Deep Learning

Assignment 1

The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.

This notebook uses the notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.



In [1]:

    
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.

# General variables
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
import pickle

# iPython Packages
from IPython.display import display, Image

# ML Packages
from scipy import ndimage
from sklearn.linear_model import LogisticRegression

# Download Packages
from urllib.request import urlretrieve

# Configuration
# Config the matplotlib backend as plotting inline in IPython
%matplotlib inline

First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine.



In [ ]:

    
# Seed the numpy randomizer early on so we always get 
# our 'random' values back
np.random.seed(42)



In [2]:

    
# What directory are you working from? Let's remember that
maindir = os.getcwd()
print(maindir)









    



/Users/vivek.menon/Dropbox/learn/books/Udacity/UD730 - Deep Learning



In [23]:

    
# Some folder walking tools

def root_folder():
    os.chdir(maindir)
    
def current_folder():
    print('Current folder: {}'.format(os.getcwd().split('/')[-1]))
    return os.getcwd()
    
def data_folder():
    root_folder()
    
    if not os.path.exists('data'):
    # Make a 'data' folder to hold these datasets if doesn't exist yet
        print('Creating data folder')
        os.makedirs('data')
    else:
        os.chdir('data')
    
    

current_folder()
data_folder()
current_folder()
root_folder()









    



Current folder: UD730 - Deep Learning
Current folder: data



In [24]:

    
url = 'http://commondatastorage.googleapis.com/books1000/'
# Where are we going? 

last_percent_reported = None
# Assume we've not downloaded anything

def download_progress_hook(count, blockSize, totalSize):
    """A hook to report the progress of a download. This is mostly intended for users with
    slow internet connections. Reports every 1% change in download progress.
    """
    global last_percent_reported
    # Reference the global percent variable
    
    percent = int(count * blockSize * 100 / totalSize)
    # Take the count * blockSize / totalSize , multiply by 100 for a percent

    if last_percent_reported != percent:
    # If the precent has since changed
        if percent % 5 == 0:
            # And if the current precent is past an increment of 5
            sys.stdout.write("%s%%" % percent)
            # Print the current percent at 5% increments
            sys.stdout.flush()
            # Flsuh the terminal; doesn't seem to do anything in Jupyter
        else:
            sys.stdout.write(".")
            # Else just print a '.'
            sys.stdout.flush()
            
    last_percent_reported = percent
    # Set the last_percent_reported global to the current percent given
        
def download(filename, expected_bytes, force=False):
    """Download a file if not present, and make sure it's the right size."""
    
        
    if force or not os.path.exists(filename):      
    # If force == True or the data does not exist  
        print('Attempting to download:', filename) 
        download = urlretrieve(url + filename, filename, reporthook=download_progress_hook)
        # Download the filename, and use the download_progress_hook inteface to report back results 
        # reporthook probably hands off count, blockSize, totalSize, and other info, because its not in here
        # urlretreive returns a tuple: ('filename', <httplib.HTTPMessage instance at someaddress>) 
        # Use filename, _ to split the name from the other stuff (we use _ to store the misc stuff) 
        
        filename, _ = download
        #print('Retrieved tuple named {}, so split it into {} and {}'.format(download, filename, _))

        # Confirm completion
        print('\nDownload Complete!')
        
    statinfo = os.stat(filename)
    # Check the bytesize of our new file
    
    if statinfo.st_size == expected_bytes:
    # If the filesize matches expected bytes
        print('Found and verified', filename)
    else:
    # If it's not 
        raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?')
        # Raise an exception to tell me it didn't work
    
    return filename
    # Returns the actual name when called, but will do this whole function in an attempt to return this

    
data_folder()
# Jump into the data folder 

train_filename = download('notMNIST_large.tar.gz', 247336696)
# What is the training file

test_filename = download('notMNIST_small.tar.gz', 8458043)
# What is the testing file









    



Found and verified notMNIST_large.tar.gz
Found and verified notMNIST_small.tar.gz

Extract the dataset from the compressed .tar.gz file. This should give you a set of directories, labelled A through J.



In [27]:

    
def extract(filename, num_classes, force=False):
    """Extract the .tar into subfolders, and check if right number of classes."""
    
    root = os.path.splitext(os.path.splitext(filename)[0])[0]  
    # remove the file type at the '.'; first for .gz, then for .tar

    if os.path.isdir(root) and not force:
        # You may override by setting force=True.
        print('%s already present - Skipping extraction of %s.' % (root, filename))
        # The directory already exists
    else:
        print('Extracting data for %s. This may take a while. Please wait.' % root)
        tar = tarfile.open(filename)
        # open the file as a 
        sys.stdout.flush()
        # Clean the terminal output
        tar.extractall()
        # Extract the tar file into the directory
        tar.close()
        # Close the tar
    
    data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root)) if os.path.isdir(os.path.join(root, d))]
    # List comprehension to get all subfolder strings from a list of all folders if they are each directories
    
    if len(data_folders) != num_classes:
    # if there are few folders than expected, raise exception
        raise Exception('Expected %d folders, one per class. Found %d instead.' % (num_classes, len(data_folders)))
    print(data_folders)
    # Say what they are
    
    return data_folders
    # return it for convenience 

data_folder()
# Jump into the data folder 

train_folders = extract(train_filename, 10)
# What are the training folders (I expect 10)

test_folders = extract(test_filename, 10)
# What are the test folders (I expect 10)









    



notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.
['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']
Extracting data for notMNIST_small. This may take a while. Please wait.
['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']

Problem 1

Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded.

Hint: you can use the package IPython.display.



In [88]:

    
def grab_sample(folder, num_images):
    """Grab a random sample of images and show them"""
    
    os.chdir(folder)
    # Get into that folder
    
    subfolder = os.listdir()[np.random.randint(0,len(os.listdir()))]
    # Pick a random subfolder

    os.chdir(subfolder)
    # Get in there
    
    image_files = [os.listdir()[i] for i in range(0,num_images)]
    # Pick a random set of image files
    
    current_folder()
    [display(Image(filename=i)) for i in image_files]
    # Show us what you picked
    
data_folder()
grab_sample('notMNIST_large',10)
data_folder()









    



Current folder: D

Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.

We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road.

A few images might not be readable, we'll just skip them.



In [162]:

    
image_height = 28  # Pixel height (pre-defined)
image_width = 28 # Pixel width (pre-defined)
pixel_depth = 255.0  # Number of levels per pixel (pre-defined)

def load_letter(folder, min_num_images):   
    """Load the data for a single letter label."""
    os.chdir(folder)
    image_files = os.listdir()
    # Go into that subfolder and grab all the images 
    
    print('Creating a {} x {} x {} tensor to fill with normalized values between 0 and {}'.format(
        len(image_files), image_height, image_width, pixel_depth))
    dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
    # Make a dataset to fill as long as the images, with the 
    # image_height, image_weidth, and holding float32 data
    
    num_images = 0    
    # Quick counter
    
    for image in image_files:
        print('On image {}.'.format(num_images))
        try: 
            image_data = (ndimage.imread(image).astype(float) - 
                          pixel_depth / 2) / pixel_depth
            
            # Read the image_data as a float, normalizing by subtracting (max_value/2) 
            # and dividing by max_value

            # Normalizing: 
            # We do this because sparce matrices (filled with 0s) are much easier on memory 
            # Since we subtract by half the max_value, we'll never be negative 
            # Values are forced into range between 0, and highest is max_value/2
            # We also divide by max_value to set them between 0 and 1 
            
            # Uncomment the following to see this in action:
            #not_normalized_image_data= ndimage.imread(image).astype(float)
            #print(not_normalized_image_data)
            #print(image_data)
            
            #if image_data.shape != (image_size, image_size):
            #    raise Exception('Unexpected image shape: %s' % str(image_data.shape))
            #dataset[num_images, :, :] = image_data
            #num_images = num_images + 1
            
            if image_data.shape != (image_size, image_size):
            # Is it the right shape (A 28 x 28 array) 
                raise Exception('Unexpected image shape: %s' % str(image_data.shape))


            print('Appending {} to dataset.'.format(image))
            # Tell us whats about to happen

            dataset[num_images, :, :] = image_data
            # Then do it

            num_images = num_images + 1
            # Update the counter
        
        
        except IOError as e:
            print('Could not read:', image_file, ':', e, ', skipping.')
        
        
    dataset = dataset[0:num_images, :, :]
    # Final dataset better be only as long as the images we appened

    if num_images < min_num_images:
    # Lets make sure we got the minimum number of images we wanted
        raise Exception('Many fewer images than expected: %d < %d' %
                        (num_images, min_num_images))
        # Raise an exception if not
        
            
    print('\n {} Dataset complete \n Mean: {} \n Standard Deviation: {}'.format(dataset.shape, 
                                                                              np.mean(dataset), np.std(dataset)))
    # Some descriptive stats on the new tensor 
    
    return dataset

data_folder()
dataset = load_letter(test_folders[2], 5)









    



Creating a 1873 x 28 x 28 tensor to fill with normalized values between 0 and 255.0
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On image 118.
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On image 119.
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On image 120.
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On image 121.
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On image 138.
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On image 144.
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On image 153.
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On image 158.
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On image 159.
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On image 160.
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On image 164.
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On image 168.
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On image 170.
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On image 180.
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On image 186.
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On image 189.
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On image 248.
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On image 275.
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On image 278.
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On image 300.
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On image 304.
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On image 335.
Appending Q2VudHVyeSBTY2hvb2xib29rIEdyZWVrIEJvbGQgSW5jbGluZWQgQlQudHRm.png to dataset.
On image 336.
Appending Q2VudHVyeSBTY2hvb2xib29rIEdyZWVrIEluY2xpbmVkIEJULnR0Zg==.png to dataset.
On image 337.
Appending Q2VudHVyeSBTY2hvb2xib29rIEJvbGQgSXRhbGljLnBmYg==.png to dataset.
On image 338.
Appending Q2VudHVyeSBTY2hvb2xib29rIEl0YWxpYy5wZmI=.png to dataset.
On image 339.
Appending Q2VudHVyeSBTY2hvb2xib29rIEl0YWxpYyBCVC50dGY=.png to dataset.
On image 340.
Appending Q2VudHVyeSBTY2hvb2xib29rIFNTaSBCb2xkIEl0YWxpYy50dGY=.png to dataset.
On image 341.
Appending Q2VudHVyeSBTY2hvb2xib29rIFQgRUUgUmVndWxhciBJdGFsaWMucGZi.png to dataset.
On image 342.
Appending Q2VudHVyeTcyNSBCbGsgQlQgQmxhY2sudHRm.png to dataset.
On image 343.
Appending Q2VudHVyeTcyNSBCZENuIEJUIEJvbGQudHRm.png to dataset.
On image 344.
Appending Q2VudHVyeTcyNUJULUl0YWxpYy5vdGY=.png to dataset.
On image 345.
Appending Q2VudHVyeTcyNUJULVJvbWFuQ29uZGVuc2VkLm90Zg==.png to dataset.
On image 346.
Appending Q2VudHVyeTczMSBCVCBJdGFsaWMudHRm.png to dataset.
On image 347.
Appending Q2VudHVyeTczMUJULUJvbGRJdGFsaWNBLm90Zg==.png to dataset.
On image 348.
Appending Q2VudHVyeU9sZFN0eWxlU3RkLVJlZ3VsYXIub3Rm.png to dataset.
On image 349.
Appending Q2VudHVyeU9TTVRTdGQtQm9sZEl0YWxpYy5vdGY=.png to dataset.
On image 350.
Appending Q2VudHVyeU9TTVRTdGQtSXRhbGljLm90Zg==.png to dataset.
On image 351.
Appending Q2VudHVyeUlUQ0JRLUJvb2tJdGFsaWMub3Rm.png to dataset.
On image 352.
Appending Q2VudHVyeUlUQ0JRLUxpZ2h0Lm90Zg==.png to dataset.
On image 353.
Appending Q2VudHVyeUlUQ0NvbmRlbnNlZEJRLUJvb2sub3Rm.png to dataset.
On image 354.
Appending Q2VudHVyeUlUQ0NvbmRlbnNlZEJRLUJvbGQub3Rm.png to dataset.
On image 355.
Appending Q2VudHVyeUlUQ0NvbmRlbnNlZEJRLUxpZ2h0Lm90Zg==.png to dataset.
On image 356.
Appending Q2VudHVyeUlUQ2J5QlQtQm9sZENvbmRJdGFsaWMub3Rm.png to dataset.
On image 357.
Appending Q2VudHVyeUlUQ2J5QlQtQm9sZENvbmRlbnNlZC5vdGY=.png to dataset.
On image 358.
Appending Q2VudHVyeUlUQ2J5QlQtTGlnaHRDb25kSXRhbGljLm90Zg==.png to dataset.
On image 359.
Appending Q2VudHVyeUNvbmRCb29rSXRhbGljLm90Zg==.png to dataset.
On image 360.
Appending Q2VudHVyeVN0ZC1VbHRyYUl0YWxpYy5vdGY=.png to dataset.
On image 361.
Appending Q2VudHVyeVNjaG9vbC5vdGY=.png to dataset.
On image 362.
Appending Q2VudHVyeVNjaG9vbGJvb2tCVC1Sb21hbi5vdGY=.png to dataset.
On image 363.
Appending Q2VudHVyeVNjaG9vbGJvb2tFRi1Cb2wub3Rm.png to dataset.
On image 364.
Appending Q2VyaWdvRUYtQm9sZEl0YWxpYy5vdGY=.png to dataset.
On image 365.
Appending Q2xhaXJ2YXV4TFRTdGQub3Rm.png to dataset.
On image 366.
Appending Q2xhc3NpY2EgT25lLnR0Zg==.png to dataset.
On image 367.
Appending Q2xhc3NpY2EtSXRhbGljLnR0Zg==.png to dataset.
On image 368.
Appending Q2xhc3NpY2FsR2FyYW1vbmRCVC1Sb21hbi5vdGY=.png to dataset.
On image 369.
Appending Q2xhc3NpYyBUcmFzaCAxIEJSSy50dGY=.png to dataset.
On image 370.
Appending Q2xhc3NpYzEwNjUudHRm.png to dataset.
On image 371.
Appending Q2xhcml0eSBHb3RoaWMgU0YudHRm.png to dataset.
On image 372.
Appending Q2xhcmVuZG9uIEh2IEJUIEhlYXZ5LnR0Zg==.png to dataset.
On image 373.
Appending Q2xhcmVuZG9uIEJsYWNrLnBmYg==.png to dataset.
On image 374.
Appending Q2xhcmVuZG9uQlEtQmxhY2sub3Rm.png to dataset.
On image 375.
Appending Q2xhcmVuZG9uTVRTdGQub3Rm.png to dataset.
On image 376.
Appending Q2xlb3BhdHJhLm90Zg==.png to dataset.
On image 377.
Appending Q2xlcmljIEJsYWNrIFNTaSBCbGFjayBJdGFsaWMudHRm.png to dataset.
On image 378.
Appending Q2xlcmZhY2UtQm9sZC5vdGY=.png to dataset.
On image 379.
Appending Q2xlcmZhY2UtQm9sZEl0YS5vdGY=.png to dataset.
On image 380.
Appending Q2xlcmZhY2UtRGVtaUJvbGQub3Rm.png to dataset.
On image 381.
Appending Q2xldmVyIER1a2UgQlROIFNtb290aC50dGY=.png to dataset.
On image 382.
Appending Q2xlYXJ2aWV3SHd5LTYtQi50dGY=.png to dataset.
On image 383.
Appending Q2xlYXJmYWNlIElUQyBCbGFjay50dGY=.png to dataset.
On image 384.
Appending Q2xlYXJmYWNlIElUQyBCbGFjayBJdGFsaWMudHRm.png to dataset.
On image 385.
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On image 386.
Appending Q2xlYXJmYWNlR290aGljTFQtQmxhY2sub3Rm.png to dataset.
On image 387.
Appending Q2xlYXJmYWNlR290aGljTFRTdGQtTGlnaHQub3Rm.png to dataset.
On image 388.
Appending Q2xlYXJmYWNlTFQtSGVhdnkub3Rm.png to dataset.
On image 389.
Appending Q2xlYXJmYWNlTVRTdGQtQm9sZC5vdGY=.png to dataset.
On image 390.
Appending Q2xlYXJnb3RoaWMtRXh0cmFCb2xkLm90Zg==.png to dataset.
On image 391.
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On image 392.
Appending Q2xlYXJseSBHb3RoaWMgSGVhdnkgSXRhbGljLnR0Zg==.png to dataset.
On image 393.
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On image 394.
Appending Q2xpcXVlLVNlcmlmQm9sZE9ibGlxdWUub3Rm.png to dataset.
On image 395.
Appending Q2xpY2hlZUNFLU9ibGlxdWUub3Rm.png to dataset.
On image 396.
Appending Q2xvaXN0ZXJPcGVuRmFjZSBCVC50dGY=.png to dataset.
On image 397.
Appending Q2xvaXN0ZXJTdGQtT3BlbkZhY2Uub3Rm.png to dataset.
On image 398.
Appending Q2xvdmVySVRDLm90Zg==.png to dataset.
On image 399.
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On image 400.
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On image 401.
Appending Q3J1c3RpRXN0LnR0Zg==.png to dataset.
On image 402.
Appending Q3J5c3RhbHNIYW5kIFJlZ3VsYXIudHRm.png to dataset.
On image 403.
Appending Q3Jha29vbSEudHRm.png to dataset.
On image 404.
Appending Q3JhenkgR2lybHogQmxvbmQgQlROLnR0Zg==.png to dataset.
On image 405.
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On image 406.
Appending Q3Jld0N1dENhcHMgSXRhbGljLnR0Zg==.png to dataset.
On image 407.
Appending Q3Jpc3RvTGlraWQgVHJ5b3V0LnR0Zg==.png to dataset.
On image 408.
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On image 409.
Appending Q3JvaXNzYW50RUYub3Rm.png to dataset.
On image 410.
Appending Q3Jvbm9zUHJvLUJvbGRDYXB0SXQub3Rm.png to dataset.
On image 411.
Appending Q3Jvbm9zUHJvLUJvbGRTdWJoLm90Zg==.png to dataset.
On image 412.
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On image 413.
Appending Q3Jvbm9zUHJvLVJlZ3VsYXIub3Rm.png to dataset.
On image 414.
Appending Q3Jvbm9zUHJvLVNlbWlib2xkSXQub3Rm.png to dataset.
On image 415.
Appending Q3Jvc2J5c0hhbmQudHRm.png to dataset.
On image 416.
Appending Q3Jvc3NvdmVyIEJvbGRPYmxpcXVlLnR0Zg==.png to dataset.
On image 417.
Appending Q3Jvc3NvdmVyIEV4dHJhTGlnaHQudHRm.png to dataset.
On image 418.
Appending Q3liZXJ3aGl6LnR0Zg==.png to dataset.
On image 419.
Appending Q3ljbGluLnR0Zg==.png to dataset.
On image 420.
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On image 421.
Appending Q3VybHpNVFN0ZC1SZWd1bGFyLm90Zg==.png to dataset.
On image 422.
Appending Q3VydmF0dXJlLVJvdW5kZWQub3Rm.png to dataset.
On image 423.
Appending Q3VydmF0dXJlLVJvdW5kZWRJdGFsaWMub3Rm.png to dataset.
On image 424.
Appending Q3VzaGluZ0Jvb2tJdGFsaWMub3Rm.png to dataset.
On image 425.
Appending Q3VzaGluZ0l0Y1RFRUhlYS50dGY=.png to dataset.
On image 426.
Appending Q3VzaGluZ0VGLUhlYXZ5SXRhbGljLm90Zg==.png to dataset.
On image 427.
Appending Q3VzaGluZ1N0ZC1Cb29rSXRhbGljLm90Zg==.png to dataset.
On image 428.
Appending Q3VzaGluZyBIZWF2eSBJdGFsaWMgQlQudHRm.png to dataset.
On image 429.
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On image 430.
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On image 431.
Appending QlJFQUtCRUFULm90Zg==.png to dataset.
On image 432.
Appending QlZHSGZldHRLdXJzaXYub3Rm.png to dataset.
On image 433.
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On image 434.
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On image 435.
Appending Qm90b24tTGlnaHRJdGFsaWMub3Rm.png to dataset.
On image 436.
Appending Qm90b25CUS1NZWRpdW1JdGFsaWMub3Rm.png to dataset.
On image 437.
Appending Qm91Z2FuIEJsYWNrIFNTaSBCb2xkLnR0Zg==.png to dataset.
On image 438.
Appending Qm91Z2FuIEJvb2sgU1NpIEJvb2sudHRm.png to dataset.
On image 439.
Appending Qm91Z2FuIFNTaSBTZW1pIEJvbGQudHRm.png to dataset.
On image 440.
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On image 441.
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On image 442.
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On image 443.
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On image 444.
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On image 445.
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On image 446.
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On image 447.
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On image 448.
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On image 449.
Appending Qm9kb25pLUJvbGRJdGFsaWMub3Rm.png to dataset.
On image 450.
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On image 451.
Appending Qm9kb25pQ2xhc3NpYy1Cb2xkSXRhbGljLm90Zg==.png to dataset.
On image 452.
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On image 453.
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On image 454.
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On image 455.
Appending Qm9kb25pQmF1ZXJCUS1SZWd1bGFyLm90Zg==.png to dataset.
On image 456.
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On image 457.
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On image 458.
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On image 459.
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On image 460.
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On image 461.
Appending Qm9kb25pRUYtRGVtaUJvbGQub3Rm.png to dataset.
On image 462.
Appending Qm9kb25pRUYtRGVtaUJvbGRJdGEub3Rm.png to dataset.
On image 463.
Appending Qm9kb25pRUYtTGlnaHQub3Rm.png to dataset.
On image 464.
Appending Qm9kb25pSUNHLm90Zg==.png to dataset.
On image 465.
Appending Qm9kb25pT2xkRmFjZUJFLVJlZ3VsYXIub3Rm.png to dataset.
On image 466.
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On image 467.
Appending Qm9kb25pT2xkRmFjZUJRLVJlZ3VsYXIub3Rm.png to dataset.
On image 468.
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On image 469.
Appending Qm9kb25pTVRTdGQtSXRhbGljLm90Zg==.png to dataset.
On image 470.
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On image 471.
Appending Qm9kb25pU2l4SVRDLUJvbGQub3Rm.png to dataset.
On image 472.
Appending Qm9kb25pU2l4SVRDLUJvbGRJdGFsT1Mub3Rm.png to dataset.
On image 473.
Appending Qm9kb25pU2l4SVRDU3RkLUJvbGQub3Rm.png to dataset.
On image 474.
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On image 475.
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On image 476.
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On image 477.
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On image 478.
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On image 479.
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On image 480.
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On image 481.
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On image 482.
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On image 483.
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On image 484.
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On image 485.
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On image 486.
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On image 487.
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On image 488.
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On image 489.
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On image 490.
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On image 495.
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On image 498.
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On image 499.
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On image 500.
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On image 505.
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On image 506.
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On image 548.
Appending QmFyYmVyUG9sZSBSZWd1bGFyLnR0Zg==.png to dataset.
On image 549.
Appending QmFza2VydmlsbGUgQm9sZCBJdCBXaW45NUJUKDEpLnR0Zg==.png to dataset.
On image 550.
Appending QmFza2VydmlsbGUgQmxhY2sgU1NpIEJvbGQgSXRhbGljLnR0Zg==.png to dataset.
On image 551.
Appending QmFza2VydmlsbGUgU1NpLnR0Zg==.png to dataset.
On image 552.
Appending QmFza2VydmlsbGUtTm9ybWFsLUl0YWxpYy50dGY=.png to dataset.
On image 553.
Appending QmFza2VydmlsbGVCUS1JdGFsaWMub3Rm.png to dataset.
On image 554.
Appending QmFza2VydmlsbGVCUS1NZWRpdW0ub3Rm.png to dataset.
On image 555.
Appending QmFza2VydmlsbGVCVC1Sb21hbi5vdGY=.png to dataset.
On image 556.
Appending QmFza2VydmlsbGVFRi1NZWRpdW0ub3Rm.png to dataset.
On image 557.
Appending QmFza2VydmlsbGVOZXdCUS1Cb2xkSXRhbGljLm90Zg==.png to dataset.
On image 558.
Appending QmFza2VydmlsbGVULnR0Zg==.png to dataset.
On image 559.
Appending QmFza2VydmlsbGVULVJlZ3Uub3Rm.png to dataset.
On image 560.
Appending QmFza2VydmlsbGVULVJlZ3VJdGFsLm90Zg==.png to dataset.
On image 561.
Appending QmFza2VydmxsZTIgQlQgUm9tYW4udHRm.png to dataset.
On image 562.
Appending QmFzaWMgU2FucyBIZWF2eSBTRiBCb2xkLnR0Zg==.png to dataset.
On image 563.
Appending QmFzaWMgU2FucyBTRiBCb2xkLnR0Zg==.png to dataset.
On image 564.
Appending QmFzc2V0dCBUaGluIEJvbGRJdGFsaWMudHRm.png to dataset.
On image 565.
Appending QmFzcXVlIFRoaW4gTm9ybWFsLnR0Zg==.png to dataset.
On image 566.
Appending QmFzZU1vbm9XaWRlVGhpbiBSZWd1bGFyLnR0Zg==.png to dataset.
On image 567.
Appending QmFzZU5pbmUgQm9sZC50dGY=.png to dataset.
On image 568.
Appending QmFzZU5pbmVCSS50dGY=.png to dataset.
On image 569.
Appending QmFzZU5pbmVTbWFsbENhcHMgQm9sZCBJdGFsaWMudHRm.png to dataset.
On image 570.
Appending QmFzZU5pbmVTQ0IudHRm.png to dataset.
On image 571.
Appending QmFzZVR3ZWx2ZVNlcmlmU0NCLnR0Zg==.png to dataset.
On image 572.
Appending Qml0c3RyZWFtIEFtZXJpZ28gQm9sZC5wZmI=.png to dataset.
On image 573.
Appending Qml0c3RyZWFtIEFtZXJpZ28gQm9sZCBJdGFsaWMucGZi.png to dataset.
On image 574.
Appending Qml0c3RyZWFtIEFycnVzIEJsYWNrLnBmYg==.png to dataset.
On image 575.
Appending Qml0c3RyZWFtIElvd2FuIE9sZCBTdHlsZS5wZmI=.png to dataset.
On image 576.
Appending Qml0c3RyZWFtIENoYXJ0ZXIgQmxhY2sgSXRhbGljIE9TRi5wZmI=.png to dataset.
On image 577.
Appending Qml0c3RyZWFtIENvb3BlciBMaWdodCBJdGFsaWMucGZi.png to dataset.
On image 578.
Appending Qml0c3RyZWFtIFZlcmEgU2FucyBNb25vIE9ibGlxdWUudHRm.png to dataset.
On image 579.
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On image 580.
Appending Qml0d2lzZS50dGY=.png to dataset.
On image 581.
Appending Qml4bGVlLUhlYXZ5LnR0Zg==.png to dataset.
On image 582.
Appending Qml4bGVlQ25kLUhlYXZ5LnR0Zg==.png to dataset.
On image 583.
Appending Qmlja2hhbSBTY3JpcHQgVHdvLnR0Zg==.png to dataset.
On image 584.
Appending QmlndG93bmVCb2xkLnR0Zg==.png to dataset.
On image 585.
Appending QmlnIEJhY29uIFRyeW91dC50dGY=.png to dataset.
On image 586.
Appending Qmlqb3V4LUJvbGQub3Rm.png to dataset.
On image 587.
Appending QmlraW5pLnR0Zg==.png to dataset.
On image 588.
Appending QmlsbGJvYXJkIDExIENvbmRlbnNlZCBOb3JtYWwudHRm.png to dataset.
On image 589.
Appending QmlsbHV5LnR0Zg==.png to dataset.
On image 590.
Appending QmlsYm9EaXNwbGF5IEJvbGQgSXRhbGljLnR0Zg==.png to dataset.
On image 591.
Appending QmlubmVyRC50dGY=.png to dataset.
On image 592.
Appending QmluZ28ub3Rm.png to dataset.
On image 593.
Appending QmlvbmljIENvbWljIEV4cCBJdGFsaWMudHRm.png to dataset.
On image 594.
Appending QmlvbmljIFR5cGUgQ29uZGVuc2VkLnR0Zg==.png to dataset.
On image 595.
Appending QmlvbWVjaGFuaWMudHRm.png to dataset.
On image 596.
Appending QmlyZC50dGY=.png to dataset.
On image 597.
Appending QmV0b25FRi1FeHRyYUJvbGQub3Rm.png to dataset.
On image 598.
Appending QmV0dGVyIE9mZiAxLnR0Zg==.png to dataset.
On image 599.
Appending QmV0dGVyS2FtcCBCb2xkSXRhbGljLnR0Zg==.png to dataset.
On image 600.
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On image 601.
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On image 602.
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On image 603.
Appending QmV0YVNlbWktQm9sZC5vdGY=.png to dataset.
On image 604.
Appending QmV0YWNhcGl0YWwudHRm.png to dataset.
On image 605.
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On image 606.
Appending QmVhc3QudHRm.png to dataset.
On image 607.
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On image 608.
Appending QmVhdG5pa0hheXNlZWQudHRm.png to dataset.
On image 609.
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On image 610.
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On image 611.
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On image 612.
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On image 613.
Appending QmVla21hbi1TcXVhcmVCb2xkLm90Zg==.png to dataset.
On image 614.
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On image 615.
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On image 616.
Appending QmVlc2tuZWVzQy5vdGY=.png to dataset.
On image 617.
Appending QmVldGhhbS1MaWdodC5vdGY=.png to dataset.
On image 618.
Appending QmVsaXppb0JvbGQub3Rm.png to dataset.
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On image 620.
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On image 622.
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On image 624.
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On image 625.
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On image 626.
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On image 627.
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On image 630.
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On image 633.
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On image 634.
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On image 635.
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On image 636.
Appending QmVuZ3VpYXRFRi1Cb2xkSXRhbGljLm90Zg==.png to dataset.
On image 637.
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On image 638.
Appending QmVuZ3VpYXRHb3RoaWNCUS1Cb2xkSXRhbGljLm90Zg==.png to dataset.
On image 639.
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On image 640.
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On image 650.
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On image 660.
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On image 676.
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On image 677.
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On image 680.
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On image 699.
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On image 700.
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On image 760.
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On image 761.
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On image 762.
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On image 763.
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On image 764.
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On image 765.
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On image 766.
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On image 767.
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On image 768.
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On image 769.
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On image 770.
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On image 771.
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On image 772.
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On image 773.
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On image 774.
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On image 775.
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On image 776.
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On image 777.
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On image 778.
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On image 779.
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On image 780.
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On image 781.
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On image 782.
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On image 783.
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On image 784.
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On image 785.
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On image 786.
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On image 787.
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On image 788.
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On image 789.
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On image 790.
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On image 791.
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On image 792.
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On image 793.
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On image 794.
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On image 795.
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On image 796.
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On image 797.
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On image 798.
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On image 799.
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On image 800.
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On image 801.
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On image 802.
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On image 803.
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On image 804.
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On image 805.
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On image 806.
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On image 807.
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On image 808.
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On image 809.
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On image 810.
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On image 811.
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On image 812.
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On image 813.
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On image 814.
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On image 815.
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On image 817.
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On image 818.
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On image 819.
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On image 820.
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On image 821.
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On image 823.
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On image 824.
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On image 825.
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On image 826.
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On image 827.
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On image 830.
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On image 831.
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On image 834.
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On image 835.
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On image 837.
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On image 838.
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On image 839.
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On image 840.
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On image 841.
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On image 842.
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On image 844.
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On image 850.
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On image 851.
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On image 852.
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On image 853.
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On image 854.
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On image 855.
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On image 856.
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On image 857.
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On image 858.
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On image 860.
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On image 862.
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On image 864.
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On image 865.
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On image 866.
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On image 868.
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On image 870.
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On image 876.
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On image 877.
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On image 886.
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On image 887.
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On image 888.
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On image 890.
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On image 894.
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On image 895.
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On image 898.
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On image 900.
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On image 901.
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On image 902.
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On image 903.
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On image 905.
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On image 906.
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On image 909.
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On image 911.
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On image 922.
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On image 924.
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On image 925.
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Appending RFRMQXJnb1QtVWx0cmFIZWF2eUl0YWxpYy5vdGY=.png to dataset.
On image 1180.
Appending RFRMRG9jdW1lbnRhVC1JdGFsaWMub3Rm.png to dataset.
On image 1181.
Appending RFRMRG9jdW1lbnRhVC1NZWRpdW0ub3Rm.png to dataset.
On image 1182.
Appending RFRMRG9yaWFuU1QtQmxhY2sub3Rm.png to dataset.
On image 1183.
Appending RFRMRmxlaXNjaG1hbm4tRFJlZ3VsYXIub3Rm.png to dataset.
On image 1184.
Appending RFRMRmxlaXNjaG1hbm4tU1RCb2xkSXQub3Rm.png to dataset.
On image 1185.
Appending RFRMRmxlaXNjaG1hbm4tVEJvbGRDYXBzLm90Zg==.png to dataset.
On image 1186.
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On image 1187.
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On image 1188.
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On image 1189.
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On image 1190.
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On image 1191.
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On image 1192.
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On image 1193.
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On image 1194.
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On image 1195.
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On image 1196.
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On image 1197.
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On image 1198.
Appending RFRMVmFuZGVuS2VlcmVEQ2Fwcy1Cb2xkLm90Zg==.png to dataset.
On image 1199.
Appending RFRMVmFuZGVuS2VlcmVTRC1Cb2xkLm90Zg==.png to dataset.
On image 1200.
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On image 1201.
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On image 1202.
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On image 1203.
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On image 1204.
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On image 1205.
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On image 1206.
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On image 1207.
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On image 1208.
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On image 1209.
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On image 1210.
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On image 1211.
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On image 1212.
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On image 1213.
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On image 1214.
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On image 1215.
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On image 1216.
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On image 1217.
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On image 1218.
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On image 1219.
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On image 1220.
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On image 1221.
Appending RGFsZXlzR290aGljLUJsYWNrLm90Zg==.png to dataset.
On image 1222.
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On image 1223.
Appending RGFudG8gTGl0ZSBCb2xkLnR0Zg==.png to dataset.
On image 1224.
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On image 1225.
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On image 1226.
Appending RGFudGVNVFN0ZC1NZWRpdW1JdGFsaWMub3Rm.png to dataset.
On image 1227.
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On image 1228.
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On image 1229.
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On image 1230.
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On image 1231.
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On image 1232.
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On image 1233.
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On image 1234.
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On image 1235.
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On image 1236.
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On image 1237.
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On image 1239.
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On image 1240.
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On image 1241.
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On image 1242.
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On image 1243.
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On image 1244.
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On image 1245.
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On image 1246.
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On image 1247.
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On image 1248.
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On image 1249.
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On image 1250.
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On image 1256.
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On image 1402.
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On image 1615.
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On image 1616.
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On image 1617.
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On image 1618.
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On image 1619.
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On image 1620.
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On image 1621.
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On image 1622.
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On image 1623.
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On image 1624.
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On image 1625.
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On image 1626.
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On image 1627.
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On image 1628.
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On image 1629.
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On image 1630.
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On image 1631.
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On image 1632.
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On image 1633.
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On image 1635.
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On image 1636.
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On image 1637.
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On image 1638.
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On image 1639.
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On image 1640.
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On image 1645.
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On image 1649.
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On image 1650.
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On image 1651.
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On image 1652.
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On image 1657.
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On image 1658.
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On image 1660.
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On image 1668.
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On image 1670.
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On image 1672.
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On image 1674.
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On image 1678.
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On image 1679.
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On image 1680.
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Appending SHVtYW5pc3QgU2xhYnNlcmlmIDcxMi5wZmI=.png to dataset.
On image 1832.
Appending SHVtYW5zdDUzMSBCVCBCb2xkLnR0Zg==.png to dataset.
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Appending SHZTdHJpcGUtRXh0cmFCb2xkLnR0Zg==.png to dataset.
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Appending SVNPQ1BFVVIgSXRhbGljLnR0Zg==.png to dataset.
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Appending SVRDIEdvdWR5IFNhbnMgTWVkaXVtLnBmYg==.png to dataset.
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Appending SVRDIEF2YW50IEdhcmRlIEdvdGhpYyBFeHRyYSBMaWdodCBPYmxpcXVlLnBmYg==.png to dataset.
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 (1873, 28, 28) Dataset complete 
 Mean: -0.14152060449123383 
 Standard Deviation: 0.44269031286239624

Pickle stuff!



In [ ]:

    
def maybe_pickle(data_folders, min_num_images_per_class, force=False):
  dataset_names = []
  for folder in data_folders:
    set_filename = folder + '.pickle'
    dataset_names.append(set_filename)
    if os.path.exists(set_filename) and not force:
      # You may override by setting force=True.
      print('%s already present - Skipping pickling.' % set_filename)
    else:
      print('Pickling %s.' % set_filename)
      dataset = load_letter(folder, min_num_images_per_class)
      try:
        with open(set_filename, 'wb') as f:
          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
      except Exception as e:
        print('Unable to save data to', set_filename, ':', e)
  
  return dataset_names

train_datasets = maybe_pickle(train_folders, 45000)
test_datasets = maybe_pickle(test_folders, 1800)



In [ ]: