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Searched refs:out_backprop (Results 1 – 25 of 57) sorted by relevance

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/external/tensorflow/tensorflow/compiler/xla/client/lib/
Dpooling_test.cc188 XlaOp out_backprop = ConstantR4FromArray4D<float>(&builder, {{{{1.}}}}); in XLA_TEST_F() local
192 AvgPoolGrad(out_backprop, {1, 1, 3, 3}, kernel_size, stride, in XLA_TEST_F()
205 XlaOp out_backprop = in XLA_TEST_F() local
210 AvgPoolGrad(out_backprop, {1, 1, 3, 3}, kernel_size, stride, in XLA_TEST_F()
223 XlaOp out_backprop = in XLA_TEST_F() local
228 AvgPoolGrad(out_backprop, {1, 1, 3, 3}, kernel_size, stride, {{1, 1}, {1, 1}}, in XLA_TEST_F()
240 XlaOp out_backprop = in XLA_TEST_F() local
245 AvgPoolGrad(out_backprop, {1, 1, 3, 3}, kernel_size, stride, {{1, 1}, {1, 1}}, in XLA_TEST_F()
255 XlaOp out_backprop = in XLA_TEST_F() local
263 AvgPoolGrad(out_backprop, {1, 1, 3, 3}, kernel_size, stride, {{1, 1}, {1, 1}}, in XLA_TEST_F()
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Dpooling.cc192 XlaOp AvgPoolGrad(XlaOp out_backprop, absl::Span<const int64> gradients_size, in AvgPoolGrad() argument
198 XlaBuilder* b = out_backprop.builder(); in AvgPoolGrad()
208 b->GetShape(out_backprop)); in AvgPoolGrad()
234 out_backprop, gradients_size, kernel_size, stride, spatial_padding, in AvgPoolGrad()
/external/tensorflow/tensorflow/core/kernels/
Ddilation_ops.cc222 const Tensor& out_backprop = context->input(2); in Compute() local
238 batch == out_backprop.dim_size(0) && in Compute()
239 out_rows == out_backprop.dim_size(1) && in Compute()
240 out_cols == out_backprop.dim_size(2) && in Compute()
241 depth == out_backprop.dim_size(3), in Compute()
257 filter.tensor<T, 3>(), out_backprop.tensor<T, 4>(), stride_rows, in Compute()
273 typename TTypes<T, 4>::ConstTensor out_backprop, in operator ()()
285 const int output_rows = out_backprop.dimension(1); in operator ()()
286 const int output_cols = out_backprop.dimension(2); in operator ()()
322 out_backprop(b, h_out, w_out, d); in operator ()()
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Davgpooling_op.cc244 const Tensor& out_backprop = context->input(1); in Compute() local
252 OP_REQUIRES(context, out_backprop.dims() == 4, in Compute()
254 const int64 out_backprop_batch = out_backprop.dim_size(0); in Compute()
255 const int64 out_backprop_rows = out_backprop.dim_size(1); in Compute()
256 const int64 out_backprop_cols = out_backprop.dim_size(2); in Compute()
257 const int64 out_backprop_depth = out_backprop.dim_size(3); in Compute()
294 const T* out_backprop_ptr = out_backprop.flat<T>().data(); in Compute()
403 const Tensor& out_backprop = context->input(1); in Compute() local
411 OP_REQUIRES(context, out_backprop.dims() == 4, in Compute()
422 nullptr, nullptr, out_backprop, output_shape, in Compute()
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Ddepthwise_conv_grad_op.cc55 const Tensor& out_backprop = context->input(2); \
63 context, out_backprop.dims() == 4, \
67 context, batch == out_backprop.dim_size(0), \
85 GetTensorDim(out_backprop.shape(), data_format_, 'H'); \
92 GetTensorDim(out_backprop.shape(), data_format_, 'W'); \
104 GetTensorDim(out_backprop.shape(), data_format_, 'C'); \
183 const T* out_backprop, T* buffer) { in CopyOutputBackpropRegion() argument
224 out_backprop + (out_r * args.out_cols + out_c) * args.out_depth; in CopyOutputBackpropRegion()
375 const T* out_backprop, const T* depthwise_filter, in operator ()()
405 auto shard = [&ctx, &args, &out_backprop, &filter_data, &in_backprop]( in operator ()()
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Ddepthwise_conv_op_gpu.h817 const T* out_backprop,
870 sum += ldg(out_backprop + out_backprop_offset +
887 const T* out_backprop,
948 sum += ldg(out_backprop + out_backprop_offset) *
964 const T* out_backprop,
988 args, out_backprop, filter, in_backprop, num_in_backprop));
995 const T* out_backprop,
1002 ctx, args, out_backprop, filter, in_backprop, data_format);
1007 ctx, args, out_backprop, filter, in_backprop, data_format);
1011 ctx, args, out_backprop, filter, in_backprop, data_format);
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Dpooling_ops_3d.cc209 const Tensor& tensor_out, const Tensor& out_backprop, in launch()
216 for (int64 p = 0; p < out_backprop.dim_size(3); ++p) { in launch()
232 for (int64 r = 0; r < out_backprop.dim_size(2); ++r) { in launch()
237 for (int64 c = 0; c < out_backprop.dim_size(1); ++c) { in launch()
252 src.FillIndicesAndSizes<5>(out_backprop.shape(), &src_indices, in launch()
281 out_backprop.tensor<T, 5>().slice(src_indices, src_sizes); in launch()
342 const Tensor& out_backprop = context->input(2); in Compute() local
347 OP_REQUIRES(context, out_backprop.dims() == 5, in Compute()
369 context, tensor_in, tensor_out, out_backprop, window, stride, out, in Compute()
384 const Tensor& out_backprop, in launch()
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Ddilation_ops_gpu.cu.cc212 typename TTypes<T, 4>::ConstTensor out_backprop, in operator ()()
224 const int output_rows = out_backprop.dimension(1); in operator ()()
225 const int output_cols = out_backprop.dimension(2); in operator ()()
243 input.data(), filter.data(), out_backprop.data(), batch, input_rows, in operator ()()
254 typename TTypes<T, 4>::ConstTensor out_backprop, in operator ()()
266 const int output_rows = out_backprop.dimension(1); in operator ()()
267 const int output_cols = out_backprop.dimension(2); in operator ()()
285 out_backprop.data(), batch, input_rows, input_cols, depth, filter_rows, in operator ()()
Dconv_grad_ops_3d.cc220 const Tensor& out_backprop = context->input(2); in Compute() local
221 const TensorShape& out_backprop_shape = out_backprop.shape(); in Compute()
246 out_backprop.tensor<T, 5>(), // output_backward in Compute()
326 const Tensor& out_backprop = context->input(2); in Compute() local
327 const TensorShape& out_backprop_shape = out_backprop.shape(); in Compute()
436 out_backprop.tensor<T, 5>(), // output_backward in Compute()
461 const T* out_backprop_data = out_backprop.template flat<T>().data(); in Compute()
676 const Tensor& out_backprop = context->input(2); in Compute() local
677 const TensorShape& out_backprop_shape = out_backprop.shape(); in Compute()
708 out_backprop.tensor<T, 5>(), // output_backward in Compute()
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Dbias_op_test.cc28 Tensor out_backprop(DT_FLOAT, TensorShape({d0, d1, d2, d3})); in BiasAddGrad() local
29 out_backprop.flat<float>().setRandom(); in BiasAddGrad()
30 test::graph::Unary(g, "BiasAddGrad", test::graph::Constant(g, out_backprop)); in BiasAddGrad()
Dbatch_norm_op.h79 typename TTypes<T, 4>::ConstTensor out_backprop, in operator()
114 db.device(d) = out_backprop.reshape(rest_by_depth).sum(reduction_axis); in operator()
120 scratch2.device(d) = (out_backprop.reshape(rest_by_depth) * in operator()
127 out_backprop.reshape(rest_by_depth) * ((scratch1 * gamma) in operator()
135 out_backprop.reshape(rest_by_depth) * in operator()
Dconv_grad_filter_ops.cc104 const Tensor& out_backprop, const Tensor& input, in operator ()()
112 out_backprop.tensor<T, 4>(), row_stride, col_stride, in operator ()()
238 const Tensor& out_backprop = context->input(2); in Compute() local
253 input.shape(), filter_shape, out_backprop.shape(), in Compute()
284 filter_backprop->tensor<T, 4>(), out_backprop.tensor<T, 4>(), in Compute()
343 const T* out_backprop_data = out_backprop.template flat<T>().data(); in Compute()
492 const Tensor& out_backprop = context->input(2); in Compute() local
524 launcher_(context, use_cudnn_, cudnn_use_autotune_, out_backprop, input, in Compute()
545 const Tensor& out_backprop, const Tensor& input, int row_dilation, in operator ()() argument
566 input.shape(), filter_shape, out_backprop.shape(), dilations, in operator ()()
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Dconv_grad_input_ops.cc110 const Tensor& out_backprop, const Tensor& filter, in operator ()()
118 out_backprop.tensor<T, 4>(), row_stride, col_stride, in operator ()()
316 const Tensor& out_backprop = context->input(2); in Compute() local
330 input_shape, filter.shape(), out_backprop.shape(), in Compute()
365 out_backprop.tensor<T, 4>(), dims.spatial_dims[0].input_size, in Compute()
457 const T* out_backprop_data = out_backprop.template flat<T>().data(); in Compute()
635 const Tensor& out_backprop = context->input(2); in Compute() local
661 launcher_(context, use_cudnn_, cudnn_use_autotune_, out_backprop, filter, in Compute()
682 const Tensor& out_backprop, const Tensor& filter, int row_dilation, in operator ()() argument
705 filter_shape, out_backprop.shape(), dilations, strides, padding, in operator ()()
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Dfractional_avg_pool_op.cc240 const Tensor& out_backprop = context->input(1); in Compute() local
244 const int64 out_batch = out_backprop.dim_size(0); in Compute()
245 const int64 out_rows = out_backprop.dim_size(1); in Compute()
246 const int64 out_cols = out_backprop.dim_size(2); in Compute()
247 const int64 out_depth = out_backprop.dim_size(3); in Compute()
275 ConstEigenMatrixMap out_backprop_mat(out_backprop.flat<T>().data(), in Compute()
Dmkl_conv_ops_test.cc179 Node* out_backprop = in DefaultConv2DBwdInput() local
187 .Input(out_backprop) in DefaultConv2DBwdInput()
207 Node* out_backprop = in MklConv2DBwdInput() local
217 .Input(out_backprop) in MklConv2DBwdInput()
242 Node* out_backprop = in DefaultConv2DBwdFilter() local
250 .Input(out_backprop) in DefaultConv2DBwdFilter()
271 Node* out_backprop = in MklConv2DBwdFilter() local
281 .Input(out_backprop) in MklConv2DBwdFilter()
Dbatch_norm_op.cc102 const Tensor& out_backprop = context->input(4); in Compute() local
116 OP_REQUIRES(context, out_backprop.dims() == 4, in Compute()
118 out_backprop.shape().DebugString())); in Compute()
156 var.vec<T>(), gamma.vec<T>(), out_backprop.tensor<T, 4>(), in Compute()
243 typename TTypes<T, 4>::ConstTensor out_backprop, T variance_epsilon, \
Dconv_grad_ops.h177 const Tensor& out_backprop, const Tensor& filter,
187 const Tensor& out_backprop, const Tensor& input,
208 const Tensor& out_backprop, const Tensor& input,
Ddepthwise_conv_op.h72 const T* out_backprop, const T* filter, T* in_backprop,
79 const T* out_backprop, const T* input, T* filter_backprop,
94 const T* out_backprop, const T* filter, T* in_backprop,
101 const T* out_backprop, const T* input, T* filter_backprop,
Ddilation_ops.h43 typename TTypes<T, 4>::ConstTensor out_backprop,
57 typename TTypes<T, 4>::ConstTensor out_backprop,
Dcudnn_pooling_gpu.cc123 const Tensor& out_backprop, const TensorShape& tensor_in_shape, in Compute() argument
148 ShapeFromFormat(FORMAT_NCHW, out_backprop.shape(), data_format); in Compute()
171 transformed_output_backprop = out_backprop; in Compute()
185 context->eigen_device<GPUDevice>(), out_backprop.tensor<T, 5>(), in Compute()
Dmkl_avgpooling_op.cc246 const Tensor& out_backprop = MklGetInput(context, 1); in Compute() local
262 mkl_context.params.in_dim = out_backprop.dims(); in Compute()
289 out_backprop.flat<T>().data())), in Compute()
296 static_cast<void*>(const_cast<T*>(out_backprop.flat<T>().data())), in Compute()
302 static_cast<void*>(const_cast<T*>(out_backprop.flat<T>().data())); in Compute()
354 const Tensor& out_backprop = MklGetInput(context, 1); in MklCreateLayoutsAndPrimitives() local
368 context, out_backprop.dims() == 4, in MklCreateLayoutsAndPrimitives()
Dpooling_ops_common.cc289 const Tensor* tensor_out, const Tensor& out_backprop, in Compute() argument
326 ShapeFromFormat(FORMAT_NCHW, out_backprop.shape(), data_format); in Compute()
349 transformed_output_backprop = out_backprop; in Compute()
371 context->eigen_device<Device>(), out_backprop.tensor<T, 4>(), in Compute()
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_BiasAddGrad.pbtxt4 name: "out_backprop"
12 1-D with size the feature dimension of `out_backprop`.
29 It accumulates all the values from out_backprop into the feature dimension.
Dapi_def_FractionalAvgPoolGrad.pbtxt11 name: "out_backprop"
55 out_backprop to those indices that form the same pooling cell. Therefore, we
/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
Dpooling_ops.cc327 auto out_backprop = ctx->Input(2); in Compile() local
339 out_backprop, init_value, scatter); in Compile()
419 auto out_backprop = ctx->Input(1); in Compile() local
428 xla::ConvertElementType(out_backprop, xla_reduction_type); in Compile()
556 auto out_backprop = ctx->Input(2); in Compile() local
566 auto bp_int = xla::BitcastConvertType(out_backprop, xla::U32); in Compile()

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