当前位置: 首页>>代码示例>>Python>>正文

Python conv_utils.normalize_padding方法代码示例

本文整理汇总了Python中keras.utils.conv_utils.normalize_padding方法的典型用法代码示例。如果您正苦于以下问题：Python conv_utils.normalize_padding方法的具体用法？Python conv_utils.normalize_padding怎么用？Python conv_utils.normalize_padding使用的例子？那么, 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keras.utils.conv_utils的用法示例。


在下文中一共展示了conv_utils.normalize_padding方法的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: __init__

# 需要导入模块: from keras.utils import conv_utils [as 别名]
# 或者: from keras.utils.conv_utils import normalize_padding [as 别名]
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 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,
                 spectral_normalization=True,
                 **kwargs):
        super(_ConvSN, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, '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, rank, 'dilation_rate')
        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=self.rank + 2)
        self.spectral_normalization = spectral_normalization
        self.u = None
开发者ID:emilwallner，项目名称:Coloring-greyscale-images，代码行数:40，代码来源:sn.py

示例2: test_invalid_padding

# 需要导入模块: from keras.utils import conv_utils [as 别名]
# 或者: from keras.utils.conv_utils import normalize_padding [as 别名]
def test_invalid_padding():
    with pytest.raises(ValueError):
        conv_utils.normalize_padding('diagonal')
开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:5，代码来源:conv_utils_test.py

示例3: __init__

# 需要导入模块: from keras.utils import conv_utils [as 别名]
# 或者: from keras.utils.conv_utils import normalize_padding [as 别名]
def __init__(self, ch_j, n_j,
                 kernel_size=(3, 3),
                 strides=(1, 1),
                 r_num=1,
                 b_alphas=[8, 8, 8],
                 padding='same',
                 data_format='channels_last',
                 dilation_rate=(1, 1),
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):
        super(Conv2DCaps, self).__init__(**kwargs)
        rank = 2
        self.ch_j = ch_j  # Number of capsules in layer J
        self.n_j = n_j  # Number of neurons in a capsule in J
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.r_num = r_num
        self.b_alphas = b_alphas
        self.padding = conv_utils.normalize_padding(padding)
        #self.data_format = conv_utils.normalize_data_format(data_format)
        self.data_format = K.normalize_data_format(data_format)
        self.dilation_rate = (1, 1)
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.input_spec = InputSpec(ndim=rank + 3)
开发者ID:brjathu，项目名称:deepcaps，代码行数:34，代码来源:capslayers.py

示例4: __init__

# 需要导入模块: from keras.utils import conv_utils [as 别名]
# 或者: from keras.utils.conv_utils import normalize_padding [as 别名]
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 normalize_weight=False,
                 kernel_initializer='complex',
                 bias_initializer='zeros',
                 gamma_diag_initializer=sqrt_init,
                 gamma_off_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 gamma_diag_regularizer=None,
                 gamma_off_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 gamma_diag_constraint=None,
                 gamma_off_constraint=None,
                 init_criterion='he',
                 seed=None,
                 spectral_parametrization=False,
                 epsilon=1e-7,
                 **kwargs):
        super(ComplexConv, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = 'channels_last' if rank == 1 else conv_utils.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.normalize_weight = normalize_weight
        self.init_criterion = init_criterion
        self.spectral_parametrization = spectral_parametrization
        self.epsilon = epsilon
        self.kernel_initializer = sanitizedInitGet(kernel_initializer)
        self.bias_initializer = sanitizedInitGet(bias_initializer)
        self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
        self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
        self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
        self.gamma_off_constraint = constraints.get(gamma_off_constraint)
        if seed is None:
            self.seed = np.random.randint(1, 10e6)
        else:
            self.seed = seed
        self.input_spec = InputSpec(ndim=self.rank + 2)
开发者ID:ChihebTrabelsi，项目名称:deep_complex_networks，代码行数:62，代码来源:conv.py

示例5: __init__

# 需要导入模块: from keras.utils import conv_utils [as 别名]
# 或者: from keras.utils.conv_utils import normalize_padding [as 别名]
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format='channels_last',
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 normalize_weight=False,
                 kernel_initializer='quaternion',
                 bias_initializer='zeros',
                 gamma_diag_initializer=sqrt_init,
                 gamma_off_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 gamma_diag_regularizer=None,
                 gamma_off_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 gamma_diag_constraint=None,
                 gamma_off_constraint=None,
                 init_criterion='he',
                 seed=None,
                 spectral_parametrization=False,
                 epsilon=1e-7,
                 **kwargs):
        super(QuaternionConv, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = K.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.normalize_weight = normalize_weight
        self.init_criterion = init_criterion
        self.spectral_parametrization = spectral_parametrization
        self.epsilon = epsilon
        self.kernel_initializer = sanitizedInitGet(kernel_initializer)
        self.bias_initializer = sanitizedInitGet(bias_initializer)
        self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
        self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
        self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
        self.gamma_off_constraint = constraints.get(gamma_off_constraint)
        if seed is None:
            self.seed = np.random.randint(1, 10e6)
        else:
            self.seed = seed
        self.input_spec = InputSpec(ndim=self.rank + 2)
开发者ID:Orkis-Research，项目名称:Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition，代码行数:62，代码来源:conv.py


注：本文中的keras.utils.conv_utils.normalize_padding方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。