def __init__(self, filters_simple, filters_complex, nb_row, nb_col,
init='glorot_uniform', activation='relu', weights=None,
padding='valid', strides=(1, 1), data_format=K.image_data_format(),
kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
W_constraint=None, bias_constraint=None,
bias=True, **kwargs):
if padding not in {'valid', 'same'}:
raise Exception('Invalid border mode for Convolution2DEnergy:', padding)
self.filters_simple = filters_simple
self.filters_complex = filters_complex
self.nb_row = nb_row
self.nb_col = nb_col
self.init = initializers.get(init, data_format=data_format)
self.activation = activations.get(activation)
assert padding in {'valid', 'same'}, 'padding must be in {valid, same}'
self.padding = padding
self.strides = tuple(strides)
assert data_format in {'channels_last', 'channels_first'}, 'data_format must be in {tf, th}'
self.data_format = data_format
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.UnitNormOrthogonal(filters_complex, data_format)
self.bias_constraint = constraints.get(bias_constraint)
self.bias = bias
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(Convolution2DEnergy, self).__init__(**kwargs)
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