rhn.py 文件源码

def __init__(self, output_dim, L,
             init='glorot_uniform', inner_init='orthogonal',
             activation='tanh', inner_activation='hard_sigmoid',
             W_regularizer=None, U_regularizer=None, b_regularizer=None,
             dropout_W=0., dropout_U=0., **kwargs):
    self.output_dim = output_dim
    self.init = initializations.get(init)
    self.inner_init = initializations.get(inner_init)
    self.activation = activations.get(activation)
    self.inner_activation = activations.get(inner_activation)
    self.W_regularizer = regularizers.get(W_regularizer)
    self.U_regularizer = regularizers.get(U_regularizer)
    self.b_regularizer = regularizers.get(b_regularizer)
    self.dropout_W, self.dropout_U = dropout_W, dropout_U
    self.L = L

    if self.dropout_W or self.dropout_U:
        self.uses_learning_phase = True
    super(RHN, self).__init__(**kwargs)