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Results 1 - 10 of 58 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/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/tfr/examples/mnist/mnist_ops_test.py
} self._assertOpAndComposite([input_, filter_, bias], tf.function(gen_mnist_ops.new_conv2d), ops_defs._composite_conv_add_relu, kwargs) def test_new_conv2d_relu6(self): input_ = tf.random.uniform([1, 4, 4, 1]) filter_ = tf.random.uniform([2, 2, 1, 8]) bias = tf.zeros([8]) kwargs = { 'input_': input_, 'filter_': filter_,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Sep 28 21:37:05 UTC 2021 - 4K 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)