DL之RBM:(sklearn自带数据集为1797个样本*64个特征+5倍数据集)深度学习之BRBM模型学习+LR进行分类实现手写数字图识别
目录
- 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, Y
-
- digits = 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 = 6000.0
-
- classifier.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自带数据集为1797个样本*64个特征+5倍数据集)深度学习之BRBM模型学习+LR进行分类实现手写数字图识别
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