| /external/llvm-project/lldb/source/Symbol/ |
| D | VariableList.cpp | 103 size_t VariableList::AppendVariablesIfUnique(VariableList &var_list) { in AppendVariablesIfUnique() argument
104 const size_t initial_size = var_list.GetSize(); in AppendVariablesIfUnique()
107 var_list.AddVariableIfUnique(*pos); in AppendVariablesIfUnique()
108 return var_list.GetSize() - initial_size; in AppendVariablesIfUnique()
112 VariableList &var_list, in AppendVariablesIfUnique() argument
114 const size_t initial_size = var_list.GetSize(); in AppendVariablesIfUnique()
121 var_list.AddVariableIfUnique(*pos); in AppendVariablesIfUnique()
125 return var_list.GetSize() - initial_size; in AppendVariablesIfUnique()
129 VariableList &var_list, in AppendVariablesWithScope() argument
131 const size_t initial_size = var_list.GetSize(); in AppendVariablesWithScope()
[all …]
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| /external/tensorflow/tensorflow/python/training/ |
| D | optimizer.py | 363 def minimize(self, loss, global_step=None, var_list=None, argument
408 loss, var_list=var_list, gate_gradients=gate_gradients,
423 def compute_gradients(self, loss, var_list=None, argument
468 if var_list is not None:
469 tape.watch(var_list)
478 if var_list is None:
479 var_list = tape.watched_variables()
483 grads = tape.gradient(loss_value, var_list, grad_loss)
484 return list(zip(grads, var_list))
503 if var_list is None:
[all …]
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| D | moving_averages.py | 403 def apply(self, var_list=None): argument
435 if var_list is None:
436 var_list = variables.trainable_variables()
437 for v in var_list:
443 for var in var_list:
488 for var in var_list:
|
| /external/tensorflow/tensorflow/python/keras/mixed_precision/ |
| D | loss_scale_benchmark.py | 98 var_list = [
102 return math_ops.add_n(var_list)
109 grads = tape.gradient(loss, var_list)
110 return opt.apply_gradients(zip(grads, var_list))
116 scaled_grads = tape.gradient(scaled_loss, var_list)
118 return opt.apply_gradients(zip(grads, var_list))
122 return opt.minimize(get_loss, var_list)
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | optimizer_v2.py | 474 def _get_gradients(self, tape, loss, var_list, grad_loss=None): argument
476 grads = tape.gradient(loss, var_list, grad_loss)
477 return list(zip(grads, var_list))
500 def minimize(self, loss, var_list, grad_loss=None, name=None, tape=None): argument
532 loss, var_list=var_list, grad_loss=grad_loss, tape=tape)
535 def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None): argument
572 if not callable(var_list):
573 tape.watch(var_list)
575 if callable(var_list):
576 var_list = var_list()
[all …]
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| D | nadam.py | 90 def _create_slots(self, var_list): argument
91 var_dtype = var_list[0].dtype.base_dtype
102 for var in var_list:
105 for var in var_list:
145 def _prepare(self, var_list): argument
148 return super(Nadam, self)._prepare(var_list)
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| D | adam.py | 123 def _create_slots(self, var_list): argument
126 for var in var_list:
128 for var in var_list:
131 for var in var_list:
375 def _create_slots(self, var_list): argument
378 for var in var_list:
380 for var in var_list:
383 for var in var_list:
|
| D | adadelta.py | 93 def _create_slots(self, var_list): argument
95 for v in var_list:
97 for v in var_list:
|
| D | adamax.py | 106 def _create_slots(self, var_list): argument
108 for var in var_list:
110 for var in var_list:
|
| D | rmsprop.py | 153 def _create_slots(self, var_list): argument
154 for var in var_list:
157 for var in var_list:
160 for var in var_list:
|
| /external/tensorflow/tensorflow/python/framework/ |
| D | meta_graph_test.py | 90 meta_graph_def, var_list = meta_graph.export_scoped_meta_graph(
99 self.assertEqual({}, var_list)
330 var_list = graph2.get_collection(ops.GraphKeys.METRIC_VARIABLES)
331 self.assertEqual(len(var_list), 1)
332 v2 = var_list[0]
403 orig_meta_graph1, var_list = meta_graph.export_scoped_meta_graph(
407 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))
408 var_names = [v.name for _, v in var_list.items()]
447 var_list = meta_graph.import_scoped_meta_graph(
453 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))
[all …]
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| D | meta_graph.py | 899 var_list = {}
903 var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v
905 return var_list, imported_return_elements
1026 var_list = {}
1031 var_list[ops.strip_name_scope(v.name, export_scope)] = v
1070 return scoped_meta_graph_def, var_list
1099 orig_meta_graph, var_list = export_scoped_meta_graph(
1101 var_list = import_scoped_meta_graph(orig_meta_graph,
1104 return var_list
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| /external/llvm-project/lldb/include/lldb/Symbol/ |
| D | VariableList.h | 50 size_t AppendVariablesIfUnique(VariableList &var_list);
58 VariableList &var_list, size_t &total_matches);
60 size_t AppendVariablesWithScope(lldb::ValueType type, VariableList &var_list,
|
| /external/tensorflow/tensorflow/python/ops/ |
| D | variables.py | 3251 def variables_initializer(var_list, name="init"): argument
3271 if var_list and not context.executing_eagerly():
3272 return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
3279 def initialize_variables(var_list, name="init"): argument
3281 return variables_initializer(var_list, name=name)
3345 def assert_variables_initialized(var_list=None): argument
3365 if var_list is None:
3366 var_list = global_variables() + local_variables()
3368 if not var_list:
3369 var_list = []
[all …]
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_optimizer.py | 111 def compute_gradients(self, loss, var_list=None, **kwargs): argument
161 return self._opt.compute_gradients(loss, var_list=var_list, **kwargs)
|
| /external/mesa3d/src/gallium/drivers/r300/compiler/ |
| D | radeon_variable.h | 83 struct rc_list * var_list,
88 struct rc_list * var_list,
|
| /external/python/uritemplates/uritemplate/ |
| D | variable.py | 87 var_list = self.original
90 var_list = self.original[1:]
95 var_list = var_list.split(',')
97 for var in var_list:
|
| /external/tensorflow/tensorflow/python/training/experimental/ |
| D | loss_scale_optimizer_test.py | 128 return lambda: opt.minimize(loss, var_list=[var])
215 run_fn = lambda: opt.minimize(loss, var_list=[var])
226 run_fn = lambda: opt.minimize(loss, var_list=[var])
249 run_fn = lambda: opt.minimize(loss, var_list=[var])
287 run_fn = lambda: opt.minimize(lambda: var + 1., var_list=[var])
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| D | loss_scale_optimizer.py | 86 var_list=None, argument
121 var_list=var_list,
|
| /external/mesa3d/src/gallium/drivers/lima/standalone/ |
| D | lima_compiler_cmdline.c | 49 insert_sorted(struct exec_list *var_list, nir_variable *new_var) in insert_sorted() argument
51 nir_foreach_variable_in_list(var, var_list) { in insert_sorted()
58 exec_list_push_tail(var_list, &new_var->node); in insert_sorted()
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.train.-optimizer.pbtxt | 28 …argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'co…
44 …argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregati…
|
| D | tensorflow.train.-gradient-descent-optimizer.pbtxt | 29 …argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'co…
45 …argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregati…
|
| D | tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt | 29 …argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'co…
45 …argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregati…
|
| D | tensorflow.train.-adadelta-optimizer.pbtxt | 29 …argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'co…
45 …argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregati…
|
| /external/tensorflow/tensorflow/python/keras/ |
| D | optimizer_v1.py | 766 def minimize(self, loss, var_list, grad_loss=None, tape=None): argument
774 if not callable(var_list):
775 tape.watch(var_list)
777 if callable(var_list):
778 var_list = var_list()
780 var_list = nest.flatten(var_list)
781 if var_list:
782 grads = tape.gradient(loss, var_list, grad_loss)
783 grads_and_vars = list(zip(grads, var_list))
|