tensorflow

import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test,y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0,x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)
=======================================================

Epoch 1/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2986 - accuracy: 0.9122
Epoch 2/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.1430 - accuracy: 0.9574
Epoch 3/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.1062 - accuracy: 0.9678
Epoch 4/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0876 - accuracy: 0.9727
Epoch 5/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0763 - accuracy: 0.9755
313/313 - 0s - loss: 0.0818 - accuracy: 0.9769