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Results 1 - 10 of 38 for _input_ (0.28 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.mlir
// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]], // CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 52.6K 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/compiler/jit/tests/device_compiler_test_helper.cc
RunOptions run_options; Tensor input_a = CreateInputTensor(shape, 0); Tensor input_b = CreateInputTensor(shape, shape.num_elements()); Tensor input_c = CreateInputTensor(shape, 2 * shape.num_elements()); TF_RETURN_IF_ERROR(session->Run( run_options, {std::make_pair("a", input_a), std::make_pair("b", input_b), std::make_pair("c", input_c)}, {"m"}, {}, &golden_output_tensors, nullptr));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Feb 09 08:24:16 UTC 2024 - 6.2K 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/c/c_api.cc
void TF_AddInput(TF_OperationDescription* desc, TF_Output input) { desc->node_builder.Input(&input.oper->node, input.index); } void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs, int num_inputs) { std::vector<NodeBuilder::NodeOut> input_list; input_list.reserve(num_inputs); for (int i = 0; i < num_inputs; ++i) { input_list.emplace_back(&inputs[i].oper->node, inputs[i].index); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 102.3K bytes - Viewed (0) -
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/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)