4. 课后练习-MNIST 手写训练集

课后练习3

Tasks

1. Familiarize yourself with the MNIST dataset: MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges. [http://yann.lecun.com/exdb/mnist/]
   
2. Familiarize yourself with sklearn package: scikit-learn: machine learning in Python — scikitlearn 0.24.1 documentation [scikit-learn.org]

Programming exercise

Q1. Use the fetch_openml function found in sklearn.datasets to load the mnist_784 dataset into python. This will load X and y variables for you.

• Print the dimensions of the variables returned by the function.
• Write a python script to find how many distinct values are present in y?
• Select one sample from X for each distinct y value.
• Resize each sample to represent the 28x28 pixel image.
• Display all the selected images in one diagram using subplots in matplotlib. The following code gives you an example of how to do this,

  fig = plt.figure()
  for i in range(1, 11):
  fig.add_subplot(2, 5, i)
  plt.imshow(images[i])
  plt.show()

  Solutions:

  from sklearn import datasets
  from sklearn,datasets import fetch_openml
  images,labels = fetch_openml('mnist_784',version=1, return_x_y=true, as_frame=false)
  # load 70000 28x28=784 handwriting images
  # print(images.shape)
  #>> (7000,784)

  or

  from sklearn import datasets
  digits = datasets.load_digits() #load the mnist dataset which already in sklearn
  images = digits.images #access  1797 8x8 images in mnist by print(images.shape)
  labels = digits.target #access 1797 labes 
  # print(images.shape)
  #>> (1797,8,8)

  import numpy as np
  from sklearn import datasets
  import matplotlib.pyplot as plt
  digits = datasets.load_digits() #load the mnist dataset which already in sklearn
  images = digits.images #access  1797 8x8 images in mnist by print(images.shape)
  labels = digits.target #access labels
  
  np.unique(labels) # summerize the labels
  print(np.unique(labels).shape)
  
  fig = plt.figure()
  for i in range(0,10):
      fig.add_subplot(2, 5, i+1) # creat a batch of subplot with 2 rows 5 columns
      # i means the position in the subplot
      plt.imshow(images[i])
  plt.show() # display the subplot

  ### Q2. Use sklearn to train a digit classifier.
• Split the X and y into a training set and testing set of 80-20 split.
• Train a Support Vector Machin (SVM) for classification of the digits using the training set. The following code shows how to train a model using sklearn.

  clf = svm.SVC()
  clf.fit(x_train, y_train)

• Test the model using the test set.
• Experiment with different parameter values for the SVM and see how it performs. Try changing the gamma value to be [0.0001, 0.0005, 0.001, 0.005, 0.01]
• Plot the accuracy value with respect to the change in gamma above.
• Plot the confusion matrix

  import numpy as np
  from sklearn import datasets
  import matplot as plt
  from sklearn.model_selection import train_test_split
  from sklearn import svm
  from sklearn import metrics
  
  digits = datasets.load_digits() # load the mnist dataset which already in sklearn
  data = digits.images # access  1797 8x8 images in mnist, print(images.shape)
  labels = digits.target # access 1797 labels
  
  images = data.reshape((len(data),-1)) # reshaape the 8x8 matrixes into 64x1 vectors
  
  x_train,x_test,y_train,y_test = train_test_split(images,labels, test_size = 0.2, shuffle = false) # 20% will be test set
  # x:images y:labels
  
  clf = svm.SVC() # create the svm classifier
  clf.fit(x_train, y_train) # fit the data  within vectors
  
  acc = clf.score(x_test, y_test) # do the test and retrun the accurancy
  disp = metrics.plot_confusion_matrix(clf,x_test,y_test) # add into confusion matrix
  print(acc) # print the accurancy
  sklearn.metrics.ConfusionMatrixDisplay(disp) # display the confusion matrix
  
  g_ = [0.0001,0.0005,0.001,0.005,0.01] # list of gamma
  scores = [] # list of accurancy
  for g in g_:
      clf = svm.SVC(gamma = g) # create the svm classifier,specify the gamma
      clf.fit(x_train, y_train) # fit the data  within vectors
  
      acc = clf.score(x_test, y_test) # do the test and retrun the accurancy
      scores.append(acc)
  
  
  
  print(g_) # print the accurancy
  print(scores)
  
  plt.plot(g_, scores)
  plt.show()  

```