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Results 41 - 50 of 88 for input_shapes_ (0.14 sec)
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tensorflow/compiler/mlir/tensorflow/tests/strip_tf_attributes.mlir
%arg4: tensor<10xf32> {tf._user_specified_name = "b2"}) -> tensor<10xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<32x28x28x1>, #tf_type.shape<3x3x1x5>, #tf_type.shape<5>, #tf_type.shape<3920x10>, #tf_type.shape<10>]} { return %arg4 : tensor<10xf32> } // ----- // CHECK-LABEL: strips_attributes_with_tf_values
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 25 20:04:10 UTC 2022 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-composite-functions-tf.mlir
tensor<?x8x8xf32>, %arg1: tensor<8x10xf32>, %arg2: tensor<8x10xf32>, %arg3: tensor<8x40xf32>, %arg4: tensor<10x40xf32>, %arg5: tensor<40xf32>) -> (tensor<8x10xf32>, tensor<?x8x10xf32>, tensor<8x10xf32>, tensor<8x10xf32>, tensor<f32>) attributes {tf._input_shapes = ["tfshape$dim { size: -1 } dim { size: 8 } dim { size: 8 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$unknown_rank: true", "tfshape$unknown_rank: false", "tfshape$unknown_rank: false"],...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/c/c_api_experimental.h
// if you want shape inference to consider the input tensors of the // op for shape inference. // - The types need not be set in `input_shapes` as it is not used. // - The number of `input_tensors` should be the same as the number of items // in `input_shapes`. // // The results are returned in `output_shapes` and // `output_resource_shapes_and_types`. The caller is responsible for freeing the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 27 21:07:00 UTC 2023 - 15.1K bytes - Viewed (0) -
tensorflow/cc/gradients/math_grad.cc
// [[g1, g1, g1], // [g2, g2, g2]] // input_shape = [2, 3] auto input_shape = Shape(scope, op.input(0)); // output_shape_kept_dims = [2, 1] auto output_shape_kept_dims = ReducedShapeHelper(scope, input_shape, op.input(1)); // This step "flips" any 1s with values from the input_shape, and // replaces remaining entries with 1. This creates a shape that
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Aug 25 18:20:20 UTC 2023 - 50.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
// through entry by entry. ArrayRef<int64_t> input_shape = input_type.getShape(); int input_shape_size = input_shape.size(); Shape slice_sizes(input_shape.begin(), input_shape.end()); int slice_dimensions = slice_sizes.size(); slice_sizes[slice_dimensions - 2] = std::min((int64_t)1, input_shape[input_shape_size - 2]); slice_sizes[slice_dimensions - 1] =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/fetch_feed_names.mlir
} func.return %0 : tensor<*xf32> } func.func private @__inference_call_440(%arg0: tensor<?x28x28x3xf32> {tf._user_specified_name = "inputs"}) -> tensor<*xf32> attributes {tf._input_shapes = [#tf_type.shape<?x28x28x3>], tf.signature.is_stateful} { %0 = tf_executor.graph { %outputs, %control = tf_executor.island wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<32xf32>} : () -> tensor<32xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.cc
file->getBuffer(), input_arrays, input_dtypes, input_shapes, output_arrays, control_output_arrays, graphdef_conversion_options, context); } return GraphdefToMlirTranslateFunction(file->getBuffer(), input_arrays, input_dtypes, input_shapes, output_arrays, control_output_arrays,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 23.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
// on `xla_call_module_context_` for details. std::vector<xla::Shape> input_shapes; input_shapes.reserve(op.getArgs().size()); for (mlir::Type type : op.getArgs().getTypes()) { input_shapes.push_back(xla::TypeToShape(type)); } absl::Status status = loader->RefineDynamicShapes(input_shapes); if (!status.ok()) { // Do not return false here. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/utils/tf_to_uniform_attribute_utils.cc
int feature_group_cnt = 1; ShapedType input_shape = mlir::dyn_cast<ShapedType>(op->getOperand(0).getType()); if (!input_shape) { return op->emitError( "Only input with known shape is supported for Uniform Quantized " "opset."); } if (op->getParentOfType<func::FuncOp>().getName().contains("depthwise_")) { feature_group_cnt = input_shape.getDimSize(3); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 18.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_saved_model_freeze_variables.cc
if (input_shapes_attr.size() != func_op.getNumArguments()) { return func_op->emitError( "Number of arguments and 'tf._input_shapes' " "attribute size do not match. ") << "Num args: " << func_op.getNumArguments() << ", tf._input_shapes size: " << input_shapes_attr.size(); } return success(); } // Validates ModuleOp. Returns `failure` if the module op is invalid.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 09:56:53 UTC 2024 - 19.4K bytes - Viewed (0)