本文整理汇总了Python中nets.nasnet.pnasnet.mobile_imagenet_config方法的典型用法代码示例。如果您正苦于以下问题：Python pnasnet.mobile_imagenet_config方法的具体用法？Python pnasnet.mobile_imagenet_config怎么用？Python pnasnet.mobile_imagenet_config使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nets.nasnet.pnasnet的用法示例。
在下文中一共展示了pnasnet.mobile_imagenet_config方法的6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: testNoAuxHeadMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testNoAuxHeadMobileModel(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
for use_aux_head in (True, False):
tf.reset_default_graph()
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.mobile_imagenet_config()
config.set_hparam('use_aux_head', int(use_aux_head))
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, end_points = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
self.assertEqual('AuxLogits' in end_points, use_aux_head) 示例2: testOverrideHParamsMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testOverrideHParamsMobileModel(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.mobile_imagenet_config()
config.set_hparam('data_format', 'NCHW')
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, end_points = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
self.assertListEqual(end_points['Stem'].shape.as_list(),
[batch_size, 135, 28, 28]) 示例3: testUseBoundedAcitvationMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testUseBoundedAcitvationMobileModel(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
for use_bounded_activation in (True, False):
tf.reset_default_graph()
inputs = tf.random_uniform((batch_size, height, width, 3))
config = pnasnet.mobile_imagenet_config()
config.set_hparam('use_bounded_activation', use_bounded_activation)
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, _ = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
for node in tf.get_default_graph().as_graph_def().node:
if node.op.startswith('Relu'):
self.assertEqual(node.op == 'Relu6', use_bounded_activation) 示例4: testNoAuxHeadMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testNoAuxHeadMobileModel(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
for use_aux_head in (True, False):
tf.reset_default_graph()
inputs = tf.random.uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.mobile_imagenet_config()
config.set_hparam('use_aux_head', int(use_aux_head))
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, end_points = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
self.assertEqual('AuxLogits' in end_points, use_aux_head) 示例5: testOverrideHParamsMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testOverrideHParamsMobileModel(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random.uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.mobile_imagenet_config()
config.set_hparam('data_format', 'NCHW')
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, end_points = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
self.assertListEqual(end_points['Stem'].shape.as_list(),
[batch_size, 135, 28, 28]) 示例6: testUseBoundedAcitvationMobileModel
# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import mobile_imagenet_config [as 别名]
def testUseBoundedAcitvationMobileModel(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
for use_bounded_activation in (True, False):
tf.reset_default_graph()
inputs = tf.random.uniform((batch_size, height, width, 3))
config = pnasnet.mobile_imagenet_config()
config.set_hparam('use_bounded_activation', use_bounded_activation)
with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
_, _ = pnasnet.build_pnasnet_mobile(
inputs, num_classes, config=config)
for node in tf.get_default_graph().as_graph_def().node:
if node.op.startswith('Relu'):
self.assertEqual(node.op == 'Relu6', use_bounded_activation)