def __init__(self, nb_filter, nb_row, nb_col,
init='glorot_uniform', activation=None, weights=None,
border_mode='valid', subsample=(1, 1), dim_ordering='default',
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, epsilon=1e-3, momentum=0.99,
beta_init='zero', gamma_init='one',
gamma_regularizer=None, beta_regularizer=None, **kwargs):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
if border_mode not in {'valid', 'same', 'full'}:
raise ValueError('Invalid border mode for Convolution2D:', border_mode)
self.nb_filter = nb_filter
self.nb_row = nb_row
self.nb_col = nb_col
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.border_mode = border_mode
self.subsample = tuple(subsample)
if dim_ordering not in {'tf', 'th'}:
raise ValueError('dim_ordering must be in {tf, th}.')
self.dim_ordering = dim_ordering
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
# added for BatchNormalization
self.supports_masking = True
self.beta_init = initializations.get(beta_init)
self.gamma_init = initializations.get(gamma_init)
self.epsilon = epsilon
self.momentum = momentum
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
self.uses_learning_phase = True
super(YOLOConvolution2D, self).__init__(**kwargs)
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