DEC.py 文件源码

def pretrain(self, x, y=None, optimizer='adam', epochs=200, batch_size=256, save_dir='results/temp'):
        print('...Pretraining...')
        self.autoencoder.compile(optimizer=optimizer, loss='mse')

        csv_logger = callbacks.CSVLogger(save_dir + '/pretrain_log.csv')
        cb = [csv_logger]
        if y is not None:
            class PrintACC(callbacks.Callback):
                def __init__(self, x, y):
                    self.x = x
                    self.y = y
                    super(PrintACC, self).__init__()

                def on_epoch_end(self, epoch, logs=None):
                    if epoch % int(epochs/10) != 0:
                        return
                    feature_model = Model(self.model.input,
                                          self.model.get_layer(
                                              'encoder_%d' % (int(len(self.model.layers) / 2) - 1)).output)
                    features = feature_model.predict(self.x)
                    km = KMeans(n_clusters=len(np.unique(self.y)), n_init=20, n_jobs=4)
                    y_pred = km.fit_predict(features)
                    # print()
                    print(' '*8 + '|==>  acc: %.4f,  nmi: %.4f  <==|'
                          % (metrics.acc(self.y, y_pred), metrics.nmi(self.y, y_pred)))

            cb.append(PrintACC(x, y))

        # begin pretraining
        t0 = time()
        self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs, callbacks=cb)
        print('Pretraining time: ', time() - t0)
        self.autoencoder.save_weights(save_dir + '/ae_weights.h5')
        print('Pretrained weights are saved to %s/ae_weights.h5' % save_dir)
        self.pretrained = True