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PCA 重建 Fashion_mnist 数据集


import tensorflow as tf
from tensorflow import keras
import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from PIL import Image

读取数据

(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
print(x_train.shape, y_train.shape)

(60000, 28, 28) (60000,)

# [60000, 28, 28] => [60000, 28 * 28]
x_train_reshape = x_train.reshape(-1, 28 * 28)
print(x_train_reshape.shape)

(60000, 784)

def printImage(images):
plt.figure(figsize=(10, 10))
for i in range(100):
plt.subplot(10,10,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(images[i], cmap=plt.cm.binary)

# 保留图片 95% 的特征
pca = PCA(0.95)
pca.fit(x_train_reshape)

PCA(copy=True, iterated_power='auto', n_components=0.95, random_state=None,
svd_solver='auto', tol=0.0, whiten=False)

# 需要 压缩到 187 个特征
pca.n_components_

187

# 提取图片主成分
x_train_reduction = pca.transform(x_train_reshape)
x_train_reduction.shape

(60000, 187)

# 由提取后的主成分还原图片
x_train_inverse = pca.inverse_transform(x_train_reduction)
# [60000, 28 * 28] => [60000, 28, 28]
x_train_inverse = x_train_inverse.reshape(-1, 28, 28)

# 输入的前 50 张+重建的前 50 张图片合并
x_concat = np.vstack([x_train[:50], x_train_inverse[:50]])
x_concat.shape

(100, 28, 28)

# x_concat.dtype='int32'

printImage(x_concat)
# 上面 5 行是原始图片, 下面 5 行是经过 PCA 提取主成分还原后的图片

PCA 重建 Fashion_mnist 数据集_读取数据


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