<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 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
Step2:  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.
Step3:  Extract the dataset from the compressed .tar.gz file.
This should give you a set of directories, labelled A through J.
Step4:  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
Step6:  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.
Step7:  Problem 2
Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint
Step8:  Question
Step9:  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.
Step10:  Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.
Step11:  Problem 4
Convince yourself that the data is still good after shuffling!
Step12:  Finally, let's save the data for later reuse
Step14:  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
Step15:  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
Step16:  Question
<ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
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
Explanation: 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.
End of explanation
url = 'http://yaroslavvb.com/upload/notMNIST/'
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):
    filename, _ = urlretrieve(url + filename, filename)
  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)
print(type(train_filename))
Explanation: 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.
End of explanation
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)
Explanation: Extract the dataset from the compressed .tar.gz file.
This should give you a set of directories, labelled A through J.
End of explanation
#Problem 1, Tong's solution -> done
def display_image(folder_index = 0, image_index = 0):
    try:
        sample_folder = train_folders[folder_index]
        image_files = os.listdir(sample_folder)
        sample_image = os.path.join(sample_folder, image_files[image_index])
        print('Displaying image: ', sample_image)
        display(Image(filename = sample_image ))
    except:
        print('Indices out of bound.')
display_image(1, 5)
Explanation: 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.
End of explanation
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)
  image_index = 0
  print(folder)
  for image in os.listdir(folder):
    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[image_index, :, :] = image_data
      image_index += 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
    
  num_images = image_index
  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)
Explanation: 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.
End of explanation
#Problem 2, Tong's solution -> done
def display_nd_image(folder_index = 0, image_index = 0):
    try:
        folder = train_datasets[folder_index]
        print("Display image in folder: ", folder)
        with open(folder, 'rb') as f:
            sample_dataset = pickle.load(f)
        img = sample_dataset[image_index, :, :]
        plt.imshow(img, cmap = "Greys")
        plt.show()
    except:
        print('Something is wrong.')
display_nd_image(1, 5)
Explanation: 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.
End of explanation
#Problem 3, Tong's solution -> done
print(train_datasets)
sizes = []
for dataset in train_datasets:
    with open(dataset, 'rb') as f:
        data = pickle.load(f)
        sizes.append(data.shape[0])
print("The samples sizes for each class are: ")
print(sizes)
print("Average: ", np.average(sizes))
print("Stdev: ", np.std(sizes))
print("Sum: ", np.sum(sizes))
#Very balanced
    
Explanation: Question: why does the image look weird?
Problem 3
Another check: we expect the data to be balanced across classes. Verify that.
End of explanation
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)
Explanation: 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.
End of explanation
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)
Explanation: Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.
End of explanation
#Problem 4, Tong's solution -> done
#Print some random images and lables from each set, see if they match
def check_data(dataset, lables, index=0):
    labelset = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I','J']
    img = dataset[index, :, :]
    label = labelset[lables[index]]
    print("Image:")
    plt.imshow(img, cmap = "Greys")
    plt.show()
    print('Lable: ', label)
    
check_data(train_dataset, train_labels, index = 1001)
check_data(valid_dataset, valid_labels, index = 11)
check_data(test_dataset, test_labels, index = 9)
#LGTM
print(train_labels[1:100])
Explanation: Problem 4
Convince yourself that the data is still good after shuffling!
End of explanation
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
statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)
Explanation: Finally, let's save the data for later reuse:
End of explanation
#Problem 5, Tong's solution -> done
#Why is there overlap??!
train_indices = train_dataset[0]
print(train_dataset.shape[0])
print(train_dataset.item(100, 27, 6))
#Brute-force checking how many rows are identical between train and valid
def overlap_rate(a_dataset, b_dataset, sample_size = 1000):
    identical_count = 0
    test_size = min(a_dataset.shape[0], sample_size)
    for i in range(test_size):
        a_record = a_dataset[i, :, :]
        for j in range(b_dataset.shape[0]):
            b_record = b_dataset[j, :, :]
            if np.array_equal(a_record, b_record):
                identical_count += 1
    print('Sample size:', str(test_size))
    print('Percent of a dataset that is overlaped in b dataset', str(identical_count*1.0/test_size)) 
overlap_rate(train_dataset, valid_dataset) #39%, surprisingly high!
overlap_rate(train_dataset, test_dataset) #58%, even higher
Optioanl questions:
-consider using np.allclose for near duplicates
-sanitized validation and test set: leave for later..
Explanation: 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.
End of explanation
%%capture
#Learn reshape
#l = np.ndarray(range(27), shape=(3, 3, 3))
a = np.arange(27).reshape((3, 3, 3))
b = a.reshape(3, 9)
print(a);
print(b);
Explanation: 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!
End of explanation
#Problem 6, Tong's solution Version 1: no tuning of hyperparameters
#Take subset of training data, reshape for regression
train_size = 1000
train = train_dataset[:train_size, :, :]
test = test_dataset.reshape(test_dataset.shape[0], image_size * image_size)
X = train.reshape(train_size, image_size * image_size)
Y = train_labels[:train_size]
#Build regression graph
logreg = LogisticRegression(C=1.0)
#Fit the model
logreg.fit(X, Y)
#Test predictions on test set
Z = logreg.predict(test)
#Evaluate
np.mean(Z == test_labels)    #Accurary 85%
#V2: tune hyperparameters with the validation set. First do this 'by hand'
valid = valid_dataset.reshape(valid_dataset.shape[0], image_size * image_size)
Cs = np.logspace(0.001, 10, num=50)
Accuracys = []
for C in Cs:
    logregC = LogisticRegression(C=C)
    logregC.fit(X, Y)
    pred = logregC.predict(valid)
    acc = np.mean(pred == valid_labels)
    Accuracys.append(acc)
Accuracys = np.array(Accuracys)
plt.plot(Cs, Accuracys)
#Looks like changing C doesn't matter all that much. Why?
Explanation: Question: is there a more elegant way to do the reshaping?
End of explanation
<END_TASK>