recurrent_convolutional.py 文件源码

def __init__(self, nb_filter, nb_row, nb_col,
                 init='glorot_uniform', inner_init='orthogonal',
                 forget_bias_init='one', activation='tanh',
                 inner_activation='hard_sigmoid', dim_ordering="tf",
                 border_mode="valid", sub_sample=(1, 1),
                 W_regularizer=None, U_regularizer=None, b_regularizer=None,
                 dropout_W=0., dropout_U=0., **kwargs):
        self.nb_filter = nb_filter
        self.nb_row = nb_row
        self.nb_col = nb_col
        self.init = initializations.get(init)
        self.inner_init = initializations.get(inner_init)
        self.forget_bias_init = initializations.get(forget_bias_init)
        self.activation = activations.get(activation)
        self.inner_activation = activations.get(inner_activation)
        self.border_mode = border_mode
        self.subsample = sub_sample

        assert dim_ordering in {'tf', "th"}, 'dim_ordering must be in {tf,"th}'
        self.dim_ordering = dim_ordering

        kwargs["nb_filter"] = nb_filter
        kwargs["nb_row"] = nb_row
        kwargs["nb_col"] = nb_col
        kwargs["dim_ordering"] = dim_ordering

        self.W_regularizer = W_regularizer
        self.U_regularizer = U_regularizer
        self.b_regularizer = b_regularizer
        self.dropout_W, self.dropout_U = dropout_W, dropout_U

        super(LSTMConv2D, self).__init__(**kwargs)