本文整理汇总了Python中tensorflow.python.keras.utils.conv_utils.normalize_data_format函数的典型用法代码示例。如果您正苦于以下问题：Python normalize_data_format函数的具体用法？Python normalize_data_format怎么用？Python normalize_data_format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normalize_data_format函数的5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_normalize_data_format
def test_normalize_data_format(self):
self.assertEqual('channels_last',
conv_utils.normalize_data_format('Channels_Last'))
self.assertEqual('channels_first',
conv_utils.normalize_data_format('CHANNELS_FIRST'))
with self.assertRaises(ValueError):
conv_utils.normalize_data_format('invalid')示例2: __init__
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(LocallyConnected1D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
if self.padding != 'valid':
raise ValueError('Invalid border mode for LocallyConnected1D '
'(only "valid" is supported): ' + padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=3)示例3: __init__
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(ConvLSTM2DCell, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2,
'dilation_rate')
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.state_size = (self.filters, self.filters)
self._dropout_mask = None
self._recurrent_dropout_mask = None示例4: __init__
def __init__(self, pool_function, pool_size, strides,
padding='valid', data_format='channels_last',
name=None, **kwargs):
super(Pooling1D, self).__init__(name=name, **kwargs)
if data_format is None:
data_format = backend.image_data_format()
if strides is None:
strides = pool_size
self.pool_function = pool_function
self.pool_size = conv_utils.normalize_tuple(pool_size, 1, 'pool_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=3)示例5: __init__
def __init__(self, data_format=None, **kwargs):
super(Flatten, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.input_spec = InputSpec(min_ndim=2)