Networks.py 文件源码

def __init__(self, output_dim, 
                 init = 'glorot_uniform', inner_init = 'orthogonal',
                 activation = 'tanh', W_regularizer = None, 
                 U_regularizer = None, b_regularizer = None, 
                 dropout_W = 0.0, dropout_U = 0.0,
                 tau=100, dt=20, noise=.1,
                 dale_ratio = None, **kwargs):
        self.output_dim = output_dim
        self.init = initializations.get(init)
        self.inner_init = initializations.get(inner_init)
        self.activation = activations.get(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.tau = tau
        self.dt = dt
        self.noise = noise
        self.dale_ratio = dale_ratio
        if dale_ratio:

            #make dales law matrix
            dale_vec = np.ones(output_dim)
            dale_vec[int(dale_ratio*output_dim):] = -1
            dale = np.diag(dale_vec)
            self.Dale = K.variable(dale)
        if self.dropout_W or self.dropout_U:
            self.uses_learning_phase = True
        super(leak_recurrent, self).__init__(**kwargs)