本文整理汇总了Python中tensorflow.contrib.distribute.python.combinations.combine函数的典型用法代码示例。如果您正苦于以下问题：Python combine函数的具体用法？Python combine怎么用？Python combine使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了combine函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_times_variable_arguments
def test_times_variable_arguments(self):
c1 = combinations.combine(mode=["graph", "eager"])
c2 = combinations.combine(optimizer=["adam", "gd"])
c3 = combinations.combine(distribution=["d1", "d2"])
c4 = combinations.times(c3, c1, c2)
self.assertEqual([
OrderedDict([("distribution", "d1"), ("mode", "graph"),
("optimizer", "adam")]),
OrderedDict([("distribution", "d1"), ("mode", "graph"),
("optimizer", "gd")]),
OrderedDict([("distribution", "d1"), ("mode", "eager"),
("optimizer", "adam")]),
OrderedDict([("distribution", "d1"), ("mode", "eager"),
("optimizer", "gd")]),
OrderedDict([("distribution", "d2"), ("mode", "graph"),
("optimizer", "adam")]),
OrderedDict([("distribution", "d2"), ("mode", "graph"),
("optimizer", "gd")]),
OrderedDict([("distribution", "d2"), ("mode", "eager"),
("optimizer", "adam")]),
OrderedDict([("distribution", "d2"), ("mode", "eager"),
("optimizer", "gd")])
], c4)
self.assertEqual(
combinations.combine(
mode=["graph", "eager"],
optimizer=["adam", "gd"],
distribution=["d1", "d2"]), c4)示例2: test_add
def test_add(self):
self.assertEqual(
[{
"a": 1
}, {
"a": 2
}, {
"b": 2
}, {
"b": 3
}],
combinations.combine(a=[1, 2]) +
combinations.combine(b=[2, 3]))示例3: strategy_and_input_combinations
def strategy_and_input_combinations():
return (
combinations.times(
combinations.combine(distribution=strategies_minus_tpu),
combinations.combine(mode=['graph'],
use_numpy=[True, False],
use_validation_data=[True, False])
+ combinations.combine(mode=['eager'],
use_numpy=[False],
use_validation_data=[False])) +
combinations.times(
combinations.combine(distribution=tpu_strategies),
combinations.combine(mode=['graph'],
use_numpy=[True, False],
use_validation_data=[True, False])))示例4: test_arguments_sorted
def test_arguments_sorted(self):
self.assertEqual([
OrderedDict([("aa", 1), ("ab", 2)]),
OrderedDict([("aa", 1), ("ab", 3)]),
OrderedDict([("aa", 2), ("ab", 2)]),
OrderedDict([("aa", 2), ("ab", 3)])
], combinations.combine(ab=[2, 3], aa=[1, 2]))示例5: all_combinations
def all_combinations():
return combinations.combine(
distribution=[combinations.default_strategy,
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus],
mode=["graph"])示例6: test_combinations_for_embedding_model
def test_combinations_for_embedding_model():
return (
combinations.times(
combinations.combine(distribution=
strategies_for_embedding_models()),
(graph_mode_test_configuration() +
eager_mode_test_configuration())))示例7: strategy_and_optimizer_combinations
def strategy_and_optimizer_combinations():
return combinations.combine(
distribution=strategies,
optimizer=[combinations.adagrad_optimizer_v1_fn,
combinations.adam_optimizer_v1_fn,
combinations.gradient_descent_optimizer_v1_fn,
combinations.rmsprop_optimizer_v1_fn],
mode=['graph'])示例8: test_combinations_for_stateful_embedding_model
def test_combinations_for_stateful_embedding_model():
return (
combinations.combine(
distribution=strategies_for_stateful_embedding_model(),
mode='graph',
use_numpy=False,
use_validation_data=False
))示例9: strategy_and_optimizer_combinations
def strategy_and_optimizer_combinations():
return combinations.times(
all_strategy_combinations(),
combinations.combine(
optimizer=[combinations.adagrad_optimizer_v1_fn,
combinations.adam_optimizer_v1_fn,
combinations.gradient_descent_optimizer_v1_fn,
combinations.rmsprop_optimizer_v1_fn]))示例10: test_combinations_with_tpu_strategies
def test_combinations_with_tpu_strategies():
tpu_strategies = [combinations.tpu_strategy,
combinations.tpu_strategy_one_step]
return (
combinations.times(
combinations.combine(distribution=tpu_strategies),
graph_mode_test_configuration()))示例11: test_combine_single_parameter
def test_combine_single_parameter(self):
self.assertEqual([{
"a": 1,
"b": 2
}, {
"a": 2,
"b": 2
}], combinations.combine(a=[1, 2], b=2))示例12: strategy_and_input_combinations
def strategy_and_input_combinations():
def cnn_model_with_batch_norm(**kwargs):
return _create_cnn_model(with_batch_norm=True, **kwargs)
return (
combinations.times(
combinations.combine(distribution=all_strategies),
combinations.combine(mode=['graph', 'eager'],
use_numpy=[True, False],
use_validation_data=[True, False]),
combinations.combine(model_with_data=[
ModelWithData('dnn', _create_dnn_model, _dnn_training_data),
ModelWithData('cnn', _create_cnn_model, _cnn_training_data),
ModelWithData('cnn_batch_norm',
cnn_model_with_batch_norm,
_cnn_training_data,
with_batch_norm=True),
])))示例13: strategy_minus_tpu_combinations
def strategy_minus_tpu_combinations():
return combinations.combine(
distribution=[combinations.default_strategy,
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus,
combinations.core_mirrored_strategy_with_gpu_and_cpu,
combinations.core_mirrored_strategy_with_two_gpus],
mode=['graph'])示例14: test_times
def test_times(self):
c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"])
c2 = combinations.combine(mode=["eager"], loss=["callable"])
c3 = combinations.combine(distribution=["d1", "d2"])
c4 = combinations.times(c3, c1 + c2)
self.assertEqual([
OrderedDict([("distribution", "d1"), ("loss", "callable"),
("mode", "graph")]),
OrderedDict([("distribution", "d1"), ("loss", "tensor"),
("mode", "graph")]),
OrderedDict([("distribution", "d1"), ("loss", "callable"),
("mode", "eager")]),
OrderedDict([("distribution", "d2"), ("loss", "callable"),
("mode", "graph")]),
OrderedDict([("distribution", "d2"), ("loss", "tensor"),
("mode", "graph")]),
OrderedDict([("distribution", "d2"), ("loss", "callable"),
("mode", "eager")])
], c4)示例15: all_strategy_combinations_minus_default
def all_strategy_combinations_minus_default():
strategy_minus_default_combinations = combinations.combine(
distribution=[
combinations.one_device_strategy,
combinations.mirrored_strategy_with_gpu_and_cpu,
combinations.mirrored_strategy_with_two_gpus,
combinations.core_mirrored_strategy_with_gpu_and_cpu,
combinations.core_mirrored_strategy_with_two_gpus],
mode=['graph', 'eager'])
return strategy_minus_default_combinations + tpu_strategy_combinations()