Hands-On Transfer Learning with Python

Standardization

The LAB color space has values from -128 to +128. Since neural networks are sensitive to the scale of input values, we normalize the transformed pixel values from -128 to +128 and bring them within the -1 to +1 range. The same is showcased in the following code snippet:

def prep_data(file_list=[],              dir_path=None,              dim_x=256,              dim_y=256):    #Get images    X = []for filename in file_list:    X.append(img_to_array(                        sp.misc.imresize(                        load_img(                        dir_path+filename),                        (dim_x, dim_y))           )        )    X = np.array(X, dtype=np.float64)    X = 1.0/255*X    return X

Once transformed, we then split the data into train and test sets. For splitting, we utilize train_test_split utility from sklearn.