DL之RBM：(sklearn自帶數據集為1797個樣本*64個特征+5倍數據集)深度學習之BRBM模型學習+LR進行分類實現手寫數字圖識別

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實現代碼

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實現代碼

from __future__ import print_function print(__doc__)import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import convolve from sklearn import linear_model, datasets, metrics from sklearn.cross_validation import train_test_split from sklearn.neural_network import BernoulliRBM from sklearn.pipeline import Pipeline def nudge_dataset(X, Y): direction_vectors = [[[0, 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, 1, 0]]]shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',weights=w).ravel() X = np.concatenate([X] + [np.apply_along_axis(shift, 1, X, vector) for vector in direction_vectors]) Y = np.concatenate([Y for _ in range(5)], axis=0) return X, Ydigits = datasets.load_digits() X = np.asarray(digits.data, 'float32') X, Y = nudge_dataset(X, digits.target) X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.2,random_state=0) logistic = linear_model.LogisticRegression() rbm = BernoulliRBM(random_state=0, verbose=True)classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)]) rbm.learning_rate = 0.06 rbm.n_iter = 20 # More components tend to give better prediction performance, but larger fitting time rbm.n_components = 100 logistic.C = 6000.0classifier.fit(X_train, Y_train) logistic_classifier = linear_model.LogisticRegression(C=100.0) logistic_classifier.fit(X_train, Y_train)print() print("Logistic regression using RBM features:\n%s\n" % (metrics.classification_report(Y_test,classifier.predict(X_test) )))print("Logistic regression using raw pixel features:\n%s\n" % ( metrics.classification_report( Y_test, logistic_classifier.predict(X_test))))plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(rbm.components_): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('100 components extracted by RBM', fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)plt.show()

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DL之RBM：(sklearn自帶數據集為1797個樣本*64個特征+5倍數據集)深度學習之BRBM模型學習+LR進行分類實現手寫數字圖識別

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