Deep Learning in Practice

Lately, I am following and practicing (one or two hours per day) the amazing deep learning lessons from Jeremy Howard and Rachel Thomas. Their teaching methodology is rather "top-down" than "bottom up" so from the first hour you will be able to run successfully image classifications.

Jeremy has put a nice and helpful file with useful utility functions ('utils.py')

I will share here some useful scripts that maybe you will find useful.

Lesson 1: fast-ai-utils.py 


https://gist.githubusercontent.com/alexopoulos7/272d3e7d1f57e95d53d84abf45fcccbf/raw/a556dae82766bf3debb9109ed2c57be7863b7297/fast-ai-utils.py

 # fast.ai Lesson 1  
 def create_validation_set(from_path, to_path, percentage = 0.2):  
   count_files_from_path = len(os.listdir(from_path))  
   validation_count = percentage * count_files_from_path  
   print('Lets copy {}/{} files for validation'.format(validation_count, count_files_from_path))  
   for i in range(int(validation_count)):  
     random_path = random.choice(os.listdir(from_path))  
     os.rename(from_path+random_path, to_path+random_path)  
   
   print('Create {} Validation Set finished! '.format(percentage))    
   
 # create_validation_set('/home/ec2-user/courses/deeplearning1/nbs/data/train/cat/', '/home/ec2-user/courses/deeplearning1/nbs/data/validation/cat/')  
 # create_validation_set('/home/ec2-user/courses/deeplearning1/nbs/data/train/dog/', '/home/ec2-user/courses/deeplearning1/nbs/data/validation/dog/')  
   
   
 def move_files_to_categories(from_directory):  
   """  
   It will move labeled images to respective folders.  
   e.g. file cat_1.jpg will be moved to folder cat  
   """  
   for filename in os.listdir(from_directory):  
     parts = filename.split('.')  
     if len(parts) > 2 and parts[2] in ['jpg', 'jpeg', 'png']:  
       category = from_directory + parts[0]  
       if not os.path.exists(category):  
         os.makedirs(category)  
       print('Rename from {}'.format(from_directory+filename))  
       os.rename(from_directory+filename, os.path.join(category, filename))  
       print ('Moved file to {}'.format(os.path.join(category, filename)))  
       
 # move_files_to_categories('/home/ec2-user/courses/deeplearning1/nbs/data/train/')  
 

import shutil
def create_sample_directory(from_directory, to_dir, sample_count = 1000):  
   """  
   It will create sample dir  
   """  
   for d in os.listdir(from_directory):  
        for i in range(sample_count):
            target_dir = to_dir+'validation/'+d
            if not os.path.exists(target_dir):  
                os.makedirs(target_dir)  
            random_path = random.choice(os.listdir(from_directory+d)) 
            
            #print ('Copy file from from {}'.format(os.path.join(from_directory+d, random_path)))  
            #print ('Copy file from to {}'.format(os.path.join(target_dir, random_path)))  
            shutil.copy2(os.path.join(from_directory+d, random_path), os.path.join(target_dir, random_path))
        
        for i in range(sample_count):
            target_dir = to_dir+'/train/'+d
            if not os.path.exists(target_dir):  
                os.makedirs(target_dir)  
            random_path = random.choice(os.listdir(from_directory+d)) 
            
            #print ('Copy file from from {}'.format(os.path.join(from_directory+d, random_path)))  
            #print ('Copy file from to {}'.format(os.path.join(target_dir, random_path)))  
            shutil.copy2(os.path.join(from_directory+d, random_path), os.path.join(target_dir, random_path))
       
create_sample_directory('/home/ec2-user/courses/deeplearning1/nbs/images/train/','/home/ec2-user/courses/deeplearning1/nbs/images/samples/')

  
 def show_image(image_path):  
   import matplotlib.image as mpimg    
   img=mpimg.imread(image_path)  
   imgplot = plt.imshow(img)