# coding: utf-8
# 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](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/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[ ]:
# 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 scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle
# 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.
import random
import matplotlib.image as mpimg
def plot_samples(data_folders, sample_size, title=None):
fig = plt.figure()
if title: fig.suptitle(title, fontsize=16, fontweight='bold')
for folder in data_folders:
image_files = os.listdir(folder)
image_sample = random.sample(image_files, sample_size)
for image in image_sample:
image_file = os.path.join(folder, image)
ax = fig.add_subplot(len(data_folders), sample_size, sample_size * data_folders.index(folder) +
image_sample.index(image) + 1)
image = mpimg.imread(image_file)
ax.imshow(image)
ax.set_axis_off()
plt.show()
plot_samples(train_folders, 10, 'Train Folders')
plot_samples(test_folders, 10, 'Test Folders')
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.
#
# ---
# ---
# Problem 3
# ---------
# Another check: we expect the data to be balanced across classes. Verify that.
#
# ---
def generate_fake_label(sizes):
labels = np.ndarray(sum(sizes), dtype=np.int32)
start = 0
end = 0
for label, size in enumerate(sizes):
start = end
end += size
for j in range(start, end):
labels[j] = label
return labels
def plot_balance():
fig, ax = plt.subplots(1, 2)
bins = np.arange(train_labels.min(), train_labels.max() + 2)
ax[0].hist(train_labels, bins=bins)
ax[0].set_xticks((bins[:-1] + bins[1:]) / 2, [chr(k) for k in range(ord("A"), ord("J") + 1)])
ax[0].set_title("Training data")
bins = np.arange(test_labels.min(), test_labels.max() + 2)
ax[1].hist(test_labels, bins=bins)
ax[1].set_xticks((bins[:-1] + bins[1:]) / 2, [chr(k) for k in range(ord("A"), ord("J") + 1)])
ax[1].set_title("Test data")
plt.show()
def mean(numbers):
return float(sum(numbers)) / max(len(numbers), 1)
def balance_check(sizes):
mean_val = mean(sizes)
print('mean of # images :', mean_val)
for i in sizes:
if abs(i - mean_val) > 0.1 * mean_val:
print("Too much or less images")
else:
print("Well balanced", i)
def load_and_display_pickle(datasets, sample_size, title=None):
fig = plt.figure()
if title: fig.suptitle(title, fontsize=16, fontweight='bold')
num_of_images = []
for pickle_file in datasets:
with open(pickle_file, 'rb') as f:
data = pickle.load(f)
print('Total images in', pickle_file, ':', len(data))
for index, image in enumerate(data):
if index == sample_size: break
ax = fig.add_subplot(len(datasets), sample_size, sample_size * datasets.index(pickle_file) +
index + 1)
ax.imshow(image)
ax.set_axis_off()
ax.imshow(image)
num_of_images.append(len(data))
balance_check(num_of_images)
plt.show()
return num_of_images
test_labels = generate_fake_label(load_and_display_pickle(test_datasets, 10, 'Test Datasets'))
train_labels = generate_fake_label(load_and_display_pickle(train_datasets, 10, 'Train Datasets'))
plot_balance()
# 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[ ]:
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)
# Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.
# In[ ]:
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!
#
# ---
def display_dataset(data, title, sample_size):
fig = plt.figure()
fig.suptitle(title, fontsize=16, fontweight='bold')
for index, image in enumerate(data):
if index == sample_size * 10: break
ax = fig.add_subplot(10, sample_size, index + 1)
ax.imshow(image)
ax.set_axis_off()
ax.imshow(image)
plt.show()
def plot_sample_dataset(dataset, labels, title):
plt.suptitle(title, fontsize=16, fontweight='bold')
items = random.sample(range(len(labels)), 12)
for i, item in enumerate(items):
plt.subplot(3, 4, i + 1)
plt.axis('off')
plt.title(chr(ord('A') + labels[item]))
plt.imshow(dataset[item])
plt.show()
plot_sample_dataset(train_dataset, train_labels, 'train dataset suffled')
plot_sample_dataset(valid_dataset, valid_labels, 'valid dataset suffled')
plot_sample_dataset(test_dataset, test_labels, 'test dataset suffled')
plot_balance()
# Finally, let's save the data for later reuse:
# In[ ]:
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[ ]:
statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)
# ---
# 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.
# ---
# Reference：https://github.com/Arn-O/udacity-deep-learning/blob/master/1_notmnist.ipynb
import hashlib
def extract_overlap_hash_where(dataset_1, dataset_2):
dataset_hash_1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])
dataset_hash_2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])
overlap = {}
for i, hash1 in enumerate(dataset_hash_1):
duplicates = np.where(dataset_hash_2 == hash1)
if len(duplicates[0]):
overlap[i] = duplicates[0]
return overlap
def display_overlap(overlap, source_dataset, target_dataset):
overlap = {k: v for k, v in overlap.items() if len(v) >= 3}
item = random.choice(list(overlap.keys()))
imgs = np.concatenate(([source_dataset[item]], target_dataset[overlap[item][0:7]]))
plt.suptitle(item)
for i, img in enumerate(imgs):
plt.subplot(2, 4, i + 1)
plt.axis('off')
plt.imshow(img)
plt.show()
def sanitize(dataset_1, dataset_2, labels_1):
dataset_hash_1 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_1])
dataset_hash_2 = np.array([hashlib.sha256(img).hexdigest() for img in dataset_2])
overlap = [] # list of indexes
for i, hash1 in enumerate(dataset_hash_1):
duplicates = np.where(dataset_hash_2 == hash1)
if len(duplicates[0]):
overlap.append(i)
return np.delete(dataset_1, overlap, 0), np.delete(labels_1, overlap, None)
overlap_test_train = extract_overlap_hash_where(test_dataset, train_dataset)
print('Number of overlaps:', len(overlap_test_train.keys()))
display_overlap(overlap_test_train, test_dataset, train_dataset)
test_dataset_sanit, test_labels_sanit = sanitize(test_dataset, train_dataset, test_labels)
print('Overlapping images removed from test_dataset: ', len(test_dataset) - len(test_dataset_sanit))
valid_dataset_sanit, valid_labels_sanit = sanitize(valid_dataset, train_dataset, valid_labels)
print('Overlapping images removed from valid_dataset: ', len(valid_dataset) - len(valid_dataset_sanit))
print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_labels_sanit.shape, valid_labels_sanit.shape)
print('Testing:', test_dataset_sanit.shape, test_labels_sanit.shape)
pickle_file_sanit = 'notMNIST_sanit.pickle'
try:
f = open(pickle_file_sanit, 'wb')
save = {
'train_dataset': train_dataset,
'train_labels': train_labels,
'valid_dataset': valid_dataset_sanit,
'valid_labels': valid_labels_sanit,
'test_dataset': test_dataset_sanit,
'test_labels': test_labels_sanit,
}
pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
f.close()
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
statinfo = os.stat(pickle_file_sanit)
print('Compressed pickle size:', statinfo.st_size)
# ---
# 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!
#
# ---
def disp_sample_dataset(dataset, labels, title=None):
fig = plt.figure()
if title: fig.suptitle(title, fontsize=16, fontweight='bold')
items = random.sample(range(len(labels)), 8)
for i, item in enumerate(items):
plt.subplot(2, 4, i + 1)
plt.axis('off')
plt.title(chr(ord('A') + labels[item]))
plt.imshow(dataset[item])
plt.show()
def train_and_predict(sample_size):
regr = LogisticRegression()
X_train = train_dataset[:sample_size].reshape(sample_size, 784)
y_train = train_labels[:sample_size]
regr.fit(X_train, y_train)
X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)
y_test = test_labels
pred_labels = regr.predict(X_test)
print('Accuracy:', regr.score(X_test, y_test), 'when sample_size=', sample_size)
disp_sample_dataset(test_dataset, pred_labels, 'sample_size=' + str(sample_size))
for sample_size in [50, 100, 1000, 5000, len(train_dataset)]:
train_and_predict(sample_size)
