| /external/tensorflow/tensorflow/compiler/xla/tests/ |
| D | scatter_test.cc | 31 Literal* scatter_indices, Literal* updates) { in RunTest() argument
32 RunTest(hlo_text, {operand, scatter_indices, updates}); in RunTest()
68 Literal updates = LiteralUtil::CreateR2<int32>({{10, 20, 30}, {70, 80, 90}}); in XLA_TEST_F() local
69 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
99 Literal updates = LiteralUtil::CreateR2<int32>({{10, 20, 30}, {70, 80, 90}}); in XLA_TEST_F() local
100 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
127 Literal updates = in XLA_TEST_F() local
129 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
193 Literal updates = in XLA_TEST_F() local
196 RunTest(hlo_text, &operand, &scatter_indices, &updates); in XLA_TEST_F()
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | scatter_nd_ops_test.py | 62 def _NumpyScatterNd(ref, indices, updates, op): argument
70 flat_updates = updates.reshape((num_updates, slice_size))
79 def _NumpyUpdate(ref, indices, updates): argument
80 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
83 def _NumpyAdd(ref, indices, updates): argument
84 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p + u)
87 def _NumpySub(ref, indices, updates): argument
88 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p - u)
91 def _NumpyMul(ref, indices, updates): argument
92 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p * u)
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| D | scatter_ops_test.py | 35 def _NumpyAdd(ref, indices, updates): argument
39 ref[indx] += updates[i]
47 def _NumpySub(ref, indices, updates): argument
49 ref[indx] -= updates[i]
57 def _NumpyMul(ref, indices, updates): argument
59 ref[indx] *= updates[i]
67 def _NumpyDiv(ref, indices, updates): argument
69 ref[indx] /= updates[i]
77 def _NumpyMin(ref, indices, updates): argument
79 ref[indx] = np.minimum(ref[indx], updates[i])
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| D | batch_scatter_ops_test.py | 36 def _NumpyUpdate(ref, indices, updates): argument
39 ref[indx] = updates[i]
64 updates = _AsType(
72 np_scatter(new, indices, updates)
77 ref.batch_scatter_update(ops.IndexedSlices(indices, updates))
79 tf_scatter(ref, indices, updates).eval()
107 updates = np.array([-3, -4, -5]).astype(np.float32)
114 state_ops.batch_scatter_update(ref, indices, updates).eval()
120 state_ops.batch_scatter_update(ref, indices, updates).eval()
125 state_ops.batch_scatter_update(ref, indices, updates).eval()
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | scatter_nd_op_test.py | 49 def _NumpyScatterNd(ref, indices, updates, op): argument
57 flat_updates = updates.reshape((num_updates, slice_size))
66 def _NumpyUpdate(indices, updates, shape): argument
67 ref = np.zeros(shape, dtype=updates.dtype)
68 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
107 updates = _AsType(np.random.randn(*(updates_shape)), vtype)
110 np_out = np_scatter(indices, updates, ref_shape)
112 tf_out = tf_scatter(indices, updates, ref_shape)
121 def _runScatterNd(self, indices, updates, shape): argument
123 updates_placeholder = array_ops.placeholder(updates.dtype)
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| /external/tensorflow/tensorflow/python/ops/ |
| D | state_ops.py | 252 def scatter_update(ref, indices, updates, use_locking=True, name=None): argument
298 return gen_state_ops.scatter_update(ref, indices, updates,
301 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
306 def scatter_nd_update(ref, indices, updates, use_locking=True, name=None): argument
361 ref, indices, updates, use_locking, name)
363 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
368 def scatter_add(ref, indices, updates, use_locking=False, name=None): argument
412 return gen_state_ops.scatter_add(ref, indices, updates,
415 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
420 def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): argument
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | scatter_nd_op.cc | 76 const Tensor& updates = c->input(1); in Compute() local
83 OP_REQUIRES(c, updates.shape().dims() >= 1, in Compute()
86 updates.shape().DebugString())); in Compute()
96 updates.shape().num_elements()), in Compute()
103 OP_REQUIRES(c, indices.shape().dim_size(i) == updates.shape().dim_size(i), in Compute()
108 ", updates shape:", updates.shape().DebugString())); in Compute()
113 c, updates.shape().dims() - outer_dims == shape.dims() - ix, in Compute()
117 " updates: ", updates.shape().DebugString())); in Compute()
118 for (int i = 0; i + outer_dims < updates.shape().dims(); ++i) { in Compute()
120 c, updates.shape().dim_size(i + outer_dims) == shape.dim_size(ix + i), in Compute()
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| D | scatter_op.cc | 39 static bool ValidShapes(const Tensor& params, const Tensor& updates, in ValidShapes() argument
41 if (updates.dims() == 0) return true; in ValidShapes()
42 if (updates.dims() != indices.dims() + params.dims() - 1) return false; in ValidShapes()
44 if (updates.dim_size(d) != indices.dim_size(d)) { in ValidShapes()
49 if (params.dim_size(d) != updates.dim_size(d - 1 + indices.dims())) { in ValidShapes()
57 const Tensor& indices, const Tensor& updates) { in DoValidationChecking() argument
64 c, ValidShapes(params, updates, indices), in DoValidationChecking()
67 "updates.shape ", updates.shape().DebugString(), in DoValidationChecking()
100 const Tensor& updates = c->input(2); in DoCompute() local
101 DoValidationChecking(c, params, indices, updates); in DoCompute()
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| /external/tensorflow/tensorflow/contrib/tensor_forest/python/kernel_tests/ |
| D | scatter_add_ndim_op_test.py | 33 updates = [100., 200.]
37 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run()
46 updates = [100., 200.]
50 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run()
58 updates = []
62 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run()
69 updates = [100.]
74 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run()
81 updates = [[100., 200., 300.], [400., 500., 600.]]
85 tensor_forest_ops.scatter_add_ndim(input_data, indices, updates).run()
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| /external/mesa3d/docs/relnotes/ |
| D | 6.4 | 36 Glide (3dfx Voodoo1/2) requires updates
37 SVGA requires updates
38 DJGPP requires updates
39 GGI requires updates
40 BeOS requires updates
41 Allegro requires updates
42 D3D requires updates
44 The drivers which require updates mostly need to be updated to work
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| /external/tensorflow/tensorflow/python/keras/ |
| D | optimizers.py | 72 self.updates = []
187 self.updates = [state_ops.assign_add(self.iterations, 1)]
200 self.updates.append(state_ops.assign(m, v))
211 self.updates.append(state_ops.assign(p, new_p))
212 return self.updates
259 self.updates = [state_ops.assign_add(self.iterations, 1)]
270 self.updates.append(state_ops.assign(a, new_a))
277 self.updates.append(state_ops.assign(p, new_p))
278 return self.updates
327 self.updates = [state_ops.assign_add(self.iterations, 1)]
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_ResourceScatterSub.pbtxt | 16 name: "updates"
21 summary: "Subtracts sparse updates from the variable referenced by `resource`."
26 ref[indices, ...] -= updates[...]
29 ref[indices[i], ...] -= updates[i, ...]
32 ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| D | api_def_ResourceScatterDiv.pbtxt | 16 name: "updates"
21 summary: "Divides sparse updates into the variable referenced by `resource`."
26 ref[indices, ...] /= updates[...]
29 ref[indices[i], ...] /= updates[i, ...]
32 ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| D | api_def_ResourceScatterAdd.pbtxt | 16 name: "updates"
21 summary: "Adds sparse updates to the variable referenced by `resource`."
26 ref[indices, ...] += updates[...]
29 ref[indices[i], ...] += updates[i, ...]
32 ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterMul.pbtxt | 16 name: "updates"
21 summary: "Multiplies sparse updates into the variable referenced by `resource`."
26 ref[indices, ...] *= updates[...]
29 ref[indices[i], ...] *= updates[i, ...]
32 ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_TensorScatterUpdate.pbtxt | 16 name: "updates"
24 A new tensor with the given shape and updates applied according
28 summary: "Scatter `updates` into an existing tensor according to `indices`."
30 This operation creates a new tensor by applying sparse `updates` to the passed
32 This operation is very similar to `tf.scatter_nd`, except that the updates are
36 If `indices` contains duplicates, then their updates are accumulated (summed).
38 **WARNING**: The order in which updates are applied is nondeterministic, so the
51 `shape`. `updates` is a tensor with shape
67 updates = tf.constant([9, 10, 11, 12])
69 updated = tf.tensor_scatter_update(tensor, indices, updates)
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| D | api_def_ResourceScatterMin.pbtxt | 16 name: "updates"
21 …summary: "Reduces sparse updates into the variable referenced by `resource` using the `min` operat…
26 ref[indices, ...] = min(ref[indices, ...], updates[...])
29 ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
32 ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
|
| D | api_def_ResourceScatterMax.pbtxt | 16 name: "updates"
21 …summary: "Reduces sparse updates into the variable referenced by `resource` using the `max` operat…
26 ref[indices, ...] = max(ref[indices, ...], updates[...])
29 ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
32 ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
37 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| D | api_def_ScatterNdNonAliasingAdd.pbtxt | 17 name: "updates"
27 updated with `updates`.
32 from `updates` according to indices `indices`. The updates are non-aliasing:
35 respect to both `input` and `updates`.
46 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
55 updates = tf.constant([9, 10, 11, 12])
56 output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
64 See `tf.scatter_nd` for more details about how to make updates to slices.
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| D | api_def_TensorScatterAdd.pbtxt | 16 name: "updates"
24 A new tensor copied from tensor and updates added according to the indices.
27 summary: "Adds sparse `updates` to an existing tensor according to `indices`."
29 This operation creates a new tensor by adding sparse `updates` to the passed
31 This operation is very similar to `tf.scatter_nd_add`, except that the updates
43 `shape`. `updates` is a tensor with shape
55 updates = tf.constant([9, 10, 11, 12])
57 updated = tf.tensor_scatter_add(tensor, indices, updates)
74 updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
79 updated = tf.tensor_scatter_add(tensor, indices, updates)
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| D | api_def_TensorScatterSub.pbtxt | 16 name: "updates"
24 A new tensor copied from tensor and updates subtracted according to the indices.
27 summary: "Subtracts sparse `updates` from an existing tensor according to `indices`."
29 This operation creates a new tensor by subtracting sparse `updates` from the
31 This operation is very similar to `tf.scatter_nd_sub`, except that the updates
43 `shape`. `updates` is a tensor with shape
55 updates = tf.constant([9, 10, 11, 12])
57 updated = tf.tensor_scatter_sub(tensor, indices, updates)
74 updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
79 updated = tf.tensor_scatter_sub(tensor, indices, updates)
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| D | api_def_ScatterUpdate.pbtxt | 16 name: "updates"
35 summary: "Applies sparse updates to a variable reference."
41 ref[indices, ...] = updates[...]
44 ref[indices[i], ...] = updates[i, ...]
47 ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
54 duplicate entries in `indices`, the order at which the updates happen
57 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| D | api_def_ScatterMul.pbtxt | 16 name: "updates"
35 summary: "Multiplies sparse updates into a variable reference."
41 ref[indices, ...] *= updates[...]
44 ref[indices[i], ...] *= updates[i, ...]
47 ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
56 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| D | api_def_ScatterDiv.pbtxt | 16 name: "updates"
35 summary: "Divides a variable reference by sparse updates."
41 ref[indices, ...] /= updates[...]
44 ref[indices[i], ...] /= updates[i, ...]
47 ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
56 Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | scatter_expander.cc | 95 HloInstruction* updates, absl::Span<const int64> update_window_dims) { in PermuteScatterAndWindowDims() argument
97 const int64 updates_rank = updates->shape().rank(); in PermuteScatterAndWindowDims()
110 return MakeTransposeHlo(updates, permutation); in PermuteScatterAndWindowDims()
115 const Shape& scatter_indices_shape, HloInstruction* updates, in AdjustScatterDims() argument
125 return PrependDegenerateDims(updates, 1); in AdjustScatterDims()
127 return CollapseFirstNDims(updates, num_scatter_dims); in AdjustScatterDims()
227 HloInstruction* updates = loop_state[2]; in ScatterLoopBody() local
267 /*zeros_to_append=*/updates->shape().dimensions_size() - 1)); in ScatterLoopBody()
268 std::vector<int64> update_slice_bounds(updates->shape().dimensions().begin(), in ScatterLoopBody()
269 updates->shape().dimensions().end()); in ScatterLoopBody()
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