| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | reduction_ops_test.py | 120 def _tf_reduce(self, x, reduction_axes, keepdims): argument
123 def _np_reduce(self, x, reduction_axes, keepdims): argument
138 def _compare(self, x, reduction_axes, keepdims, feed_dict=None): argument
139 np_ans = self._np_reduce(x, reduction_axes, keepdims)
141 tf_ans = self._tf_reduce(x, reduction_axes, keepdims)
146 def _compareAll(self, x, reduction_axes, feed_dict=None): argument
147 if reduction_axes is not None and np.shape(reduction_axes) == (1,):
149 self._compareAll(x, reduction_axes[0])
150 self._compare(x, reduction_axes, keepdims=False, feed_dict=feed_dict)
151 self._compare(x, reduction_axes, keepdims=True, feed_dict=feed_dict)
[all …]
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| D | reduction_ops_test_big.py | 32 def _tf_reduce(self, x, reduction_axes, keepdims): argument
39 def _tf_reduce_max(self, x, reduction_axes, keepdims): argument
40 return math_ops.reduce_max(x, reduction_axes, keepdims)
42 def _tf_reduce_all(self, x, reduction_axes, keepdims): argument
43 return math_ops.reduce_all(x, reduction_axes, keepdims)
45 def _tf_reduce_mean(self, x, reduction_axes, keepdims): argument
46 return math_ops.reduce_mean(x, reduction_axes, keepdims)
48 def _tf_reduce_sum(self, x, reduction_axes, keepdims): argument
49 return math_ops.reduce_sum(x, reduction_axes, keepdims)
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| D | sparse_ops_test.py | 614 def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum): argument
618 if reduction_axes is None:
624 if not isinstance(reduction_axes, list): # Single scalar.
625 reduction_axes = [reduction_axes]
626 reduction_axes = np.array(reduction_axes).astype(np.int32)
628 reduction_axes = (reduction_axes + ndims) % ndims
630 reduction_axes.sort()
631 for ra in reduction_axes.ravel()[::-1]:
639 tf_dense_ans = sparse_ops.sparse_reduce_sum(sp_t, reduction_axes,
642 tf_dense_ans = sparse_ops.sparse_reduce_max(sp_t, reduction_axes,
[all …]
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | normalization_test.py | 177 reduction_axes=[-2, -1], channels_axis=-3)
185 reduction_axes=[-2, -1], channels_axis=-3)
194 normalization.group_norm(inputs, reduction_axes=[1, 5])
200 normalization.group_norm(inputs, channels_axis=-2, reduction_axes=[-2])
203 normalization.group_norm(inputs, channels_axis=1, reduction_axes=[1, 3])
206 normalization.group_norm(inputs, channels_axis=-2, reduction_axes=[2])
222 reduction_axes=[-3, -2])
229 reduction_axes=[-3, -2], groups=groups)
237 reduction_axes=[-3, -2])
263 channels_axis=-1, reduction_axes=(-3, -2),
[all …]
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| D | normalization.py | 169 reduction_axes=(-3, -2), argument
277 reduction_axes = list(reduction_axes)
278 for i in range(len(reduction_axes)):
279 if reduction_axes[i] < 0:
280 reduction_axes[i] += inputs.shape.ndims
282 for a in reduction_axes:
315 for a in reduction_axes:
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | reduction_ops.h | 46 const ReductionAxes& reduction_axes, const Reducer& reducer) { in operator()
47 out.device(d) = in.reduce(reduction_axes, reducer); in operator()
56 const ReductionAxes& reduction_axes,
60 out.device(d) = in.reduce(reduction_axes, sum_reducer) /
72 const ReductionAxes& reduction_axes,
77 (in * in.conjugate()).reduce(reduction_axes, sum_reducer).sqrt();
86 const ReductionAxes& reduction_axes,
92 .reduce(reduction_axes, sum_reducer)
133 const ReductionAxes& reduction_axes,
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| D | reduction_gpu_kernels.cu.h | 855 const ReductionAxes& reduction_axes, Op op) {
862 reduction_axes[0] == 1) { // row reduction
865 reduction_axes[0] == 0) { // column reduction
867 } else if (in_rank == 3 && out_rank == 2 && reduction_axes[0] == 1) {
870 } else if (in_rank == 3 && out_rank == 1 && reduction_axes[0] == 0 &&
871 reduction_axes[1] == 2) {
878 if (out_rank == 1) ss << " " << reduction_axes[0];
879 if (out_rank == 2) ss << " " << reduction_axes[1];
888 const ReductionAxes& reduction_axes,
896 const ReductionAxes& reduction_axes,
[all …]
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| D | sparse_reduce_op.cc | 59 std::vector<int32> reduction_axes(axes_slice.begin(), axes_slice.end()); in SparseTensorReduceHelper() local
61 for (int64 i = 0; i < reduction_axes.size(); ++i) { in SparseTensorReduceHelper()
62 reduction_axes[i] = (reduction_axes[i] + ndims) % ndims; in SparseTensorReduceHelper()
64 std::sort(reduction_axes.begin(), reduction_axes.end()); in SparseTensorReduceHelper()
74 perm.begin(), perm.end(), reduction_axes.begin(), reduction_axes.end(), in SparseTensorReduceHelper()
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| D | reduction_ops_gpu_bool.cu.cc | 40 const Eigen::array<Index, NUM_AXES>& reduction_axes, \
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| D | reduction_ops_half_mean_sum.cu.cc | 40 const Eigen::array<Index, NUM_AXES>& reduction_axes, \
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| D | reduction_ops_gpu_complex128.cu.cc | 40 const Eigen::array<Index, NUM_AXES>& reduction_axes, \
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| D | reduction_ops_gpu_complex64.cu.cc | 40 const Eigen::array<Index, NUM_AXES>& reduction_axes, \
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| D | reduction_ops_half_prod_max_min.cu.cc | 40 const Eigen::array<Index, NUM_AXES>& reduction_axes, \
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SparseReduceSumSparse.pbtxt | 23 name: "reduction_axes"
40 Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
42 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
45 If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
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| D | api_def_SparseReduceMaxSparse.pbtxt | 23 name: "reduction_axes"
40 Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
42 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
45 If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
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| D | api_def_SparseReduceMax.pbtxt | 23 name: "reduction_axes"
46 Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
48 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
51 If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
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| D | api_def_SparseReduceSum.pbtxt | 23 name: "reduction_axes"
46 Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
48 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
51 If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
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| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| D | virtual_batchnorm_impl.py | 199 reduction_axes = list(range(ndims))
200 del reduction_axes[axis]
219 self._reference_batch, reduction_axes)
247 def _virtual_statistics(self, inputs, reduction_axes): argument
249 cur_mean, cur_mean_sq = _statistics(inputs, reduction_axes)
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| D | virtual_batchnorm_test.py | 80 reduction_axes = list(range(4))
81 del reduction_axes[reduction_axis]
82 mom_mean, mom_variance = nn.moments(full_batch, reduction_axes)
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| D | batch_normalization.py | 211 reduction_axes = [i for i in range(ndims) if i not in self.batchnorm.axis]
220 reduction_axes != list(range(ndims - 1))):
261 reduction_axes = [i for i in range(len(input_shape)) if i not in event_dims]
269 _, v = nn.moments(y, axes=reduction_axes, keep_dims=True)
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
| D | batch_normalization_test.py | 111 reduction_axes = self._reduction_axes(input_shape, event_dims)
115 x_, axis=reduction_axes, keepdims=keepdims)
116 expected_batch_var = np.var(x_, axis=reduction_axes, keepdims=keepdims)
122 self.assertAllClose(np.mean(zeros, axis=reduction_axes),
123 np.mean(norm_x_, axis=reduction_axes))
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| /external/tensorflow/tensorflow/python/ops/ |
| D | sparse_ops.py | 1082 reduction_axes = None
1088 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims,
1095 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims,
1107 reduction_axes=None, keep_dims=None): argument
1167 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims)
1177 reduction_axes=None, argument
1216 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims))
1272 reduction_axes = None
1278 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims,
1284 math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims,
[all …]
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.sparse.pbtxt | 65 …argspec: "args=[\'sp_input\', \'axis\', \'keepdims\', \'reduction_axes\', \'keep_dims\'], varargs=…
69 …argspec: "args=[\'sp_input\', \'axis\', \'keepdims\', \'reduction_axes\', \'keep_dims\'], varargs=…
73 …argspec: "args=[\'sp_input\', \'axis\', \'keepdims\', \'reduction_axes\', \'keep_dims\'], varargs=…
77 …argspec: "args=[\'sp_input\', \'axis\', \'keepdims\', \'reduction_axes\', \'keep_dims\'], varargs=…
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | normalization.py | 565 def _moments(self, inputs, reduction_axes, keep_dims): argument
566 return nn.moments(inputs, reduction_axes, keep_dims=keep_dims)
597 reduction_axes = [i for i in range(ndims) if i not in self.axis]
599 del reduction_axes[1] # Do not reduce along virtual batch dim
608 reduction_axes != list(range(ndims - 1))):
641 reduction_axes,
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| /external/tensorflow/tensorflow/core/graph/ |
| D | quantize_training.cc | 478 Node* reduction_axes; in MakeEMAMinMaxVars() local
480 MakeReductionAxes(graph, name_prefix, input, &reduction_axes)); in MakeEMAMinMaxVars()
485 .Input(reduction_axes) in MakeEMAMinMaxVars()
491 .Input(reduction_axes) in MakeEMAMinMaxVars()
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