convolutional.py 文件源码

def __init__(self, filters,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 kernel_constraint=kconstraints.NonNeg(),
                 k_initializer='zeros',
                 k_regularizer=None,
                 k_constraint=None,
                 tied_k=False,
                 activity_regularizer=None,
                 strides=1,
                 padding='valid',
                 dilation_rate=1,
                 data_format=K.image_data_format(),
                 **kwargs):
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)
        super(Conv2DSoftMinMax, self).__init__(**kwargs)

        self.filters = filters
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.k_initializer = initializers.get(k_initializer)
        self.k_regularizer = regularizers.get(k_regularizer)
        self.k_constraint = constraints.get(k_constraint)
        self.tied_k = tied_k
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
        self.padding = conv_utils.normalize_padding(padding)
        self.input_spec = InputSpec(min_ndim=2)
        self.data_format = data_format
        self.supports_masking = True