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Results 1 - 10 of 11 for MULTINOMIAL (0.15 sec)
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tensorflow/compiler/mlir/tfrt/tests/optimize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jul 01 23:50:06 UTC 2023 - 2.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-with-tf2xla-hlo-importer.mlir
func.return %1 : tensor<1000xi32> } // CHECK-LABEL: multinomial func.func @multinomial(%arg0: tensor<2x4xf32>, %seed: tensor<i32>, %seed2: tensor<i32>) -> tensor<2x10xi32> { // CHECK-NOT: tf.Multinomial %samples = "tf.Const"() { value = dense<10> : tensor<i32> } : () -> tensor<i32> %1 = "tf.Multinomial"(%arg0, %samples) {seed = 0, seed2 = 0}: (tensor<2x4xf32>, tensor<i32>) -> tensor<2x10xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 38.6K bytes - Viewed (1) -
tensorflow/compiler/jit/compilability_check_util.h
return op_name == "RandomUniform" || op_name == "RandomShuffle" || op_name == "RandomUniformInt" || op_name == "RandomStandardNormal" || op_name == "TruncatedNormal" || op_name == "Multinomial"; } bool OpProducesOrConsumesVariant(const Node& node) const { auto is_variant = [](DataType dtype) { return dtype == DT_VARIANT; }; return absl::c_any_of(node.input_types(), is_variant) ||
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
} func.func @multinomial_i64(%arg0: tensor<2xf32>, %arg1: tensor<1xi32>) -> tensor<10xi64> { %0 = "tf.Multinomial"(%arg0, %arg1) {seed = 0 : i64, seed2 = 0: i64} : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi64> func.return %0 : tensor<10xi64> // CHECK-LABEL:multinomial_i64 // CHECK: "tfl.multinomial"(%arg0, %arg1) <{seed = 0 : i64, seed2 = 0 : i64}> : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi64> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
CONV_3D_TRANSPOSE = 141, VAR_HANDLE = 142, READ_VARIABLE = 143, ASSIGN_VARIABLE = 144, BROADCAST_ARGS = 145, RANDOM_STANDARD_NORMAL = 146, BUCKETIZE = 147, RANDOM_UNIFORM = 148, MULTINOMIAL = 149, GELU = 150, DYNAMIC_UPDATE_SLICE = 151, RELU_0_TO_1 = 152, UNSORTED_SEGMENT_PROD = 153, UNSORTED_SEGMENT_MAX = 154, UNSORTED_SEGMENT_SUM = 155, ATAN2 = 156,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/ops.mlir
} // ----- // CHECK-LABEL: testMultinomial func.func @testMultinomial(%arg0: tensor<2xf32>, %arg1: tensor<1xi32>) -> tensor<10xi64> { // CHECK: "tfl.multinomial"(%arg0, %arg1) %0 = "tfl.multinomial"(%arg0, %arg1) {seed = 0 : i64, seed2 = 0: i64} : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi64> func.return %0 : tensor<10xi64> } // ----- // CHECK-LABEL: testMultinomialInt32
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
"MaxPool3D", "MaxPool3DGrad", "MaxPool3DGradGrad", "MaxPoolGrad", "MaxPoolGradGrad", "MaxPoolGradGradV2", "MaxPoolGradV2", "MaxPoolV2", "Multinomial", "NextAfter", "NonMaxSuppressionV3", "NonMaxSuppressionV4", "ParallelDynamicStitch", "ParameterizedTruncatedNormal", "PartitionedCall", "Polygamma",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
); let results = (outs TFL_TensorOf<[F32]>:$out); let hasOptions = 1; let customOption = "RandomOptions"; } def TFL_MultinomialOp : TFL_Op<"multinomial", []> { let summary = "Draws samples from a categorical distribution."; let description = [{ The generated values will have a categorical distribution based on the `logits`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
); let results = (outs Res<TF_StrTensor, [{A string representing the resource.}]>:$string_handle ); } def TF_MultinomialOp : TF_Op<"Multinomial", [TF_CannotDuplicate]> { let summary = "Draws samples from a multinomial distribution."; let arguments = (ins Arg<TF_IntOrFpTensor, [{2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
RELEASE.md
`TransformedDistribution`. * Fix a bug in `import_meta_graph`'s handling of partitioned variables when * Ensure `tf.distributions.Multinomial` doesn't underflow in `log_prob`. Before this change, all partitions of an integer variable were initialized with the shape of the unpartitioned variable; after this
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)