/external/tensorflow/tensorflow/contrib/tensor_forest/python/kernel_tests/ |
H A D | scatter_add_ndim_op_test.py | 30 input_data = variables.Variable( 37 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run() 40 input_data.eval()) 43 input_data = variables.Variable([[[1., 2., 3.], [4., 5., 6.]], 50 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run() 52 [[7., 8., 9.], [10., 11., 212.]]], input_data.eval()) 56 input_data = variables.Variable(init_val) 62 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run() 63 self.assertAllEqual(init_val, input_data.eval()) 67 input_data [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
H A D | reinterpret_string_to_float_op.cc | 38 void Evaluate(const Tensor& input_data, Tensor output_data, int32 start, argument 41 const auto in_data = input_data.unaligned_flat<string>(); 54 const Tensor& input_data = context->input(0); variable 57 if (!CheckTensorBounds(context, input_data)) return; 61 context, context->allocate_output(0, input_data.shape(), &output_data)); 64 const int32 num_data = static_cast<int32>(input_data.NumElements()); 68 Evaluate(input_data, *output_data, 0, num_data); 70 auto work = [&input_data, output_data, num_data](int64 start, int64 end) { 73 Evaluate(input_data, *output_data, static_cast<int32>(start),
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/external/tensorflow/tensorflow/examples/tutorials/mnist/ |
H A D | __init__.py | 21 from tensorflow.examples.tutorials.mnist import input_data namespace
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/external/protobuf/src/google/protobuf/util/ |
H A D | json_util_test.cc | 242 string input_data = "0123456789"; local 243 for (int input_pattern = 0; input_pattern < (1 << (input_data.size() - 1)); 250 for (int j = 0; j < input_data.length() - 1; ++j) { 252 byte_sink.Append(&input_data[start], j - start + 1); 256 byte_sink.Append(&input_data[start], input_data.length() - start); 258 EXPECT_EQ(input_data, string(buffer, input_data.length())); 262 input_data = "012345678"; 263 for (int input_pattern = 0; input_pattern < (1 << (input_data [all...] |
/external/tensorflow/tensorflow/python/keras/_impl/keras/ |
H A D | testing_utils.py | 55 input_data=None, expected_output=None, 65 input_data: Numpy array of input data. 73 if input_data is None: 81 input_data = 10 * np.random.random(input_data_shape) 83 input_data -= 0.5 84 input_data = input_data.astype(input_dtype) 86 input_shape = input_data.shape 88 input_dtype = input_data.dtype 121 actual_output = model.predict(input_data) [all...] |
/external/tensorflow/tensorflow/core/kernels/ |
H A D | spectrogram_convert_test_data.cc | 29 std::vector<std::vector<std::complex<double>>> input_data; local 30 ReadCSVFileToComplexVectorOrDie(input_filename, &input_data); 32 if (!WriteComplexVectorToRawFloatFile(output_filename, input_data)) {
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H A D | colorspace_op.h | 30 typename TTypes<T, 2>::ConstTensor input_data, 37 auto R = input_data.template chip<1>(0); 38 auto G = input_data.template chip<1>(1); 39 auto B = input_data.template chip<1>(2); 47 V.device(d) = input_data.maximum(channel_axis); 49 range.device(d) = V - input_data.minimum(channel_axis); 68 typename TTypes<T, 2>::ConstTensor input_data, 70 auto H = input_data.template chip<1>(0); 71 auto S = input_data.template chip<1>(1); 72 auto V = input_data 29 operator ()(const Device &d, typename TTypes<T, 2>::ConstTensor input_data, typename TTypes<T, 1>::Tensor range, typename TTypes<T, 2>::Tensor output_data) argument 67 operator ()(const Device &d, typename TTypes<T, 2>::ConstTensor input_data, typename TTypes<T, 2>::Tensor output_data) argument [all...] |
H A D | colorspace_op.cc | 65 typename TTypes<T, 2>::ConstTensor input_data = input.flat_inner_dims<T>(); variable 71 TensorShape({input_data.dimension(0)}), 76 functor::RGBToHSV<Device, T>()(context->eigen_device<Device>(), input_data, 102 typename TTypes<T, 2>::ConstTensor input_data = input.flat_inner_dims<T>(); variable 105 functor::HSVToRGB<Device, T>()(context->eigen_device<Device>(), input_data, 129 const GPUDevice& d, TTypes<T, 2>::ConstTensor input_data, \ 134 const GPUDevice& d, TTypes<T, 2>::ConstTensor input_data, \
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/external/tensorflow/tensorflow/examples/speech_commands/ |
H A D | input_data_test.py | 27 from tensorflow.examples.speech_commands import input_data namespace 66 len(input_data.prepare_words_list(words_list)), len(words_list)) 70 input_data.which_set("foo.wav", 10, 10), 71 input_data.which_set("foo.wav", 10, 10)) 73 input_data.which_set("foo_nohash_0.wav", 10, 10), 74 input_data.which_set("foo_nohash_1.wav", 10, 10)) 79 audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 85 self.assertEquals(input_data.UNKNOWN_WORD_INDEX, 92 _ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 10, 10, 100 _ = input_data [all...] |
/external/bsdiff/ |
H A D | bz2_decompressor.h | 18 bool SetInputData(const uint8_t* input_data, size_t size) override;
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H A D | brotli_decompressor.cc | 11 bool BrotliDecompressor::SetInputData(const uint8_t* input_data, size_t size) { argument 18 next_in_ = input_data;
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H A D | brotli_decompressor.h | 20 bool SetInputData(const uint8_t* input_data, size_t size) override;
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H A D | decompressor_interface.h | 21 // Set the buffer starting from |input_data| with length |size| as the input 24 virtual bool SetInputData(const uint8_t* input_data, size_t size) = 0;
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/external/libjpeg-turbo/ |
H A D | jdsample.h | 17 JSAMPARRAY input_data,
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H A D | jsimd.h | 43 JSAMPARRAY input_data, JSAMPARRAY output_data); 49 JSAMPARRAY input_data, JSAMPARRAY output_data); 53 JSAMPARRAY input_data, JSAMPARRAY output_data); 61 JSAMPARRAY input_data, JSAMPARRAY *output_data_ptr); 64 JSAMPARRAY input_data, JSAMPARRAY *output_data_ptr); 67 JSAMPARRAY input_data, JSAMPARRAY *output_data_ptr); 74 JSAMPARRAY input_data, JSAMPARRAY *output_data_ptr); 77 JSAMPARRAY input_data, JSAMPARRAY *output_data_ptr);
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H A D | jcsample.c | 62 JSAMPARRAY input_data, 148 JSAMPARRAY input_data, JSAMPARRAY output_data) 165 expand_right_edge(input_data, cinfo->max_v_samp_factor, 175 inptr = input_data[inrow+v] + outcol_h; 195 JSAMPARRAY input_data, JSAMPARRAY output_data) 198 jcopy_sample_rows(input_data, 0, output_data, 0, 220 JSAMPARRAY input_data, JSAMPARRAY output_data) 232 expand_right_edge(input_data, cinfo->max_v_samp_factor, 237 inptr = input_data[outrow]; 257 JSAMPARRAY input_data, JSAMPARRA 147 int_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 194 fullsize_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 219 h2v1_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 256 h2v2_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 299 h2v2_smooth_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 399 fullsize_smooth_downsample(j_compress_ptr cinfo, jpeg_component_info *compptr, JSAMPARRAY input_data, JSAMPARRAY output_data) argument [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/core/ops/ |
H A D | routing_function_op.cc | 51 .Input("input_data: float") 68 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]` 88 const Tensor& input_data = context->input(0); variable 92 if (input_data.shape().dim_size(0) > 0) { 94 context, input_data.shape().dims() == 2, 95 errors::InvalidArgument("input_data should be two-dimensional")); 99 if (!CheckTensorBounds(context, input_data)) return; 101 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0)); 103 static_cast<int32>(input_data [all...] |
H A D | hard_routing_function_op.cc | 52 .Input("input_data: float") 69 Chooses a single path for each instance in `input_data` and returns the leaf 74 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]` 97 const Tensor& input_data = context->input(0); variable 101 if (input_data.shape().dim_size(0) > 0) { 103 context, input_data.shape().dims() == 2, 104 errors::InvalidArgument("input_data should be two-dimensional")); 108 if (!CheckTensorBounds(context, input_data)) return; 110 const int32 num_data = static_cast<int32>(input_data [all...] |
H A D | k_feature_routing_function_op.cc | 54 .Input("input_data: float") 77 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]` 103 const Tensor& input_data = context->input(0); variable 107 if (input_data.shape().dim_size(0) > 0) { 109 context, input_data.shape().dims() == 2, 110 errors::InvalidArgument("input_data should be two-dimensional")); 114 if (!CheckTensorBounds(context, input_data)) return; 116 const int32 num_data = static_cast<int32>(input_data.shape().dim_size(0)); 118 static_cast<int32>(input_data [all...] |
H A D | stochastic_hard_routing_function_op.cc | 56 .Input("input_data: float") 73 Samples a path for each instance in `input_data` and returns the 79 input_data: The training batch's features as a 2-d tensor; `input_data[i][j]` 108 const Tensor& input_data = context->input(0); variable 112 if (input_data.shape().dim_size(0) > 0) { 114 context, input_data.shape().dims() == 2, 115 errors::InvalidArgument("input_data should be two-dimensional")); 119 if (!CheckTensorBounds(context, input_data)) return; 121 const int32 num_data = static_cast<int32>(input_data [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/python/ |
H A D | tensor_forest_test.py | 64 input_data = [[-1., 0.], [-1., 2.], # node 1 76 graph = graph_builder.training_graph(input_data, input_labels) 80 input_data = [[-1., 0.], [-1., 2.], # node 1 93 graph = graph_builder.training_graph(input_data, input_labels) 97 input_data = [[-1., 0.], [-1., 2.], # node 1 108 probs, paths, var = graph_builder.inference_graph(input_data) 114 input_data = sparse_tensor.SparseTensor( 128 graph = graph_builder.training_graph(input_data, input_labels) 132 input_data = sparse_tensor.SparseTensor( 152 probs, paths, var = graph_builder.inference_graph(input_data) [all...] |
/external/tensorflow/tensorflow/contrib/lite/kernels/internal/optimized/ |
H A D | cblas_conv.h | 34 inline void Conv(const float* input_data, const Dims<4>& input_dims, argument 52 optimized_ops::Im2col(input_data, input_dims, stride_width, stride_height, 59 gemm_input_data = input_data;
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/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
H A D | fertile-stats-resource.cc | 23 const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target, 27 collection_op_->AddExample(input_data, target, examples, node_id); 33 input_data, target, example, node_id); 22 AddExampleToStatsAndInitialize( const std::unique_ptr<TensorDataSet>& input_data, const InputTarget* target, const std::vector<int>& examples, int32 node_id, bool* is_finished) argument
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/external/tensorflow/tensorflow/compiler/xla/tests/ |
H A D | convolution_variants_test.cc | 383 std::vector<float> input_data(64); 384 std::iota(input_data.begin(), input_data.end(), 0.0); 385 Array4D<float> input_array(1, 1, 8, 8, input_data); 403 std::vector<float> input_data(16 * 1 * 1 * 1); 404 std::iota(input_data.begin(), input_data.end(), 1.0); 405 Array4D<float> input_array(16, 1, 1, 1, input_data); 515 std::vector<float> input_data(2 * 8 * 8); 516 std::iota(input_data [all...] |
/external/libjpeg-turbo/simd/ |
H A D | jcsample-altivec.c | 33 JSAMPARRAY input_data, JSAMPARRAY output_data) 49 expand_right_edge(input_data, max_v_samp_factor, image_width, 54 inptr = input_data[outrow]; 89 JSAMPARRAY input_data, JSAMPARRAY output_data) 106 expand_right_edge(input_data, max_v_samp_factor, image_width, 112 inptr0 = input_data[inrow]; 113 inptr1 = input_data[inrow + 1]; 30 jsimd_h2v1_downsample_altivec(JDIMENSION image_width, int max_v_samp_factor, JDIMENSION v_samp_factor, JDIMENSION width_blocks, JSAMPARRAY input_data, JSAMPARRAY output_data) argument 86 jsimd_h2v2_downsample_altivec(JDIMENSION image_width, int max_v_samp_factor, JDIMENSION v_samp_factor, JDIMENSION width_blocks, JSAMPARRAY input_data, JSAMPARRAY output_data) argument
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