| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | scatter_nd_ops_test.py | 54 def _NumpyScatterNd(ref, indices, updates, op): argument
62 flat_updates = updates.reshape((num_updates, slice_size))
71 def _NumpyUpdate(ref, indices, updates): argument
72 return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
75 def _NumpyAdd(ref, indices, updates): argument
76 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p + u)
79 def _NumpySub(ref, indices, updates): argument
80 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p - u)
83 def _NumpyMul(ref, indices, updates): argument
84 return _NumpyScatterNd(ref, indices, updates, lambda p, u: p * u)
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| D | scatter_ops_test.py | 34 def _NumpyAdd(ref, indices, updates): argument
38 ref[indx] += updates[i]
41 def _NumpySub(ref, indices, updates): argument
43 ref[indx] -= updates[i]
46 def _NumpyMul(ref, indices, updates): argument
48 ref[indx] *= updates[i]
51 def _NumpyDiv(ref, indices, updates): argument
53 ref[indx] /= updates[i]
56 def _NumpyUpdate(ref, indices, updates): argument
58 ref[indx] = updates[i]
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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/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/tensorflow/tensorflow/core/kernels/ |
| D | scatter_functor_gpu.cu.h | 33 __global__ void ScatterOpCustomKernel(T* params, const T* updates, in ScatterOpCustomKernel() argument
49 params[params_i] = ldg(updates + updates_i); in ScatterOpCustomKernel()
53 CudaAtomicAdd(params + params_i, ldg(updates + updates_i)); in ScatterOpCustomKernel()
57 CudaAtomicSub(params + params_i, ldg(updates + updates_i)); in ScatterOpCustomKernel()
61 CudaAtomicMul(params + params_i, ldg(updates + updates_i)); in ScatterOpCustomKernel()
65 CudaAtomicDiv(params + params_i, ldg(updates + updates_i)); in ScatterOpCustomKernel()
78 typename TTypes<T>::ConstMatrix updates,
85 const Index updates_size = updates.size();
89 params.data(), updates.data(), indices.data(), first_dim_size,
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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() != indices.dims() + params.dims() - 1) return false; in ValidShapes()
43 if (updates.dim_size(d) != indices.dim_size(d)) { in ValidShapes()
48 if (params.dim_size(d) != updates.dim_size(d - 1 + indices.dims())) { in ValidShapes()
56 const Tensor& indices, const Tensor& updates) { in DoValidationChecking() argument
63 c, ValidShapes(params, updates, indices), in DoValidationChecking()
66 "updates.shape ", updates.shape().DebugString(), ", indices.shape ", in DoValidationChecking()
99 const Tensor& updates = c->input(2); in DoCompute() local
100 DoValidationChecking(c, params, indices, updates); in DoCompute()
125 auto updates_flat = updates.shaped<T, 2>({N, updates.NumElements() / N}); in DoCompute()
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| D | scatter_nd_op.cc | 62 const Tensor& updates = c->input(1); in Compute() local
76 c, indices, updates, shape, &out, true /*allocate*/)); in Compute()
128 const Tensor& updates = c->input(2); in DoCompute() local
170 c, indices, updates, params_shape, ¶ms, false /*allocate*/)); in DoCompute()
295 const Tensor& indices, const Tensor& updates) { in ValidateUpdateShape() argument
304 updates.shape().DebugString(), in ValidateUpdateShape()
310 if (updates.dims() < batch_dim) return shape_err(); in ValidateUpdateShape()
311 if (params_shape.dims() < slice_dim + (updates.dims() - batch_dim)) { in ValidateUpdateShape()
314 if (updates.dims() != batch_dim + params_shape.dims() - slice_dim) { in ValidateUpdateShape()
318 if (updates.dim_size(d) != indices.dim_size(d)) return shape_err(); in ValidateUpdateShape()
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| D | scatter_functor.h | 130 typename TTypes<T>::ConstMatrix updates,
138 typename TTypes<T>::ConstMatrix updates,
151 updates.template chip<0>(i));
162 typename TTypes<T>::ConstMatrix updates,
175 d, params.template chip<0>(index), updates.template chip<0>(i));
186 typename TTypes<T>::ConstMatrix updates,
199 updates.data() + i * updates.dimension(1),
200 updates.dimension(1) * sizeof(T));
211 params.template chip<0>(index), updates.template chip<0>(i));
227 typename TTypes<T>::ConstMatrix updates,
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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/_impl/keras/ |
| D | optimizers.py | 91 self.updates = []
181 self.updates = [K.update_add(self.iterations, 1)]
194 self.updates.append(K.update(m, v))
205 self.updates.append(K.update(p, new_p))
206 return self.updates
254 self.updates = [K.update_add(self.iterations, 1)]
265 self.updates.append(K.update(a, new_a))
272 self.updates.append(K.update(p, new_p))
273 return self.updates
316 self.updates = [K.update_add(self.iterations, 1)]
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| 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:
57 updates = tf.constant([9, 10, 11, 12])
58 output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
66 See @{tf.scatter_nd} for more details about how to make updates to slices.
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| D | api_def_ScatterNd.pbtxt | 10 name: "updates"
24 A new tensor with the given shape and updates applied according
28 summary: "Scatter `updates` into a new (initially zero) tensor according to `indices`."
30 Creates a new tensor by applying sparse `updates` to individual
35 **WARNING**: The order in which updates are applied is nondeterministic, so the
46 `shape`. `updates` is a tensor with shape
62 updates = tf.constant([9, 10, 11, 12])
64 scatter = tf.scatter_nd(indices, updates, shape)
85 updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
90 scatter = tf.scatter_nd(indices, 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:]`.
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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:]`.
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| D | api_def_ResourceScatterUpdate.pbtxt | 16 name: "updates"
21 summary: "Assigns 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, ...]
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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:]`.
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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:]`.
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| D | api_def_ScatterAdd.pbtxt | 16 name: "updates"
35 summary: "Adds sparse updates to a variable reference."
40 ref[indices, ...] += updates[...]
43 ref[indices[i], ...] += updates[i, ...]
46 ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
54 Requires `updates.shape = indices.shape + ref.shape[1:]`.
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| D | api_def_ScatterSub.pbtxt | 16 name: "updates"
35 summary: "Subtracts sparse updates to a variable reference."
39 ref[indices, ...] -= updates[...]
42 ref[indices[i], ...] -= updates[i, ...]
45 ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
54 Requires `updates.shape = indices.shape + ref.shape[1:]`.
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| D | api_def_ResourceScatterNdUpdate.pbtxt | 17 name: "updates"
31 summary: "Applies sparse `updates` to individual values or slices within a given"
44 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
56 updates = tf.constant([9, 10, 11, 12])
57 update = tf.scatter_nd_update(ref, indices, updates)
66 See @{tf.scatter_nd} for more details about how to make updates to
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| D | api_def_ScatterNdSub.pbtxt | 17 name: "updates"
38 summary: "Applies sparse subtraction between `updates` and individual values or slices"
51 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
62 updates = tf.constant([9, 10, 11, 12])
63 sub = tf.scatter_nd_sub(ref, indices, updates)
71 See @{tf.scatter_nd} for more details about how to make updates to
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| D | api_def_ScatterNdAdd.pbtxt | 17 name: "updates"
38 summary: "Applies sparse addition between `updates` and individual values or slices"
51 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
62 updates = tf.constant([9, 10, 11, 12])
63 add = tf.scatter_nd_add(ref, indices, updates)
71 See @{tf.scatter_nd} for more details about how to make updates to
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| D | api_def_ScatterNdUpdate.pbtxt | 17 name: "updates"
38 summary: "Applies sparse `updates` to individual values or slices within a given"
51 `updates` is `Tensor` of rank `Q-1+P-K` with shape:
63 updates = tf.constant([9, 10, 11, 12])
64 update = tf.scatter_nd_update(ref, indices, updates)
73 See @{tf.scatter_nd} for more details about how to make updates to
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| /external/tensorflow/tensorflow/python/ops/ |
| D | state_ops.py | 308 def scatter_update(ref, indices, updates, use_locking=True, name=None): argument
354 return gen_state_ops.scatter_update(ref, indices, updates,
357 ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
362 def scatter_nd_update(ref, indices, updates, use_locking=True, name=None): argument
418 ref, indices, updates, use_locking, name)
420 ref.handle, indices, ops.convert_to_tensor(updates, dtype=ref.dtype),
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| /external/tensorflow/tensorflow/compiler/tf2xla/lib/ |
| D | scatter.cc | 35 const xla::ComputationDataHandle& updates, in XlaScatter() argument
44 builder->GetShape(updates)); in XlaScatter()
106 auto flat_updates = builder->Reshape(updates, flat_updates_shape); in XlaScatter()
118 auto updates = loop_vars[1]; in XlaScatter() local
159 auto update = body_builder->DynamicSlice(updates, updates_offset, in XlaScatter()
183 return std::vector<xla::ComputationDataHandle>{indices, updates, buffer}; in XlaScatter()
|