOAT, TensorShape({}));
224 x_t.flat<float>()(0) = 1.0f
[all...] |
| arithmetic_optimizer_test.cc | 911 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({3, 3, 28, 28})); local
948 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({3, 3, 28, 28})); local
977 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({4, 3, 28, 28})); local
1009 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({4, 3, 28, 28})); local
1041 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({8, 3, 28, 28})); local
1098 auto x_t = GenerateRandomTensor<DT_INT8>(TensorShape({8, 3, 28, 28, 4})); local
1409 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({8, 12, 28, 28})); local
1446 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 3})); local
1766 auto x_t = GenerateRandomTensor<DT_UINT8>(TensorShape({2, 3})); local
1800 auto x_t = GenerateRandomTensor<DT_INT8>(TensorShape({2, 3})); local
1833 auto x_t = GenerateRandomTensor<DT_INT8>(TensorShape({2, 3})); local
1944 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); local
2085 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); local
2152 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({32})); local
2342 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); local
[all...] |
| constant_folding_test.cc | 47 Tensor x_t(DTYPE, TensorShape({2, 2}));
51 x_t.flat<T>()(i) = T(i + 1);
114 EvaluateNodes(item.graph, item.fetch, {{"x", x_t}});
115 auto tensors = EvaluateNodes(output, item.fetch, {{"x", x_t}});
653 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); local
659 {{"x", x_t}, {"y", y_t}, {"a", a_t}, {"b", b_t}, {"bias", bias_t}});
663 {{"x", x_t}, {"y", y_t}, {"a", a_t}, {"b", b_t}, {"bias", bias_t}});
3300 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({})); local
3651 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({1, 2, 3, 4})); local
[all...] |
| /external/skia/src/gpu/gradients/ |
| GrTwoPointConicalGradientLayout.fp | 39 // calculations of t and x_t below overflow and produce an incorrect interpolant (which then
69 float x_t = -1;
71 x_t = dot(p, p) / p.x;
73 x_t = length(p) - p.x * invR1;
81 // is really critical, maybe we should just compute the area where temp and x_t are
85 x_t = -sqrt(temp) - p.x * invR1;
87 x_t = sqrt(temp) - p.x * invR1;
92 // The final calculation of t from x_t has lots of static optimizations but only do them
93 // when x_t is positive (which can be assumed true if isWellBehaved is true)
97 if (x_t <= 0.0)
[all...] |
| /external/skqp/src/gpu/gradients/ |
| GrTwoPointConicalGradientLayout.fp | 39 // calculations of t and x_t below overflow and produce an incorrect interpolant (which then
69 float x_t = -1;
71 x_t = dot(p, p) / p.x;
73 x_t = length(p) - p.x * invR1;
81 // is really critical, maybe we should just compute the area where temp and x_t are
85 x_t = -sqrt(temp) - p.x * invR1;
87 x_t = sqrt(temp) - p.x * invR1;
92 // The final calculation of t from x_t has lots of static optimizations but only do them
93 // when x_t is positive (which can be assumed true if isWellBehaved is true)
97 if (x_t <= 0.0)
[all...] |
| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| adadelta.py | 49 $$\Delta x_t = -RMS[\Delta x]_{t-1} * g_t / RMS[g]_t$$
50 $$E[\Delta x^2]_t := \rho * E[\Delta x^2]_{t-1} + (1 - \rho) * \Delta x_t^2$$
51 $$x_t := x_{t-1} + \Delta x_{t}
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| sparse_tensor_dense_matmul_op_test.py | 352 x_t = constant_op.constant(x)
355 x_t, y_t, adjoint_a, adjoint_b)
358 x_t = constant_op.constant(x)
361 x_t, y_t, adjoint_a, adjoint_b)
|
| functional_ops_test.py | 393 x_t = array_ops.transpose(x)
397 result_t = functional_ops.scan(lambda a, x: a + x, x_t, infer_shape=False)
400 result_t_grad = gradients_impl.gradients(result_t, [x_t])[0]
[all...] |
| /external/tensorflow/tensorflow/core/framework/ |
| tensor_util_test.cc | 529 Tensor x_t; local
530 EXPECT_TRUE(x_t.FromProto(x));
533 test::ExpectTensorEqual<T>(x_t, y_t);
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| student_t.py | 282 x_t = self.df / (y**2. + self.df)
283 neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
|
| /external/tensorflow/tensorflow/python/ops/ |
| rnn.py | 62 x_t = array_ops.transpose(
65 x_t.set_shape(
69 return x_t
[all...] |
