model.py 文件源码

def _build(self,input_shape):
        data_dim = np.prod(input_shape) 
        self.gs = self.build_gs()
        self.gs2 = self.build_gs(N=data_dim)
        self.gs3 = self.build_gs(N=data_dim)

        _encoder = self.build_encoder(input_shape)
        _decoder = self.build_decoder(input_shape)

        x = Input(shape=input_shape)
        z = Sequential([flatten, *_encoder, self.gs])(x)
        y = Sequential([flatten,
                        *_decoder,
                        self.gs2,
                        Lambda(take_true),
                        Reshape(input_shape)])(z)

        z2 = Input(shape=(self.parameters['N'], self.parameters['M']))
        y2 = Sequential([flatten,
                        *_decoder,
                        self.gs3,
                        Lambda(take_true),
                        Reshape(input_shape)])(z2)

        def rec(x, y):
            return bce(K.reshape(x,(K.shape(x)[0],data_dim,)),
                       K.reshape(y,(K.shape(x)[0],data_dim,)))
        def loss(x, y):
            return rec(x,y) + self.gs.loss() + self.gs2.loss()

        self.callbacks.append(LambdaCallback(on_epoch_end=self.gs.cool))
        self.callbacks.append(LambdaCallback(on_epoch_end=self.gs2.cool))
        self.callbacks.append(LambdaCallback(on_epoch_end=self.gs3.cool))
        self.custom_log_functions['tau'] = lambda: K.get_value(self.gs.tau)
        self.loss = loss
        self.metrics.append(rec)
        self.encoder     = Model(x, z)
        self.decoder     = Model(z2, y2)
        self.net = Model(x, y)
        self.autoencoder = self.net