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Results 1 - 10 of 20 for dnumerical (0.35 sec)
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tensorflow/c/experimental/gradients/grad_test_helper.cc
memcpy(&dnumerical[0], TF_TensorData(numerical_tensor), TF_TensorByteSize(numerical_tensor)); float* danalytical = new float[num_elem_analytical]{0}; memcpy(&danalytical[0], TF_TensorData(analytical_tensor), TF_TensorByteSize(analytical_tensor)); for (int j = 0; j < num_elem_numerical; j++) { ASSERT_NEAR(dnumerical[j], danalytical[j], abs_error); }
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 5K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker_test.cc
auto num_elem_numerical = TF_TensorElementCount(numerical_tensor); ASSERT_EQ(num_elem_numerical, num_grad); float* dnumerical = new float[num_elem_numerical]{0}; memcpy(&dnumerical[0], TF_TensorData(numerical_tensor), TF_TensorByteSize(numerical_tensor)); for (int j = 0; j < num_grad; j++) { ASSERT_NEAR(dnumerical[j], expected_grad[j], abs_error); } delete[] dnumerical;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Apr 14 10:03:59 GMT 2023 - 6.5K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker.cc
TF_DeleteTensor(grad_tensor); dtheta_approx[i] = grad_data[0]; } // Populate *numerical_grad with the data from dtheta_approx. TF_RETURN_IF_ERROR(TestTensorHandleWithDims<float, TF_FLOAT>( ctx, dtheta_approx.data(), theta_dims.data(), num_dims, numerical_grad)); TF_DeleteTensor(theta_tensor); return absl::OkStatus(); } } // namespace gradients
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 7.3K bytes - Viewed (0) -
tensorflow/c/eager/gradient_checker.h
namespace tensorflow { namespace gradients { /* Returns numerical grad inside `dtheta_approx` given `forward` model and * parameter specified by `input_index`. * * I.e. if y = <output of the forward model> and w = inputs[input_index], * this will calculate dy/dw numerically. * * `use_function` indicates whether to use graph mode(true) or eager(false). * * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Dec 11 02:34:32 GMT 2020 - 1.8K bytes - Viewed (0) -
android/guava/src/com/google/common/math/PairedStatsAccumulator.java
* * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It * is not guaranteed to return zero when the dataset consists of the same pair of values multiple * times, due to numerical errors. * * <h3>Non-finite values</h3> * * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
Java - Registered: Fri Apr 26 12:43:10 GMT 2024 - Last Modified: Fri May 12 17:02:53 GMT 2023 - 10.3K bytes - Viewed (0) -
android/guava/src/com/google/common/math/Stats.java
* * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It * is not guaranteed to return zero when the dataset consists of the same value multiple times, * due to numerical errors. However, it is guaranteed never to return a negative result. * * <h3>Non-finite values</h3> * * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
Java - Registered: Fri Apr 26 12:43:10 GMT 2024 - Last Modified: Thu Feb 15 16:12:13 GMT 2024 - 22K bytes - Viewed (0) -
android/guava/src/com/google/common/math/PairedStats.java
* * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It * is not guaranteed to return zero when the dataset consists of the same pair of values multiple * times, due to numerical errors. * * <h3>Non-finite values</h3> * * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
Java - Registered: Fri Apr 26 12:43:10 GMT 2024 - Last Modified: Fri May 12 17:02:53 GMT 2023 - 12.6K bytes - Viewed (0) -
android/guava/src/com/google/common/math/StatsAccumulator.java
* * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It * is not guaranteed to return zero when the dataset consists of the same value multiple times, * due to numerical errors. However, it is guaranteed never to return a negative result. * * <h3>Non-finite values</h3> * * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
Java - Registered: Fri Apr 26 12:43:10 GMT 2024 - Last Modified: Fri May 12 17:02:53 GMT 2023 - 14.2K bytes - Viewed (0) -
android/guava/src/com/google/common/math/LinearTransformation.java
* itself. In all other cases, the inverse is a transformation such that applying both the * original transformation and its inverse to a value gives you the original value give-or-take * numerical errors. Calling this method multiple times on the same instance will always return * the same instance. Calling this method on the result of calling this method on an instance will * always return that original instance.
Java - Registered: Fri Apr 26 12:43:10 GMT 2024 - Last Modified: Fri May 12 17:02:53 GMT 2023 - 9.6K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/nn_grad_test.cc
BuildImmediateExecutionContext(std::get<1>(GetParam()), &ctx_raw); ASSERT_EQ(errors::OK, status_.code()) << status_.message(); immediate_execution_ctx_.reset(ctx_raw); } // Computing numerical gradients with TensorFloat-32 is numerically // unstable. Some forward pass tests also fail with TensorFloat-32 due to // low tolerances enable_tensor_float_32_execution(false); }
C++ - Registered: Tue Mar 26 12:39:09 GMT 2024 - Last Modified: Wed Feb 28 13:53:47 GMT 2024 - 8.3K bytes - Viewed (0)