Recognizing hand-written digits of sklean

由来

https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py

An example showing how the scikit-learn can be used to recognize images of hand-written digits.

This example is commented in the tutorial section of the user manual.

• 
• 

Out:

Classification report for classifier SVC(gamma=0.001):
              precision    recall  f1-score   support

           0       1.00      0.99      0.99        88
           1       0.99      0.97      0.98        91
           2       0.99      0.99      0.99        86
           3       0.98      0.87      0.92        91
           4       0.99      0.96      0.97        92
           5       0.95      0.97      0.96        91
           6       0.99      0.99      0.99        91
           7       0.96      0.99      0.97        89
           8       0.94      1.00      0.97        88
           9       0.93      0.98      0.95        92

    accuracy                           0.97       899
   macro avg       0.97      0.97      0.97       899
weighted avg       0.97      0.97      0.97       899


Confusion matrix:
[[87  0  0  0  1  0  0  0  0  0]
 [ 0 88  1  0  0  0  0  0  1  1]
 [ 0  0 85  1  0  0  0  0  0  0]
 [ 0  0  0 79  0  3  0  4  5  0]
 [ 0  0  0  0 88  0  0  0  0  4]
 [ 0  0  0  0  0 88  1  0  0  2]
 [ 0  1  0  0  0  0 90  0  0  0]
 [ 0  0  0  0  0  1  0 88  0  0]
 [ 0  0  0  0  0  0  0  0 88  0]
 [ 0  0  0  1  0  1  0  0  0 90]]

Code

https://github.com/fanqingsong/code_snippet/blob/master/sklearn/recognize_hand_written_digits.ipynb

print(__doc__)

# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# License: BSD 3 clause

# Standard scientific Python imports
import matplotlib.pyplot as plt

# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix


# The digits dataset
digits = datasets.load_digits()

# The data that we are interested in is made of 8x8 images of digits, let's
# have a look at the first 4 images, stored in the `images` attribute of the
# dataset.  If we were working from image files, we could load them using
# matplotlib.pyplot.imread.  Note that each image must have the same size. For these
# images, we know which digit they represent: it is given in the 'target' of
# the dataset.
_, axes = plt.subplots(2, 4)
images_and_labels = list(zip(digits.images, digits.target))
for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):
    ax.set_axis_off()
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    ax.set_title('Training: %i' % label)

    
# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
print("----------- images shape:")
print(digits.images.shape)

data = digits.images.reshape((n_samples, -1))
print("----------- data shape:")
print(data.shape)



# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)

# Split data into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(
    data, digits.target, test_size=0.5, shuffle=True)

# We learn the digits on the first half of the digits
classifier.fit(X_train, y_train)



# Now predict the value of the digit on the second half:
predicted = classifier.predict(X_test)

# display some predicted instance
images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
for ax, (image, prediction) in zip(axes[1, :], images_and_predictions[:4]):
    ax.set_axis_off()
    ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    ax.set_title('Prediction: %i' % prediction)

    
print("Classification report for classifier %s:
%s
"
      % (classifier, metrics.classification_report(y_test, predicted)))

disp = metrics.plot_confusion_matrix(classifier, X_test, y_test)
disp.figure_.suptitle("Confusion Matrix")

print("Confusion matrix:
%s" % disp.confusion_matrix)

cm = confusion_matrix(y_test, predicted, normalize="true")
print("confustion matrix with normalize=true")
print(cm)



plt.show()

其中将 8*8的二维矩阵 转变为 1维打印

----------- images shape:
(1797, 8, 8)
----------- data shape:
(1797, 64)

概率混淆矩阵

confustion matrix with normalize=true
[[1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.         1.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.         0.         1.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.         0.         0.         0.98780488 0.         0.
  0.         0.01219512 0.         0.        ]
 [0.         0.         0.         0.         0.99019608 0.
  0.         0.         0.00980392 0.        ]
 [0.         0.         0.         0.         0.         0.97894737
  0.01052632 0.         0.         0.01052632]
 [0.         0.         0.         0.         0.         0.
  1.         0.         0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         1.         0.         0.        ]
 [0.         0.03157895 0.         0.         0.         0.
  0.         0.         0.96842105 0.        ]
 [0.         0.         0.         0.01136364 0.         0.02272727
  0.         0.01136364 0.01136364 0.94318182]]