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tensorflow/compiler/mlir/tfr/examples/mnist/ops_defs.py
] @Composite( 'NewFullyConnected', inputs=['input_: T', 'filter_: T', 'bias: T'], attrs=['act: {"", "RELU", "RELU6", "TANH"} = ""'], derived_attrs=['T: {float, int8}'], outputs=['o: T']) def _composite_fully_connected(input_, filter_, bias, act): res = tf.raw_ops.MatMul( a=input_, b=filter_, transpose_a=False, transpose_b=True) res = tf.raw_ops.Add(x=res, y=bias)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 31 20:23:51 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.h
func::FuncOp fused_func_op_; Value input_; Value weight_; Value bias_; Value projection_; bool couple_input_forget_gates_; // internal state Value weight_transposed_; Value projection_transposed_; RankedTensorType weight_type_; RankedTensorType projection_type_; int num_gates_; int n_cell_; int n_output_; int n_input_; int num_cols_weight_transposed_;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 03 00:14:05 UTC 2023 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf_device_ops.mlir
%10 = "tf.opK"() : () -> tensor<*xi16> %11 = "tf.opL"() : () -> tensor<*xi64> tf_device.replicate([%0, %1, %2] as %input0: tensor<*xi1>, %9 as %input1: tensor<*xi8>, %10 as %input2: tensor<*xi16>, [%3, %4, %5] as %input3: tensor<*xi32>, [%6, %7, %8] as %input4: tensor<*xf32>, %11 as %input5: tensor<*xi64>) {n = 3 : i32} { tf_device.return } func.return // CHECK: %[[OP_A:[a-z0-9]*]] = "tf.opA"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 23:53:20 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s module { func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} { %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
platforms/software/dependency-management/src/test/groovy/org/gradle/internal/rules/RuleSourceBackedRuleActionTest.groovy
void theRule(List subject, String input1, Integer input2, Set input3) { subject.add(input1) subject.add(input2) subject.addAll(input3) } } static class ArrayListRuleSource { @Mutate void theRule(ArrayList subject, String input1, Integer input2, Set input3) { subject.add(input1) subject.add(input2) subject.addAll(input3)
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Oct 10 21:10:11 UTC 2023 - 6.4K bytes - Viewed (0) -
subprojects/core/src/test/groovy/org/gradle/api/internal/file/CalculatedTaskInputFileCollectionTest.groovy
0 * calculated._ } def "notifies each of the inputs of task start and complete"() { def input1 = Mock(LifecycleAwareValue) def input2 = "other" def input3 = Mock(LifecycleAwareValue) def fileCollection = new CalculatedTaskInputFileCollection(taskDependencyFactory, ":task", Stub(MinimalFileSet), input1, input2, input3) when: fileCollection.prepareValue()
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Fri Oct 28 15:32:09 UTC 2022 - 3.8K bytes - Viewed (0) -
platforms/software/signing/src/test/groovy/org/gradle/plugins/signing/SignOperationSpec.groovy
input2 = getResourceFile("2.txt") output2 = signing.signatureType.fileFor(input2) input2Artifact = new DefaultPublishArtifact(input2.name, "Text File", "txt", null, null, input2) [output1, output2].each { output -> assert !output.exists() || output.delete() } assert input1.text && input2.text // don't care what it is, just need some }
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Wed Oct 11 12:16:09 UTC 2023 - 2.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/signature_def_with_multiple_entry_points.mlir
func.func @add(%arg0: tensor<?xf32> {tf_saved_model.index_path = ["input1"]}, %arg1: tensor<?xf32> {tf_saved_model.index_path = ["input2"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["result"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "input1:0,input2:0", outputs = "result:0"}, tf_saved_model.exported_names = ["add"]} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/quant_stats.pbtxt
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \ # RUN: -tf-input-shapes=4:4 \ # RUN: -tf-input-data-types=DT_FLOAT,DT_FLOAT \ # RUN: -tf-output-arrays=Add \ # RUN: -tf-inference-type=DT_QUINT8 \ # RUN: -tf-input-min-values='-2,-3' \ # RUN: -tf-input-max-values='2,3' \ # RUN: --quant-stats=%s.stats \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/add.pbtxt
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=Add %s -o - | flatbuffer_to_string - | FileCheck %s # Add two tensor<4xi32> inputs and return the result node { name: "Add" op: "Add" input: "input0" input: "input1" attr { key: "T" value { type: DT_INT32 } } } node { name: "input0" op: "Placeholder" attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 2.4K bytes - Viewed (0)