Searched refs:input_data (Results 1 - 25 of 180) sorted by relevance

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/external/tensorflow/tensorflow/contrib/tensor_forest/python/kernel_tests/
H A Dscatter_add_ndim_op_test.py30 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
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/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/
H A Dreinterpret_string_to_float_op.cc38 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),
/external/tensorflow/tensorflow/examples/tutorials/mnist/
H A D__init__.py21 from tensorflow.examples.tutorials.mnist import input_data namespace
/external/protobuf/src/google/protobuf/util/
H A Djson_util_test.cc242 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
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/external/tensorflow/tensorflow/python/keras/_impl/keras/
H A Dtesting_utils.py55 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)
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/external/tensorflow/tensorflow/core/kernels/
H A Dspectrogram_convert_test_data.cc29 std::vector<std::vector<std::complex<double>>> input_data; local
30 ReadCSVFileToComplexVectorOrDie(input_filename, &input_data);
32 if (!WriteComplexVectorToRawFloatFile(output_filename, input_data)) {
H A Dcolorspace_op.h30 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
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H A Dcolorspace_op.cc65 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, \
/external/tensorflow/tensorflow/examples/speech_commands/
H A Dinput_data_test.py27 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
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/external/bsdiff/
H A Dbz2_decompressor.h18 bool SetInputData(const uint8_t* input_data, size_t size) override;
H A Dbrotli_decompressor.cc11 bool BrotliDecompressor::SetInputData(const uint8_t* input_data, size_t size) { argument
18 next_in_ = input_data;
H A Dbrotli_decompressor.h20 bool SetInputData(const uint8_t* input_data, size_t size) override;
H A Ddecompressor_interface.h21 // 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;
/external/libjpeg-turbo/
H A Djdsample.h17 JSAMPARRAY input_data,
H A Djsimd.h43 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);
H A Djcsample.c62 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
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/core/ops/
H A Drouting_function_op.cc51 .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
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H A Dhard_routing_function_op.cc52 .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
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H A Dk_feature_routing_function_op.cc54 .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
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H A Dstochastic_hard_routing_function_op.cc56 .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
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/external/tensorflow/tensorflow/contrib/tensor_forest/python/
H A Dtensor_forest_test.py64 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)
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/external/tensorflow/tensorflow/contrib/lite/kernels/internal/optimized/
H A Dcblas_conv.h34 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;
/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/
H A Dfertile-stats-resource.cc23 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
/external/tensorflow/tensorflow/compiler/xla/tests/
H A Dconvolution_variants_test.cc383 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
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/external/libjpeg-turbo/simd/
H A Djcsample-altivec.c33 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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