Pca VS AutoEncoders

Thursday, Nov 25, 2021

PCA import numpy as np import sklearn import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.datasets import fashion_mnist import seaborn as sns import os import gzip import sys # The number of components for pca N_COMP = 100 #@param {type:"integer"} #Load data: (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data() #Design matrix print('Design matrix size: {}'.format(X_train.shape)) Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz 32768/29515 [=================================] - 0s 0us/step 40960/29515 [=========================================] - 0s 0us/step Downloading data from https://storage.
@ rushi
5 minutes read

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– 2020 年 09 月 09 日更新

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