DL之RBM：基于RBM實現手寫數字圖片識別提高準確率

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設計代碼

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設計代碼

import numpy as np import matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_split from sklearn import metrics,linear_model from sklearn.neural_network import BernoulliRBM from sklearn.datasets import load_digits from sklearn.pipeline import Pipeline digits = load_digits() X = digits.data y = digits.target X -= X.min() X /= X.max() X_train, X_test, y_train, y_test = train_test_split(X, y) 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 rbm.n_components = 200 logistic.C = 6000.0 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))

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