MNIST with KERAS

IDE 

pyCharm CE

Source 

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#Load data

(X_train, y_train),(X_test, y_test) = tf.keras.datasets.mnist.load_data()

#plot with 3 rows and 3 columns grid, and locate each subplot using the sequence number 1,2 ...plt.subplot(331)
plt.imshow(X_train[0],cmap=plt.get_cmap('gray'))
plt.subplot(332)
plt.imshow(X_train[1],cmap=plt.get_cmap('gray'))
plt.subplot(333)
plt.imshow(X_train[2],cmap=plt.get_cmap('gray'))
plt.subplot(334)
plt.imshow(X_train[3],cmap=plt.get_cmap('gray'))
plt.subplot(335)
plt.imshow(X_train[4],cmap=plt.get_cmap('gray'))
plt.subplot(336)
plt.imshow(X_train[5],cmap=plt.get_cmap('gray'))
plt.subplot(337)
plt.imshow(X_train[6],cmap=plt.get_cmapå('gray'))
plt.subplot(338)
plt.imshow(X_train[7],cmap=plt.get_cmap('gray'))
plt.subplot(339)
plt.imshow(X_train[8],cmap=plt.get_cmap('gray'))
plt.show()

Output