Why is evaluation done on the training set

[fulcus]

If I understand correctly, the model is evaluated on the same data that it's trained on. Doesn't this lead to a wrong evaluation?

Load data

DEC-keras/DEC.py

Line 290 in 2438070

x, y = load_data(args.dataset)

DEC-keras/datasets.py

Lines 94 to 103 in 2438070

	def load_mnist():
	# the data, shuffled and split between train and test sets
	from keras.datasets import mnist
	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	x = np.concatenate((x_train, x_test))
	y = np.concatenate((y_train, y_test))
	x = x.reshape((x.shape[0], -1))
	x = np.divide(x, 255.)
	print('MNIST samples', x.shape)
	return x, y

Evaluate

DEC-keras/DEC.py

Lines 333 to 335 in 2438070

	y_pred = dec.fit(x, y=y, tol=args.tol, maxiter=args.maxiter, batch_size=args.batch_size,
	update_interval=update_interval, save_dir=args.save_dir)
	print('acc:', metrics.acc(y, y_pred))

Shouldn't x_train and y_trained used to pretrain and fit, and then x_test and y_test used to evaluate the model?