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Results 31 - 40 of 260 for weights (0.24 sec)
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pkg/scheduler/framework/plugins/interpodaffinity/scoring.go
} func (m scoreMap) processTerm(term *framework.AffinityTerm, weight int32, pod *v1.Pod, nsLabels labels.Set, node *v1.Node, multiplier int32) { if term.Matches(pod, nsLabels) { if tpValue, tpValueExist := node.Labels[term.TopologyKey]; tpValueExist { if m[term.TopologyKey] == nil { m[term.TopologyKey] = make(map[string]int64) } m[term.TopologyKey][tpValue] += int64(weight * multiplier) } } }
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Fri Dec 15 03:30:06 UTC 2023 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.h
inline constexpr std::array<int64_t, 4> kNchwToNhwcPermutation = {0, 2, 3, 1}; // Permutation from the OIHW (== (output features, input features, height, // width)) tensor format to HWIO. This is commonly used to transpose convolution // weights represented as OIHW format to HWIO, which is more desirable for // certain downstream optimization passes (e.g. XLA).
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
SPARSE = 1, DENSE = 2, } table LSHProjectionOptions { type: LSHProjectionType; } table SVDFOptions { rank:int; fused_activation_function:ActivationFunctionType; // For weights-only quantization, use asymmetric quantization for non // constant inputs at evaluation time. asymmetric_quantize_inputs:bool; } // An implementation of TensorFlow RNNCell. table RNNOptions {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
pilot/pkg/xds/endpoints/endpoint_builder.go
} func (e *LocalityEndpoints) refreshWeight() { var weight *wrapperspb.UInt32Value if len(e.llbEndpoints.LbEndpoints) == 0 { weight = nil } else { weight = &wrapperspb.UInt32Value{} for _, lbEp := range e.llbEndpoints.LbEndpoints { weight.Value += lbEp.GetLoadBalancingWeight().Value } } e.llbEndpoints.LoadBalancingWeight = weight } func (e *LocalityEndpoints) AssertInvarianceInTest() {
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Sun Apr 28 02:18:19 UTC 2024 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
"Non-constant weights are not supported at the moment," " except matmul and einsum."); } else if (!quant_options_.enable_two_input_tensors() && !is_unitwise_quantization_enabled) { return absl::InternalError( "Quantization is disabled for this op due to the non-constant " "weight. You can enable it by setting `enable_two_input_tensors` "
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=4000 enable-custom-op-quantization=CustomTestOp=1-3,CustomTestOp3=3" | FileCheck --check-prefix=MinElement %s // RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=19" | FileCheck --check-prefix=LSTMOpQuantized %s // RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=21" | FileCheck --check-prefix=LSTMOpNotQuantized %s
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
docs/ru/docs/tutorial/body-nested-models.md
А `dict`, с именем `weights`, который вы получите в качестве ответа Pydantic, действительно будет иметь ключи типа `int` и значения типа `float`. ## Резюме
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Fri Mar 22 01:42:11 UTC 2024 - 14.9K bytes - Viewed (0) -
docs/de/docs/tutorial/body-nested-models.md
Das bedeutet, dass Ihre API-Clients nur Strings senden können, aber solange diese Strings nur Zahlen enthalten, wird Pydantic sie konvertieren und validieren. Und das `dict` welches Sie als `weights` erhalten, wird `int`-Schlüssel und `float`-Werte haben. ## Zusammenfassung Mit **FastAPI** haben Sie die maximale Flexibilität von Pydantic-Modellen, während Ihr Code einfach, kurz und elegant bleibt.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Fri Mar 22 01:42:11 UTC 2024 - 10.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/dense_to_sparse.cc
type.getNumElements()); } return sparsity; } typedef struct InspectResult { // Whether the weight tensor is sparse enough to be compressed. bool can_compress; // If the weight tensor cannot be encoded in a block configuration that the op // supports, a Densify() op will be inserted afterwards to fall back to dense // execution. bool needs_densify;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 16.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/test_schema.fbs
SPARSE = 1, DENSE = 2, } table LSHProjectionOptions { type: LSHProjectionType; } table SVDFOptions { rank:int; fused_activation_function:ActivationFunctionType; // For weights-only quantization, use asymmetric quantization for non // constant inputs at evaluation time. asymmetric_quantize_inputs:bool; } // An implementation of TensorFlow RNNCell. table RNNOptions {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 19 19:46:06 UTC 2021 - 26.1K bytes - Viewed (0)