Recognizing hand-written digits
""" ================================ Recognizing hand-written digits ================================ This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9. """ # Author: Gael Varoquaux# 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 ############################################################################### # Digits dataset # -------------- # # The digits dataset consists of 8x8 # pixel images of digits. The ``images`` attribute of the dataset stores # 8x8 arrays of grayscale values for each image. We will use these arrays to # visualize the first 4 images. The ``target`` attribute of the dataset stores # the digit each image represents and this is included in the title of the 4 # plots below. # # Note: if we were working from image files (e.g., 'png' files), we would load # them using :func:`matplotlib.pyplot.imread`. digits = datasets.load_digits() _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3)) for ax, image, label in zip(axes, digits.images, digits.target): ax.set_axis_off() ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest") ax.set_title("Training: %i" % label) ############################################################################### # Classification # -------------- # # To apply a classifier on this data, we need to flatten the images, turning # each 2-D array of grayscale values from shape ``(8, 8)`` into shape # ``(64,)``. Subsequently, the entire dataset will be of shape # ``(n_samples, n_features)``, where ``n_samples`` is the number of images and # ``n_features`` is the total number of pixels in each image. # # We can then split the data into train and test subsets and fit a support # vector classifier on the train samples. The fitted classifier can # subsequently be used to predict the value of the digit for the samples # in the test subset. # flatten the images n_samples = len(digits.images) data = digits.images.reshape((n_samples, -1)) # Create a classifier: a support vector classifier clf = svm.SVC(gamma=0.001) # Split data into 50% train and 50% test subsets X_train, X_test, y_train, y_test = train_test_split( data, digits.target, test_size=0.5, shuffle=False ) # Learn the digits on the train subset clf.fit(X_train, y_train) # Predict the value of the digit on the test subset predicted = clf.predict(X_test) ############################################################################### # Below we visualize the first 4 test samples and show their predicted # digit value in the title. _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3)) for ax, image, prediction in zip(axes, X_test, predicted): ax.set_axis_off() image = image.reshape(8, 8) ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest") ax.set_title(f"Prediction: {prediction}") ############################################################################### # :func:`~sklearn.metrics.classification_report` builds a text report showing # the main classification metrics. print( f"Classification report for classifier {clf}:n" f"{metrics.classification_report(y_test, predicted)}n" ) ############################################################################### # We can also plot a :ref:`confusion matrix ` of the # true digit values and the predicted digit values. metrics.plot_confusion_matrix(clf, X_test, y_test) plt.show() # disp = metrics.confusion_matrix(y_test, predicted) # disp.figure_.suptitle("Confusion Matrix") # print(f"Confusion matrix:n{disp.confusion_matrix}") # plt.show()
输出结果
figure
