147 lines
4.2 KiB
Python
147 lines
4.2 KiB
Python
"""
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==============================================================
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Restricted Boltzmann Machine features for digit classification
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==============================================================
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For greyscale image data where pixel values can be interpreted as degrees of
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blackness on a white background, like handwritten digit recognition, the
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Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
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<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear
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feature extraction.
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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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# Generate data
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# -------------
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#
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# In order to learn good latent representations from a small dataset, we
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# artificially generate more labeled data by perturbing the training data with
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# linear shifts of 1 pixel in each direction.
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import numpy as np
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from scipy.ndimage import convolve
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import minmax_scale
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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], [0, 0, 0], [0, 0, 0]],
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[[0, 0, 0], [1, 0, 0], [0, 0, 0]],
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[[0, 0, 0], [0, 0, 1], [0, 0, 0]],
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[[0, 0, 0], [0, 0, 0], [0, 1, 0]],
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]
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def shift(x, w):
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return convolve(x.reshape((8, 8)), mode="constant", weights=w).ravel()
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X = np.concatenate(
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[X] + [np.apply_along_axis(shift, 1, X, vector) for vector in direction_vectors]
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)
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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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X, y = datasets.load_digits(return_X_y=True)
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X = np.asarray(X, "float32")
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X, Y = nudge_dataset(X, y)
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X = minmax_scale(X, feature_range=(0, 1)) # 0-1 scaling
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
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# %%
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# Models definition
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# -----------------
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#
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# We build a classification pipeline with a BernoulliRBM feature extractor and
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# a :class:`LogisticRegression <sklearn.linear_model.LogisticRegression>`
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# classifier.
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from sklearn import linear_model
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from sklearn.neural_network import BernoulliRBM
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from sklearn.pipeline import Pipeline
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logistic = linear_model.LogisticRegression(solver="newton-cg", tol=1)
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rbm = BernoulliRBM(random_state=0, verbose=True)
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rbm_features_classifier = Pipeline(steps=[("rbm", rbm), ("logistic", logistic)])
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# %%
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# Training
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# --------
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#
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# The hyperparameters of the entire model (learning rate, hidden layer size,
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# regularization) were optimized by grid search, but the search is not
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# reproduced here because of runtime constraints.
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from sklearn.base import clone
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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 = 10
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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
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# Training RBM-Logistic Pipeline
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rbm_features_classifier.fit(X_train, Y_train)
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# Training the Logistic regression classifier directly on the pixel
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raw_pixel_classifier = clone(logistic)
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raw_pixel_classifier.C = 100.0
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raw_pixel_classifier.fit(X_train, Y_train)
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# %%
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# Evaluation
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# ----------
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from sklearn import metrics
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Y_pred = rbm_features_classifier.predict(X_test)
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print(
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"Logistic regression using RBM features:\n%s\n"
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% (metrics.classification_report(Y_test, Y_pred))
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)
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# %%
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Y_pred = raw_pixel_classifier.predict(X_test)
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print(
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"Logistic regression using raw pixel features:\n%s\n"
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% (metrics.classification_report(Y_test, Y_pred))
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)
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# %%
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# The features extracted by the BernoulliRBM help improve the classification
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# accuracy with respect to the logistic regression on raw pixels.
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# %%
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# Plotting
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# --------
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import matplotlib.pyplot as plt
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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, 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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plt.show()
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