| /external/tensorflow/tensorflow/core/kernels/ |
| D | cudnn_pooling_gpu.cc | 40 const Tensor& tensor_in, Tensor* output) { in Compute() argument
41 const auto in_shape = tensor_in.shape(); in Compute()
44 const int64 in_batch = GetTensorDim(tensor_in, data_format, 'N'); in Compute()
45 const int64 in_features = GetTensorDim(tensor_in, data_format, 'C'); in Compute()
51 ShapeFromFormat(FORMAT_NCHW, tensor_in.shape(), in Compute()
55 tensor_in.tensor<T, 5>(), in Compute()
58 transformed_input = tensor_in; in Compute()
87 GetTensorDim(tensor_in, data_format, '2' - i)); in Compute()
124 const Tensor* tensor_in, const Tensor* tensor_out, Tensor* input_backprop) { in Compute() argument
126 (tensor_in && tensor_out)) in Compute()
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| D | pooling_ops_3d.cc | 102 static void launch(OpKernelContext* context, const Tensor& tensor_in, in launch()
109 Eigen::CuboidAvgPooling(tensor_in.tensor<T, 5>(), window[0], window[1], in launch()
117 static void launch(OpKernelContext* context, const Tensor& tensor_in, in launch()
124 Eigen::CuboidMaxPooling(tensor_in.tensor<T, 5>(), window[0], window[1], in launch()
167 const Tensor& tensor_in = context->input(0); in Compute() local
169 OP_REQUIRES(context, tensor_in.dims() == 5, in Compute()
171 const int64 depth = GetTensorDim(tensor_in, data_format_, 'C'); in Compute()
172 const int64 in_batch = GetTensorDim(tensor_in, data_format_, 'N'); in Compute()
176 {GetTensorDim(tensor_in, data_format_, '2'), in Compute()
177 GetTensorDim(tensor_in, data_format_, '1'), in Compute()
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| D | pooling_ops_common.h | 110 const Tensor& tensor_in = context->input(0); in Compute() local
112 padding_, FORMAT_NHWC, tensor_in.shape()}; in Compute()
133 DepthwiseMaxPool(context, output, tensor_in, params); in Compute()
135 SpatialMaxPool(context, output, tensor_in, params, padding_); in Compute()
147 const Tensor& tensor_in, const PoolParameters& params) { in DepthwiseMaxPool() argument
149 in_by_pool(tensor_in.flat<T>().data(), params.depth_window, in DepthwiseMaxPool()
150 tensor_in.NumElements() / params.depth_window); in DepthwiseMaxPool()
157 const Tensor& tensor_in, const PoolParameters& params, in SpatialMaxPool() argument
168 tensor_in.tensor<T, 4>(), params.window_rows, params.window_cols, in SpatialMaxPool()
176 ConstEigenMatrixMap in_mat(tensor_in.flat<T>().data(), params.depth, in SpatialMaxPool()
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| D | maxpooling_op.cc | 60 Tensor* input_backprop, const Tensor& tensor_in, const Tensor& out_backprop, in SpatialMaxPoolWithArgMaxHelper() argument
78 tensor_in.flat<T>().data(), params.depth, in SpatialMaxPoolWithArgMaxHelper()
247 const Tensor& tensor_in = context->input(0); in Compute() local
252 OP_REQUIRES(context, tensor_in.dims() == 4, in Compute()
260 const TensorShape& output_shape = tensor_in.shape(); in Compute()
299 padding_, FORMAT_NHWC, tensor_in.shape()}; in Compute()
309 context, &tensor_out_dup, &tensor_out_arg_max, output, tensor_in, in Compute()
325 const std::vector<int32>& stride, Padding padding, const Tensor* tensor_in, in MaxPoolingBackwardCustomKernel() argument
338 tensor_in->flat<T>().data(), params.tensor_in_batch, in MaxPoolingBackwardCustomKernel()
379 const Tensor& tensor_in = context->input(0); in Compute() local
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| D | pooling_ops_common.cc | 158 TensorFormat data_format, const Tensor& tensor_in, in Compute() argument
164 if (tensor_in.shape().num_elements() == 0) { in Compute()
169 padding, data_format, tensor_in.shape()}; in Compute()
183 ShapeFromFormat(FORMAT_NCHW, tensor_in.shape(), in Compute()
187 tensor_in.tensor<T, 4>(), in Compute()
190 transformed_input = tensor_in; in Compute()
204 auto& transformed_input = tensor_in; in Compute()
288 Padding padding, TensorFormat data_format, const Tensor* tensor_in, in Compute() argument
292 (tensor_in && tensor_out)) in Compute()
313 if (data_format == FORMAT_NHWC || !tensor_in) { in Compute()
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| D | pooling_ops_3d_sycl.h | 179 static void launch(OpKernelContext* context, const Tensor& tensor_in,
189 const int batch = GetTensorDim(tensor_in, data_format, 'N');
190 const int in_planes = GetTensorDim(tensor_in, data_format, '0');
191 const int in_rows = GetTensorDim(tensor_in, data_format, '1');
192 const int in_cols = GetTensorDim(tensor_in, data_format, '2');
193 const int depth = GetTensorDim(tensor_in, data_format, 'C');
198 device.get_sycl_buffer(tensor_in.template flat<T>().data());
347 static void launch(OpKernelContext* context, const Tensor& tensor_in,
355 const int batch = GetTensorDim(tensor_in, data_format, 'N');
356 const int in_planes = GetTensorDim(tensor_in, data_format, '0');
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| D | quantized_pooling_ops.cc | 56 const Tensor& tensor_in = context->input(0); in Compute() local
58 padding_, FORMAT_NHWC, tensor_in.shape()}; in Compute()
70 OP_REQUIRES(context, tensor_in.dims() == 4, in Compute()
83 Tensor int32_input(DT_INT32, tensor_in.shape()); in Compute()
84 int32_input.flat<int32>() = tensor_in.flat<T>().template cast<int32>(); in Compute()
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| D | fractional_max_pool_op.cc | 78 const Tensor& tensor_in = context->input(0); in Compute() local
79 OP_REQUIRES(context, tensor_in.dims() == tensor_in_and_out_dims, in Compute()
85 input_size[i] = tensor_in.dim_size(i); in Compute()
125 ConstEigenMatrixMap in_mat(tensor_in.flat<T>().data(), input_size[3], in Compute()
230 const Tensor& tensor_in = context->input(0); in Compute() local
241 input_size[i] = tensor_in.dim_size(i); in Compute()
260 tensor_in.flat<T>().data(), input_size[3], in Compute()
339 {0}, 0, tensor_in.shape(), &output)); in Compute()
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| D | avgpooling_op.cc | 77 const Tensor& tensor_in = context->input(0); in Compute() local
79 padding_, data_format_, tensor_in.shape()}; in Compute()
88 OP_REQUIRES(context, tensor_in.dims() == 4, in Compute()
95 SpatialAvgPool<Device, T>(context, output, tensor_in, params, padding_); in Compute()
142 const Tensor& tensor_in = context->input(0); in Compute() local
144 padding_, data_format_, tensor_in.shape()}; in Compute()
153 OP_REQUIRES(context, tensor_in.dims() == 4, in Compute()
160 stride_, padding_, data_format_, tensor_in, in Compute()
170 tensor_in.tensor<T, 4>(), params.window_rows, params.window_cols, in Compute()
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| D | cudnn_pooling_gpu.h | 45 TensorFormat data_format, const Tensor& tensor_in,
62 const Tensor* tensor_in, const Tensor* tensor_out,
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| D | pooling_ops_common_gpu.h | 46 TensorFormat data_format, const Tensor& tensor_in,
61 TensorFormat data_format, const Tensor* tensor_in,
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| D | mkl_maxpooling_op.cc | 79 const Tensor& tensor_in = MklGetInput(context, 0); in Compute() local
87 tensor_in.shape()); in Compute()
135 const_cast<void*>(static_cast<const void*>(tensor_in.flat<T>().data())); in Compute()
237 const Tensor& tensor_in = MklGetInput(context, 0); in Compute() local
247 mkl_context.params.in_dim = tensor_in.dims(); in Compute()
254 tensor_in.shape()); in Compute()
391 const Tensor& tensor_in = MklGetInput(context, 0); in MklPrepareInputs() local
405 tensor_in.flat<T>().data())), in MklPrepareInputs()
414 static_cast<const void*>(tensor_in.flat<T>().data())), in MklPrepareInputs()
420 static_cast<const void*>(tensor_in.flat<T>().data())); in MklPrepareInputs()
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| D | fractional_avg_pool_op.cc | 75 const Tensor& tensor_in = context->input(0); in Compute() local
76 OP_REQUIRES(context, tensor_in.dims() == tensor_in_and_out_dims, in Compute()
82 input_size[i] = tensor_in.dim_size(i); in Compute()
119 ConstEigenMatrixMap in_mat(tensor_in.flat<T>().data(), input_size[3], in Compute()
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| D | mkl_avgpooling_op.cc | 70 const Tensor& tensor_in = MklGetInput(context, 0); in Compute() local
75 mkl_context.params.in_dim = tensor_in.dims(); in Compute()
82 tensor_in.shape()); in Compute()
106 static_cast<void*>(const_cast<T*>(tensor_in.flat<T>().data())), in Compute()
113 static_cast<void*>(const_cast<T*>(tensor_in.flat<T>().data())), in Compute()
119 static_cast<void*>(const_cast<T*>(tensor_in.flat<T>().data())); in Compute()
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| D | losses_ops.py | 37 def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None): argument
40 [tensor_in, labels]):
41 predictions = nn.xw_plus_b(tensor_in, weights, biases)
49 def softmax_classifier(tensor_in, argument
76 with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
77 logits = nn.xw_plus_b(tensor_in, weights, biases)
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| D | embeddings_ops.py | 73 def categorical_variable(tensor_in, n_classes, embedding_size, name): argument
92 return embedding_lookup(embeddings, tensor_in)
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| /external/tensorflow/tensorflow/contrib/quantize/python/ |
| D | quantize_test.py | 222 [tensor_in.name for tensor_in in conv_op.inputs])
|