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Results 1 - 10 of 203 for activations (0.18 sec)
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maven-core/src/main/java/org/apache/maven/execution/ProjectActivation.java
final ActivationSettings settings = ActivationSettings.of(active, optional); this.activations.add(new ProjectActivationSettings(selector, settings)); } private Stream<ProjectActivationSettings> getProjects(final Predicate<ActivationSettings> predicate) { return this.activations.stream().filter(activation -> predicate.test(activation.activationSettings)); }
Registered: Wed Jun 12 09:55:16 UTC 2024 - Last Modified: Mon Dec 26 15:12:32 UTC 2022 - 7K bytes - Viewed (0) -
maven-core/src/main/java/org/apache/maven/execution/ProfileActivation.java
getInactiveProfiles().forEach(this.activations::remove); inactiveProfileIds.forEach(this::deactivateOptionalProfile); } /** * Mark a profile as required and activated. * @param id The identifier of the profile. */ public void activateRequiredProfile(String id) { this.activations.put(id, ActivationSettings.ACTIVATION_REQUIRED); } /**
Registered: Wed Jun 12 09:55:16 UTC 2024 - Last Modified: Mon Dec 26 15:12:32 UTC 2022 - 5.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/resources/decomposition_lib.mlir
// // REGISTER_OP("Relu") // .Input("features: T") // .Output("activations: T") // .Attr("T: {realnumbertype, qint8}") // T is a derived attribute. tfr.func @tf__relu_(!tfr.tensor<T>) -> !tfr.tensor<T> attributes{T} // Translated from: // // REGISTER_OP("Relu6") // .Input("features: T") // .Output("activations: T") // .Attr("T: {realnumbertype}") // T is a derived attribute.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 13 16:33:28 UTC 2021 - 4.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/rewrite_tpu_embedding_ops.mlir
} // CHECK-LABEL: func @recv_send_ops func.func @recv_send_ops() -> () { // CHECK: %[[DATA:.*]] = "tf.XlaRecvTPUEmbeddingDeduplicationData"() // CHECK: %[[ACTIVATIONS:.*]] = "tf.XlaRecvTPUEmbeddingActivations"(%[[DATA]]) // CHECK: "tf.XlaSendTPUEmbeddingGradients"(%[[ACTIVATIONS]], %[[DATA]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 4.2K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/nn_grad.cc
AbstractTensorHandle* upstream_grad = grad_outputs[0]; AbstractTensorHandle* activations = forward_outputs_[0]; // Calculate Grad std::string name = "relu_grad"; TF_RETURN_IF_ERROR(ReluGrad(ctx, upstream_grad, activations, &grad_inputs[0], name.c_str())); return absl::OkStatus(); } ~ReluGradientFunction() override {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 09 06:38:45 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/c/experimental/ops/nn_ops.cc
// Example usage: // >>> tf.nn.relu([-2., 0., 3.]).numpy() // array([0., 0., 3.], dtype=float32) Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features, AbstractTensorHandle** activations, const char* name, const char* raw_device_name) { AbstractOperationPtr op_ptr(ctx->CreateOperation()); TF_RETURN_IF_ERROR(op_ptr->Reset("Relu", raw_device_name));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 5.9K bytes - Viewed (0) -
maven-model-builder/src/main/java/org/apache/maven/model/building/DefaultModelBuilder.java
activation = activation.clone(); } activations.put(profile.getId(), activation); } return activations; } private void injectProfileActivations(Model model, Map<String, Activation> activations) { for (Profile profile : model.getProfiles()) { Activation activation = profile.getActivation(); if (activation == null) {
Registered: Wed Jun 12 09:55:16 UTC 2024 - Last Modified: Tue May 21 09:54:32 UTC 2024 - 82.9K bytes - Viewed (0) -
tensorflow/c/experimental/ops/nn_ops.h
const char* raw_device_name = nullptr); // Computes rectified linear: `max(features, 0)`. Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features, AbstractTensorHandle** activations, const char* name = nullptr, const char* raw_device_name = nullptr); // Adds `bias` to `value`. Status BiasAdd(AbstractContext* ctx, AbstractTensorHandle* const value,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc
namespace mlir { namespace TFL { namespace { #define GEN_PASS_DEF_PREPAREDYNAMICRANGEQUANTIZEPASS #include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc" // A boolean attribute used to describe whether input activations need to be // asymmetrically quantized. constexpr char kAsymmetricQuantizeInputsAttr[] = "asymmetric_quantize_inputs"; using QuantizationUnits = llvm::SetVector<std::pair<Operation*, int>>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/default_quant_params.cc
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h" //===----------------------------------------------------------------------===// // The Pass to add default quantization parameters for the activations which // don't have quantization information. These default parameters are usually // not from real measurement, so this pass is only for test purpose. namespace mlir { namespace TFL {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.4K bytes - Viewed (0)