| /external/tensorflow/tensorflow/core/kernels/ |
| inplace_ops.cc | 36 Tensor* output) {
38 auto Toutput = output->flat_outer_dims<T>();
47 Tensor* output) {
48 CHECK_EQ(value.dtype(), output->dtype());
52 return DoParallelConcatUpdate<CPUDevice, type>(d, value, loc, output);
65 Tensor* output) {
66 CHECK_EQ(value.dtype(), output->dtype());
70 return DoParallelConcatUpdate<SyclDevice, type>(d, value, loc, output);
110 Tensor output = value; // This creates an alias intentionally. variable
113 ctx, ::tensorflow::functor::DoParallelConcat(d, update, loc_, &output));
[all...] |
| matrix_diag_op.cc | 69 Tensor* output = nullptr; variable
70 OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
72 auto output_reshaped = output->flat_inner_dims<T, 2>();
106 Tensor* output = nullptr; variable
107 OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
109 auto output_reshaped = output->flat_inner_dims<T, 3>();
148 typename TTypes<T, 3>::Tensor output) {
149 output.device(d) = output.constant(T());
150 for (int64 r = 0; r < output.dimension(0); ++r)
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| matrix_inverse_op.cc | 120 // Allocate output.
121 Tensor* output; variable
124 {0}, 0, input.shape(), &output),
164 auto output_reshaped = output->template flat_inner_dims<Scalar, 3>();
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| matrix_set_diag_op.cc | 83 Tensor* output = nullptr; variable
85 {0}, 0, input_shape, &output));
86 auto output_reshaped = output->flat_inner_dims<T, 3>();
120 typename TTypes<T, 3>::Tensor output) {
121 if (input.data() != output.data()) {
122 output.device(device) = input;
124 auto compute_shard = [&output, &diag](int64 begin, int64 end) {
127 output(batch, col, col) = diag(batch, col);
133 int64 cost_per_batch = 10 * output.dimension(1); // Heuristic.
134 thread_pool->ParallelFor(output.dimension(0), cost_per_batch
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| matrix_triangular_solve_op.cc | 86 MatrixMap& output = outputs->at(0); variable
99 output.noalias() = triangle.adjoint().solve(rhs);
101 output.noalias() = triangle.solve(rhs);
106 output.noalias() = triangle.adjoint().solve(rhs);
108 output.noalias() = triangle.solve(rhs);
178 MatrixMap& output = outputs->at(0); variable
188 auto out_ptr = AsDeviceMemory(output.data());
197 errors::Internal("Failed to copy rhs into output before solve"));
201 // output = matrix \ rhs
202 // where matrix, rhs and output are assumed to be in column major
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| nth_element_op.cc | 90 T* output = output_tensor.flat<T>().data(); local
98 auto SubNthElement = [&, input, output, last_dim, n](int start, int limit) {
111 output[b] = buf[n];
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| pack_op.cc | 80 Tensor output; variable
81 CHECK(output.CopyFrom(values[0], output_shape));
82 c->set_output(0, output);
86 // Allocate output
87 Tensor* output; variable
88 OP_REQUIRES_OK(c, c->allocate_output(0, output_shape, &output));
102 const int64 output_size = output->NumElements();
105 output->shaped<T, 2>({before_dim, after_dim * axis_dim});
117 ConcatGPU<T>(c, inputs_flat, output, &output_flat);
170 .HostMemory("output")
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| pad_op.cc | 83 // Compute the shape of the output tensor, and allocate it.
98 // If there is no padding to be done, forward the input to output.
107 Tensor* output = nullptr; variable
108 OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
113 Operate<0>(context, in0.tensor<T, 0>(), paddings, pad_value, output);
118 Operate<1>(context, in0.flat<T>(), paddings, pad_value, output);
121 Operate<2>(context, in0.tensor<T, 2>(), paddings, pad_value, output);
124 Operate<3>(context, in0.tensor<T, 3>(), paddings, pad_value, output);
127 Operate<4>(context, in0.tensor<T, 4>(), paddings, pad_value, output);
130 Operate<5>(context, in0.tensor<T, 5>(), paddings, pad_value, output);
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| population_count_op.cc | 51 auto output = output_t->flat<uint8>(); variable
54 popcnt(c, input, output);
99 TTypes<uint8>::Flat output) {
101 uint8* output_ptr = output.data();
110 // (bitset.count() -> output). The .count() itself is relatively cheap.
147 TTypes<uint8>::Flat output); \
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| quantize_and_dequantize_op.cc | 57 Tensor* output = nullptr; variable
58 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
79 range_given_, &input_min_tensor, &input_max_tensor, output->flat<T>());
107 Tensor* output = nullptr; variable
108 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
138 range_given_, &input_min_tensor, &input_max_tensor, output->flat<T>());
170 Tensor* output = nullptr; variable
171 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, input.shape(), &output));
183 output->flat<T>());
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| reference_gemm.h | 82 int32_t output = ((((total + offset_c) * mult_c) + rounding) >> shift_c); local
83 if (output > highest) {
84 output = highest;
86 if (output < lowest) {
87 output = lowest;
89 c[c_index] = static_cast<T3>(output);
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| reverse_op_test.cc | 57 Tensor* output = GetOutput(0); local
60 test::ExpectTensorEqual<T>(expected, *output);
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| reverse_sequence_op.cc | 133 Tensor* output = nullptr; variable
135 context->allocate_output(0, input.shape(), &output));
141 seq_dim_, seq_lens_t, output->tensor<T, NDIM>()); \
189 typename TTypes<T, Dims>::Tensor output); \
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| roll_op.cc | 40 const T* input, T* output, const gtl::ArraySlice<int>& threshold,
42 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range](
62 output[i + offset] = input[i];
103 const T* input, T* output,
107 auto work = [input, output, num_dims, &dim_size, &threshold, &dim_range, isd](
125 T* out_ptr = &output[0];
285 Tensor* output = NULL; variable
287 context->allocate_output(0, input.shape(), &output));
289 auto output_flat = output->flat<T>().data();
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| scan_ops.cc | 63 Tensor* output = nullptr; variable
64 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, output_shape, &output));
83 output->shaped<T, 3>(reduced_shape),
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| spectrogram_test.cc | 54 std::vector<std::vector<complex<double>>> output; local
55 sgram.ComputeComplexSpectrogram(input, &output);
56 EXPECT_EQ(0, output.size());
66 std::vector<std::vector<complex<double>>> output; local
67 sgram.ComputeComplexSpectrogram(input, &output);
68 EXPECT_EQ(2, output.size());
78 std::vector<std::vector<complex<double>>> output; local
79 sgram.ComputeComplexSpectrogram(input, &output);
80 EXPECT_EQ(2, output.size());
91 std::vector<std::vector<complex<double>>> output; local
107 std::vector<std::vector<complex<double>>> output; local
132 std::vector<std::vector<complex<double>>> output; local
151 std::vector<std::vector<complex<double>>> output; local
246 std::vector<std::vector<complex<double>>> output; local
268 std::vector<std::vector<complex<float>>> output; local
288 std::vector<std::vector<double>> output; local
310 std::vector<std::vector<float>> output; local
335 std::vector<std::vector<complex<double>>> output; local
[all...] |
| substr_op.cc | 54 // Allocate output
57 context->allocate_output("output", input_tensor.shape(),
59 auto output = output_tensor->flat<string>(); variable
72 output(i) = in.substr(pos, len);
86 output(i) = in.substr(pos, len);
104 OP_REQUIRES_OK(context, context->allocate_output("output", output_shape,
110 auto output = output_tensor->shaped<string, 1>(bcast.result_shape()); variable
152 output(i) = in.substr(pos, len);
159 auto output = output_tensor->shaped<string, 2>(bcast.result_shape()); variable
204 output(i, j) = in.substr(pos, len)
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| transpose_op.cc | 58 Tensor* output = nullptr; variable
60 context->allocate_output(0, input.shape(), &output));
61 auto Tout = output->vec<T>();
129 // output = TransposeOp(T<any> input, T<int32> perm) takes a tensor
133 // Specifically, the returned tensor output meets the following condition:
134 // 1) output.dims() == input.dims();
135 // 2) output.dim_size(i) == input.dim_size(perm[i]);
136 // 3) output.tensor<T, N>(i_0, i_1, ..., i_N-1) ==
191 Tensor output; local
192 OP_REQUIRES(ctx, output.CopyFrom(input, shape)
198 Tensor* output = nullptr; local
[all...] |
| unpack_op.cc | 71 errors::InvalidArgument("output size must fit in Eigen DenseIndex"));
85 Tensor output; variable
86 CHECK(output.CopyFrom(input.Slice(i, i + 1), output_shape));
87 context->set_output(i, output);
110 Tensor* output; variable
112 context->allocate_output(i, output_shape, &output));
115 auto output_shaped = output->shaped<T, 3>({1, before_dim, after_dim});
155 .HostMemory("output")
161 .HostMemory("output")
178 .HostMemory("output")
[all...] |
| /external/tensorflow/tensorflow/core/platform/cloud/ |
| gcs_dns_cache.cc | 104 std::vector<string> output; local
121 output.emplace_back(buf);
131 return output;
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| /external/tensorflow/tensorflow/java/src/main/java/org/tensorflow/ |
| Operation.java | 22 * other Operations in the Graph) as input, and produces zero or more {@link Tensor}s as output.
78 * output of the operation.
82 * @return the size of the list of Tensors produced by this named output.
83 * @throws IllegalArgumentException if this operation has no output with the provided name.
99 * @return array of {@code Output}
101 public Output<?>[] outputList(int idx, int length) {
102 Output<?>[] outputs = new Output<?>[length];
104 outputs[i] = output(idx + i);
114 * operation.<Integer>output(0)
120 public <T> Output<T> output(int idx) { method in class:Operation
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| /external/tensorflow/tensorflow/python/framework/ |
| cpp_shape_inference.cc | 135 // Convert output shapes.
140 ProtoFromShapeHandle(c.output(i), &c, out.mutable_shape());
192 std::vector<string> output; local
197 &output, &input_tensors_needed_out);
203 output.push_back(input_tensors_needed_out);
204 return output;
|
| /external/v8/src/compiler/ |
| instruction-scheduler.cc | 186 const InstructionOperand* output = instr->OutputAt(i); local
187 if (output->IsUnallocated()) {
188 operands_map_[UnallocatedOperand::cast(output)->virtual_register()] =
190 } else if (output->IsConstant()) {
191 operands_map_[ConstantOperand::cast(output)->virtual_register()] =
|
| /external/vogar/src/vogar/ |
| Outcome.java | 35 private final String output; field in class:Outcome
41 this.output = sanitizeOutputLines(outputLines);
48 this.output = sanitizeOutputLine(outputLine);
55 this.output = sanitizeOutputLine(outputLine);
62 this.output = sanitizeOutputLines(throwableToLines(throwable));
91 return output;
95 return Arrays.asList(output.split("\n"));
163 && output.equals(outcome.output);
172 hashCode = 37 * hashCode + output.hashCode()
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| /external/vogar/src/vogar/commands/ |
| VmCommandBuilder.java | 46 private PrintStream output; field in class:VmCommandBuilder
113 public VmCommandBuilder output(PrintStream output) { method in class:VmCommandBuilder
114 this.output = output;
159 // Only output this if there's something on the boot classpath,
178 .tee(output)
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