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Results 1 - 10 of 25 for _input_ (0.11 sec)
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
} Status AddInput(AbstractTensorHandle* input) override { GraphTensor* t = dyn_cast<GraphTensor>(input); if (!t) { return tensorflow::errors::InvalidArgument( "Unable to cast input to GraphTensor"); } TF_AddInput(op_.get(), t->output_); return absl::OkStatus(); } Status AddInputList(absl::Span<AbstractTensorHandle* const> inputs) override {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 20:00:09 UTC 2024 - 15.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-post-training-16bits.mlir
// CHECK-SAME: %[[input_0]], // CHECK-SAME: %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], // CHECK-SAME: %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]], // CHECK-SAME: %[[input_9]], %[[input_9]], %[[input_9]], // CHECK-SAME: %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], // CHECK-SAME: %[[input_9]], %[[input_9]], // CHECK-SAME: %[[input_14]], %[[input_15]],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_device_ops.td
is used instead. Operands are replicated inputs and packed inputs. replicated_inputs: each group of `n` inputs corresponds to an input for a single individual replica and is mapped to a single region argument. Inside one group the operands are matching in order the `devices` attribute. Each replicated input must have compatible shapes and types. packed_inputs: each input corresponds to an input broadcasted across all
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 23:53:20 UTC 2024 - 14.8K bytes - Viewed (0) -
tensorflow/c/experimental/saved_model/internal/saved_model_api_test.cc
std::vector<TFE_TensorHandle*> compute_fn_inputs; TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f); TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f); compute_fn_inputs.push_back(input_a); compute_fn_inputs.push_back(input_b); TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 23 08:08:45 UTC 2024 - 21.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
// TFL lstm only supports time-majored inputs, so if it's not time-majored, // we will transpose the inputs and outputs. auto time_major_attr = func_op->getAttrOfType<BoolAttr>("tf.time_major"); if (time_major_attr == nullptr) return failure(); bool time_majored = time_major_attr.getValue(); auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType()); if (!input_type) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 36.2K bytes - Viewed (0) -
tensorflow/compiler/jit/partially_decluster_pass_test.cc
Node* input = ops::SourceOp("FakeNullary", builder.opts().WithName("Input")); Node* clustered_producer = ops::BinaryOp("FakeBinary", input, input, builder.opts().WithName("ClusteredProducer")); // The first input is hostmem and the second input is devicemem. Node* consumer_in_different_cluster = ops::BinaryOp("FakeBinary", input, clustered_producer,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jun 10 12:32:39 UTC 2022 - 23K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/tf_to_corert/control_flow.mlir
} // CHECK-LABEL: func @tensor_array_while_test // CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) { %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 00:40:32 UTC 2024 - 17.5K bytes - Viewed (0) -
tensorflow/cc/framework/ops.h
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/c/experimental/gradients/math_grad.cc
class MatMulGradientFunction : public GradientFunction { public: explicit MatMulGradientFunction(vector<AbstractTensorHandle*> f_inputs, AttrBuilder f_attrs) : forward_inputs_(f_inputs), forward_attrs_(f_attrs) { for (auto input : forward_inputs_) { if (input) { input->Ref(); } } } Status Compute(AbstractContext* ctx,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 28 13:53:47 UTC 2024 - 15.2K bytes - Viewed (0) -
tensorflow/cc/framework/gradients.cc
return errors::InvalidArgument( "Must specify a gradient input for each output."); } std::vector<bool> reachable_nodes = GetReachableNodes(); for (const Output& input : inputs_) { if (!reachable_nodes[input.node()->id()]) { return errors::InvalidArgument( "Cannot compute the partial derivative for node '", input.node()->name(), "' as it's unreachable from the output node(s).");
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 05:57:22 UTC 2024 - 22K bytes - Viewed (0)