import tensorflow as tf 
from tensorflow import keras 
 
# Load the MNIST dataset 
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() 

# Preprocess the data 
x_train = x_train / 255.0 
x_test = x_test / 255.0 

# Define the model architecture 
model = keras.Sequential([ 
    keras.layers.Flatten(input_shape=(28, 28)), 
    keras.layers.Dense(128, activation='relu'), 
    keras.layers.Dense(10, activation='softmax') 
]) 

# Compile the model 
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) 

# Train the model 
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))