ecd414d7164613177725c81d023e6a66701de470,keras/layers/recurrent.py,LSTM,build,#LSTM#,561

Before Change

        self.regularizers = []
        for W in [self.W_i, self.W_f, self.W_c, self.W_o]:
            append_regulariser(self.W_regularizer, W, self.regularizers)
        for U in [self.U_i, self.U_f, self.U_c, self.U_o]:
            append_regulariser(self.U_regularizer, U, self.regularizers)
        for b in [self.b_i, self.b_f, self.b_c, self.b_o]:
            append_regulariser(self.b_regularizer, b, self.regularizers)

        self.trainable_weights = [self.W_i, self.U_i, self.b_i,

After Change

        self.b_o = K.zeros((self.output_dim,), name="{}_b_o".format(self.name))

        self.regularizers = []
        if self.W_regularizer:
            self.W_regularizer.set_param(K.concatenate([self.W_i,
                                                        self.W_f,
                                                        self.W_c,
                                                        self.W_o]))
            self.regularizers.append(self.W_regularizer)
        if self.U_regularizer:
            self.U_regularizer.set_param(K.concatenate([self.U_i,
                                                        self.U_f,
                                                        self.U_c,

Instances