CoCalc Public Filesscratch / 1_notmnist.ipynbOpen with one click!
Author: Harald Schilly
Views : 69
Description: Jupyter notebook scratch/1_notmnist.ipynb
Compute Environment: Ubuntu 18.04 (Deprecated)

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 [20]:
# These are all the modules we'll be using later. Make sure you can import them # before proceeding further. from __future__ import print_function import matplotlib.pyplot as plt import numpy as np import os import sys import tarfile from IPython.display import display, Image from scipy import ndimage from sklearn.linear_model import LogisticRegression from six.moves.urllib.request import urlretrieve from six.moves import cPickle as pickle # Config the matlotlib 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 [ ]:
url = 'http://commondatastorage.googleapis.com/books1000/' last_percent_reported = None 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 percent = int(count * blockSize * 100 / totalSize) if last_percent_reported != percent: if percent % 5 == 0: sys.stdout.write("%s%%" % percent) sys.stdout.flush() else: sys.stdout.write(".") sys.stdout.flush() last_percent_reported = percent def maybe_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): print('Attempting to download:', filename) filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook) print('\nDownload Complete!') statinfo = os.stat(filename) if statinfo.st_size == expected_bytes: print('Found and verified', filename) else: raise Exception( 'Failed to verify ' + filename + '. Can you get to it with a browser?') return filename train_filename = maybe_download('notMNIST_large.tar.gz', 247336696) test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)

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

In [ ]:
num_classes = 10 np.random.seed(133) def maybe_extract(filename, force=False): root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz 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)) else: print('Extracting data for %s. This may take a while. Please wait.' % root) tar = tarfile.open(filename) sys.stdout.flush() tar.extractall() tar.close() data_folders = [ os.path.join(root, d) for d in sorted(os.listdir(root)) if os.path.isdir(os.path.join(root, d))] if len(data_folders) != num_classes: raise Exception( 'Expected %d folders, one per class. Found %d instead.' % ( num_classes, len(data_folders))) print(data_folders) return data_folders train_folders = maybe_extract(train_filename) test_folders = maybe_extract(test_filename)

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.


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 [ ]:
image_size = 28 # Pixel width and height. pixel_depth = 255.0 # Number of levels per pixel. def load_letter(folder, min_num_images): """Load the data for a single letter label.""" image_files = os.listdir(folder) dataset = np.ndarray(shape=(len(image_files), image_size, image_size), dtype=np.float32) print(folder) num_images = 0 for image in image_files: image_file = os.path.join(folder, image) try: image_data = (ndimage.imread(image_file).astype(float) - pixel_depth / 2) / pixel_depth 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 except IOError as e: print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.') dataset = dataset[0:num_images, :, :] if num_images < min_num_images: raise Exception('Many fewer images than expected: %d < %d' % (num_images, min_num_images)) print('Full dataset tensor:', dataset.shape) print('Mean:', np.mean(dataset)) print('Standard deviation:', np.std(dataset)) return dataset 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)

Problem 2

Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.


In [40]:
E = pickle.load(open('notMNIST_large/E.pickle', 'rb')) import matplotlib.pyplot as plt plt.matshow(E[3473, :, :], cmap = 'gray')
<matplotlib.image.AxesImage at 0x7f2cf09b15f8>

Problem 3

Another check: we expect the data to be balanced across classes. Verify that.


In [45]:
import os from glob import glob for p in glob('notMNIST_large/*.pickle'): data = pickle.load(open(p, 'rb')) print(data.shape)
(52911, 28, 28) (52911, 28, 28) (52912, 28, 28) (52911, 28, 28) (52912, 28, 28) (52912, 28, 28) (52912, 28, 28) (52912, 28, 28) (52912, 28, 28) (52909, 28, 28)

Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune train_size as needed. The labels will be stored into a separate array of integers 0 through 9.

Also create a validation dataset for hyperparameter tuning.

In [46]:
def make_arrays(nb_rows, img_size): if nb_rows: dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32) labels = np.ndarray(nb_rows, dtype=np.int32) else: dataset, labels = None, None return dataset, labels def merge_datasets(pickle_files, train_size, valid_size=0): num_classes = len(pickle_files) valid_dataset, valid_labels = make_arrays(valid_size, image_size) train_dataset, train_labels = make_arrays(train_size, image_size) vsize_per_class = valid_size // num_classes tsize_per_class = train_size // num_classes start_v, start_t = 0, 0 end_v, end_t = vsize_per_class, tsize_per_class end_l = vsize_per_class+tsize_per_class for label, pickle_file in enumerate(pickle_files): try: with open(pickle_file, 'rb') as f: letter_set = pickle.load(f) # let's shuffle the letters to have random validation and training set np.random.shuffle(letter_set) if valid_dataset is not None: valid_letter = letter_set[:vsize_per_class, :, :] valid_dataset[start_v:end_v, :, :] = valid_letter valid_labels[start_v:end_v] = label start_v += vsize_per_class end_v += vsize_per_class train_letter = letter_set[vsize_per_class:end_l, :, :] train_dataset[start_t:end_t, :, :] = train_letter train_labels[start_t:end_t] = label start_t += tsize_per_class end_t += tsize_per_class except Exception as e: print('Unable to process data from', pickle_file, ':', e) raise return valid_dataset, valid_labels, train_dataset, train_labels train_size = 200000 valid_size = 10000 test_size = 10000 valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets( train_datasets, train_size, valid_size) _, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size) print('Training:', train_dataset.shape, train_labels.shape) print('Validation:', valid_dataset.shape, valid_labels.shape) print('Testing:', test_dataset.shape, test_labels.shape)
Training: (200000, 28, 28) (200000,) Validation: (10000, 28, 28) (10000,) Testing: (10000, 28, 28) (10000,)

Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.

In [47]:
def randomize(dataset, labels): permutation = np.random.permutation(labels.shape[0]) shuffled_dataset = dataset[permutation,:,:] shuffled_labels = labels[permutation] return shuffled_dataset, shuffled_labels train_dataset, train_labels = randomize(train_dataset, train_labels) test_dataset, test_labels = randomize(test_dataset, test_labels) valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)

Problem 4

Convince yourself that the data is still good after shuffling!


Finally, let's save the data for later reuse:

In [48]:
pickle_file = 'notMNIST.pickle' try: f = open(pickle_file, 'wb') save = { 'train_dataset': train_dataset, 'train_labels': train_labels, 'valid_dataset': valid_dataset, 'valid_labels': valid_labels, 'test_dataset': test_dataset, 'test_labels': test_labels, } pickle.dump(save, f, pickle.HIGHEST_PROTOCOL) f.close() except Exception as e: print('Unable to save data to', pickle_file, ':', e) raise
In [49]:
statinfo = os.stat(pickle_file) print('Compressed pickle size:', statinfo.st_size)
Compressed pickle size: 690800503

Problem 5

By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it. Measure how much overlap there is between training, validation and test samples.

Optional questions:

  • What about near duplicates between datasets? (images that are almost identical)
  • Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.


Problem 6

Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.

Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.

Optional question: train an off-the-shelf model on all the data!