human
I am working on the file "DCGAN/DCGAN_Exercises.ipynb".
The first task is:
Here we need to do a bit of preprocessing and getting the images into a form where we can pass batches to the network. First off, we need to rescale the images to a range of -1 to 1, since the output of our generator is also in that range. We also have a set of test and validation images which could be used if we're trying to identify the numbers in the images.
Can you write Python program for it?

Write me a the Python program you requested?
assistant
#Here is the Python program you requested.

def scale(x, feature_range=(-1, 1)):
    # scale to (0, 1)
    x = ((x - x.min())/(255 - x.min()))
    
    # scale to feature_range
    min, max = feature_range
    x = x * (max - min) + min
    return x

class Dataset:
    def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None):
        split_idx = int(len(test['y'])*(1 - val_frac))
        self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:]
        self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]
        self.train_x, self.train_y = train['X'], train['y']
        
        self.train_x = np.rollaxis(self.train_x, 3)
        self.valid_x = np.rollaxis(self.valid_x, 3)
        self.test_x = np.rollaxis(self.test_x, 3)
        
        if scale_func is None:
            self.scaler = scale
        else:
            self.scaler = scale_func
        self.shuffle = shuffle
        
    def batches(self, batch_size):
        if self.shuffle:
            idx = np.arange(len(dataset.train_x))
            np.random.shuffle(idx)
            self.train_x = self.train_x[idx]
            self.train_y = self.train_y[idx]
        
        n_batches = len(self.train_y)//batch_size
        for ii in range(0, len(self.train_y), batch_size):
            x = self.train_x[ii:ii+batch_size]
            y = self.train_y[ii:ii+batch_size]
            
            yield self.scaler(x), self.scaler(y)