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DL之RBM：(sklearn自帶數(shù)據(jù)集為1797個(gè)樣本*64個(gè)特征+5倍數(shù)據(jù)集)深度學(xué)習(xí)之BRBM模型學(xué)習(xí)+LR進(jìn)行分類實(shí)現(xiàn)手寫數(shù)字圖識(shí)別

輸出結(jié)果

?

實(shí)現(xiàn)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, 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自帶數(shù)據(jù)集為1797個(gè)樣本*64個(gè)特征+5倍數(shù)據(jù)集)深度學(xué)習(xí)之BRBM模型學(xué)習(xí)+LR進(jìn)行分類實(shí)現(xiàn)手寫數(shù)字圖識(shí)別