| /external/tensorflow/tensorflow/core/kernels/ |
| cudnn_pooling_gpu.h | 62 const Tensor* tensor_in, const Tensor* tensor_out,
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| pooling_ops_common_gpu.h | 62 const Tensor* tensor_out, const Tensor& out_backprop,
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| pooling_ops_common.cc | 147 Tensor* tensor_out = nullptr; local
149 context->allocate_output(0, tensor_out_shape, &tensor_out));
183 transformed_output = *tensor_out;
233 tensor_out->tensor<T, 4>());
243 const Tensor* tensor_out, const Tensor& out_backprop,
246 (tensor_in && tensor_out))
247 << "For MaxPoolGrad, both tensor_in and tensor_out needs to be "
278 if (data_format == FORMAT_NHWC || !tensor_out) {
285 transformed_output = *tensor_out;
316 if (tensor_out) {
[all...] |
| mkl_input_conversion_op.cc | 190 Tensor* tensor_out; variable
202 AllocateOutputSetMklShape(context, tf_tensor_index, &tensor_out,
212 const_cast<T*>(tensor_out->flat<T>().data()));
315 Tensor* tensor_out; variable
327 AllocateOutputSetMklShape(context, 0, &tensor_out,
340 tensor_out, &net),
423 Tensor* tensor_out; variable
435 AllocateOutputSetMklShape(context, tf_tensor_index, &tensor_out,
449 tensor_out, &net),
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| cudnn_pooling_gpu.cc | 124 const Tensor* tensor_in, const Tensor* tensor_out, Tensor* input_backprop) {
126 (tensor_in && tensor_out))
127 << "For MaxPoolGrad, both tensor_in and tensor_out needs to be "
146 if (data_format == FORMAT_NHWC || tensor_out == nullptr) {
153 transformed_output = *tensor_out;
179 if (tensor_out != nullptr) {
181 tensor_out->tensor<T, 5>(),
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| pooling_ops_3d.cc | 209 const Tensor& tensor_out, const Tensor& out_backprop,
273 // Slice from tensor_out.
276 tensor_out.tensor<T, 5>().slice(src_indices, src_sizes);
341 const Tensor& tensor_out = context->input(1); variable
345 OP_REQUIRES(context, tensor_out.dims() == 5,
346 errors::InvalidArgument("tensor_out must be 5-dimensional"));
369 context, tensor_in, tensor_out, out_backprop, window, stride, out,
544 const Tensor& tensor_in, const Tensor& tensor_out,
561 ConstEigenMatrixMap out_mat(tensor_out.flat<T>().data(), params.depth,
685 const Tensor& tensor_out = context->input(1) variable
[all...] |
| fractional_max_pool_op.cc | 231 const Tensor& tensor_out = context->input(1); variable
244 output_size[i] = tensor_out.dim_size(i);
252 {1}, DataTypeToEnum<T>::v(), tensor_out.shape(),
256 tensor_out.shape(),
258 // Find arg_max for each tensor_out
325 // Check tensor_out_dup is the same as tensor_out.
327 tensor_out.flat<T>().data(), output_size[3],
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| maxpooling_op.cc | 234 const Tensor& tensor_out = context->input(1); variable
240 OP_REQUIRES(context, tensor_out.dims() == 4,
241 errors::InvalidArgument("tensor_out must be 4-dimensional"));
250 {1}, DataTypeToEnum<T>::v(), tensor_out.shape(),
254 tensor_out.shape(),
366 const Tensor& tensor_out = context->input(1); variable
372 OP_REQUIRES(context, tensor_out.dims() == 4,
373 errors::InvalidArgument("tensor_out must be 4-dimensional"));
408 stride, padding_, data_format_, &tensor_in, &tensor_out, out_backprop,
472 const Tensor& tensor_out = context->input(1) variable
669 const Tensor& tensor_out = context->input(1); variable
[all...] |
| pooling_ops_3d_sycl.h | 348 const Tensor& tensor_out, const Tensor& out_backprop,
366 device.get_sycl_buffer(tensor_out.template flat<T>().data());
477 const Tensor& tensor_in, const Tensor& tensor_out,
486 device.get_sycl_buffer(tensor_out.template flat<T>().data());
[all...] |
| /external/tensorflow/tensorflow/core/util/ |
| mkl_util.h | [all...] |
