x_valid,x_train=x_train[:3000],x_train[3000:] y_valid,y_train=y_train[:3000],y_train[3000:]

These lines of code are dividing the original training set (x_train and y_train) into two new sets: x_valid and y_valid, which will be used for validation, and x_train and y_train, which will be used for training.

The first line of code is creating x_valid by taking the first 3000 samples from x_train using slicing notation. The second line of code is creating x_train by taking all the samples in x_train after the first 3000.

Similarly, the third line of code is creating y_valid by taking the first 3000 samples from y_train, and the fourth line of code is creating y_train by taking all the samples in y_train after the first 3000.

This is a common technique in machine learning called "train-validation split," where a portion of the training data is set aside for validation to tune hyperparameters and prevent overfitting.