import tensorflow as tf 
from tensorflow import keras
 
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
 
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
 
x_train = x_train / 255.0
x_test = x_test / 255.0
 
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dropout(0.2), 
keras.layers.Dense(10),
])
 
model. compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
 
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
 
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print('Test accuracy:', test_acc) 

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