!pip install keras==2.4.3
# MNIST
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from matplotlib import pyplot as plt
from tensorflow.keras.layers.experimental.preprocessing import RandomRotation
import numpy as np
import pandas as pd
pip install tensorflow
(X_train, y_train), (X_valid, y_valid) = mnist.load_data()
array([5,0,4,1,9,2,1,3,1,4,3,5], dtype=uint8)
plt.figure (figsize=(5,5))
for k in range (12):
plt.subplot(3,4, k+1)
plt.imshow(X_train[k], cmap='Greys')
plt.axis('off')
plt.tight_layout()
plt.show()
X_train = X_train.reshape(60000, 784).astype('float32')
X_valid = X_valid.reshape(10000, 784).astype('float32')
X_train /=255
X_valid /=255
n_classes =10
y_train = keras.utils.to_categorical(y_train, n_classes)
y_valid = keras.utils.to_categorical(y_valid, n_classes)
array([0.,0.,0.,0.,0.,0.,0.,1.,0.,0.], dtype=float32)
model =Sequential()
model.add(Dense(64, activation ='sigmoid', input_shape = (784,)))
model.add(Dense(10, activation='softmax'))
