Ex 2: Restricted Boltzmann Machine features for digit classification

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​http://scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py​
此範例將使用BernoulliRBM特徵選取方法，提升手寫數字識別的精確率，伯努利限制玻爾茲曼機器模型（`BernoulliRBM
`）將可以對數據做有效的非線性 特徵提取的處理。 為了讓此模型訓練出來更為強健，將輸入的圖檔，分別做上左右下，一像素的平移，用以增加更多訓練資料， 訓練網路的參數是使用grid search演算法，但此訓練太耗費時間，因此不再這重現，。 此範例結果將比較， 1.使用原本的像素值做的邏輯回歸 2.使用BernoulliRBM做特徵選取的邏輯回歸 結果將顯示:使用BernoulliRBM將可以提升分類的準確度。
(一)引入函式庫與資料
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from __future__ import print_function
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​
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print(__doc__)
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​
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# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve
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# License: BSD
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​
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import numpy as np
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import matplotlib.pyplot as plt
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​
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from scipy.ndimage import convolve
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from sklearn import linear_model, datasets, metrics
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import BernoulliRBM
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from sklearn.pipeline import Pipeline
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(二)資料前處理、讀取資料、選取模型
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def nudge_dataset(X, Y):
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    """
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    此副函式是用來將輸入資料的數字圖形，分別做上左右下一像素的平移，目的是製造更多的訓練資料讓模型訓練出來更強健
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    """
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    direction_vectors = [
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        [[0, 1, 0],
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         [0, 0, 0],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [1, 0, 0],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [0, 0, 1],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [0, 0, 0],
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         [0, 1, 0]]]
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​
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    shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',
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                                  weights=w).ravel()
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    X = np.concatenate([X] +
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                       [np.apply_along_axis(shift, 1, X, vector)
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                        for vector in direction_vectors])
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    Y = np.concatenate([Y for _ in range(5)], axis=0)
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    return X, Y
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​
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# Load Data
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digits = datasets.load_digits()
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X = np.asarray(digits.data, 'float32')
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X, Y = nudge_dataset(X, digits.target)
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X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 將灰階影像降尺度降到[0,1]
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# 將資料切割成訓練集與測試集
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
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                                                    test_size=0.2,
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                                                    random_state=0)
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​
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# Models we will use
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logistic = linear_model.LogisticRegression()
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rbm = BernoulliRBM(random_state=0, verbose=True)
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​
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classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])
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(三)設定模型參數與訓練模型
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# 參數選擇需使用cross-validation去比較
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# 此參數是使用GridSearchCV找出來的. Here we are not performing cross-validation to save time.
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#GridSratch 就是將參數設定好，跑過全部參數後去找結果最好的一組參數
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rbm.learning_rate = 0.06
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rbm.n_iter = 20
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#.n_components = 100 表示隱藏層單元為100，即表示萃取出100個特徵，特徵萃取的越多準確率會越高，但越耗時間
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rbm.n_components = 100
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logistic.C = 6000.0
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​
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# Training RBM-Logistic Pipeline
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classifier.fit(X_train, Y_train)
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​
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# Training Logistic regression
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logistic_classifier = linear_model.LogisticRegression(C=100.0)
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logistic_classifier.fit(X_train, Y_train)
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(四)評估模型的分辨準確率
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print()
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print("Logistic regression using RBM features:\n%s\n" % (
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    metrics.classification_report(
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        Y_test,
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        classifier.predict(X_test))))
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​
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print("Logistic regression using raw pixel features:\n%s\n" % (
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    metrics.classification_report(
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        Y_test,
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        logistic_classifier.predict(X_test))))
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圖1:使用RBM演算法後準確率為0.95
圖2:不使用任何特徵選取方法做的做的邏輯回歸準確率0.77
(五)畫出100個RBM萃取出的特徵
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plt.figure(figsize=(4.2, 4))
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for i, comp in enumerate(rbm.components_):
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    plt.subplot(10, 10, i + 1)
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    plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r,
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               interpolation='nearest')
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    plt.xticks(())
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    plt.yticks(())
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plt.suptitle('100 components extracted by RBM', fontsize=16)
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plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
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​
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plt.show()
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圖3:使用RBM演算法，尋找出來的特徵
(六)完整程式碼
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from __future__ import print_function
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​
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print(__doc__)
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​
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# Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve
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# License: BSD
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​
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import numpy as np
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import matplotlib.pyplot as plt
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​
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from scipy.ndimage import convolve
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from sklearn import linear_model, datasets, metrics
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import BernoulliRBM
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from sklearn.pipeline import Pipeline
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​
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​
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###############################################################################
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# Setting up
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​
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def nudge_dataset(X, Y):
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    """
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    This produces a dataset 5 times bigger than the original one,
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    by moving the 8x8 images in X around by 1px to left, right, down, up
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    """
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    direction_vectors = [
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        [[0, 1, 0],
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         [0, 0, 0],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [1, 0, 0],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [0, 0, 1],
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         [0, 0, 0]],
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​
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        [[0, 0, 0],
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         [0, 0, 0],
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         [0, 1, 0]]]
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​
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    shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant',
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                                  weights=w).ravel()
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    X = np.concatenate([X] +
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                       [np.apply_along_axis(shift, 1, X, vector)
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                        for vector in direction_vectors])
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    Y = np.concatenate([Y for _ in range(5)], axis=0)
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    return X, Y
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​
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# Load Data
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digits = datasets.load_digits()
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X = np.asarray(digits.data, 'float32')
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X, Y = nudge_dataset(X, digits.target)
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X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 0-1 scaling
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​
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
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                                                    test_size=0.2,
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                                                    random_state=0)
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​
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# Models we will use
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logistic = linear_model.LogisticRegression()
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rbm = BernoulliRBM(random_state=0, verbose=True)
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​
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classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)])
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​
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###############################################################################
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# Training
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​
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# Hyper-parameters. These were set by cross-validation,
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# using a GridSearchCV. Here we are not performing cross-validation to
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# save time.
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rbm.learning_rate = 0.06
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rbm.n_iter = 20
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# More components tend to give better prediction performance, but larger
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# fitting time
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rbm.n_components = 100
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logistic.C = 6000.0
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​
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# Training RBM-Logistic Pipeline
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classifier.fit(X_train, Y_train)
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​
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# Training Logistic regression
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logistic_classifier = linear_model.LogisticRegression(C=100.0)
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logistic_classifier.fit(X_train, Y_train)
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​
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###############################################################################
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# Evaluation
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​
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print()
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print("Logistic regression using RBM features:\n%s\n" % (
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    metrics.classification_report(
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        Y_test,
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        classifier.predict(X_test))))
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​
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print("Logistic regression using raw pixel features:\n%s\n" % (
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    metrics.classification_report(
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        Y_test,
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        logistic_classifier.predict(X_test))))
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​
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###############################################################################
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# Plotting
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​
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plt.figure(figsize=(4.2, 4))
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for i, comp in enumerate(rbm.components_):
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    plt.subplot(10, 10, i + 1)
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    plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r,
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               interpolation='nearest')
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    plt.xticks(())
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    plt.yticks(())
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plt.suptitle('100 components extracted by RBM', fontsize=16)
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plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
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​
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plt.show()
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Ex 1: Visualization of MLP weights on MNIST
Ex 3: Compare Stochastic learning strategies for MLPClassifier
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