mnist-get-data.py

import tensorflow as tf
import numpy as np

#For 3D Plot
import pandas as pd
import plotly.express as px

#For PCA
from sklearn.decomposition import PCA

#For t-SNE
from sklearn.manifold import TSNE

#For UMAP
import umap

# Prepare MNIST data.
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)
x_train, x_test = x_train / 255., x_test / 255.

#reshape to (60000, 784) and (10000, 784)
x_train_shape = x_train.shape
x_train = x_train.reshape(x_train_shape[0], x_train_shape[1] * x_train_shape[2])
x_test_shape = x_test.shape
x_test = x_test.reshape(x_test_shape[0], x_test_shape[1] * x_test_shape[2])

#reduce to 1000
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]