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pca.py
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pca.py
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from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
(x_train, _), (x_test, y_test) = mnist.load_data()
# normalize
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# flatten
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
def find_first_digit(d):
return next(i for i in range(len(y_test)) if y_test[i] == d)
indices = [find_first_digit(d) for d in range(10)]
from sklearn.decomposition import PCA
pca = PCA(36)
encoded_imgs = pca.fit(x_train).transform(x_test)
decoded_imgs = pca.inverse_transform(encoded_imgs)
n = len(indices) # how many digits we will display
fig = plt.figure(figsize=(20, 4))
rows = 3
side = 6
for i in range(n):
ax = plt.subplot(rows, n, i + 1)
plt.imshow(x_test[indices[i]].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display encoding
ax = plt.subplot(rows, n, i + 1 + n)
plt.imshow(((encoded_imgs[indices[i]] + 1) / 2).reshape(side, side))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(rows, n, i + 1 + 2 * n)
plt.imshow(decoded_imgs[indices[i]].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.savefig("/home/fdavidcl/Documentos/research/publications/2017/ReviewAutoencoders/examples/pca-36.pdf", pad_inches = 0)