import  tensorflow as tf
from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics


# 设置GPU使用方式
# 获取GPU列表
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    # 设置GPU为增长式占用
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True) 
  except RuntimeError as e:
    # 打印异常
    print(e)

(xs, ys),_ = datasets.mnist.load_data()
print('datasets:', xs.shape, ys.shape, xs.min(), xs.max())

batch_size = 32

xs = tf.convert_to_tensor(xs, dtype=tf.float32) / 255.
db = tf.data.Dataset.from_tensor_slices((xs,ys))
db = db.batch(batch_size).repeat(30)


model = Sequential([layers.Dense(256, activation='relu'), 
                     layers.Dense(128, activation='relu'),
                     layers.Dense(10)])
model.build(input_shape=(4, 28*28))
model.summary()

optimizer = optimizers.SGD(lr=0.01)
acc_meter = metrics.Accuracy()

for step, (x,y) in enumerate(db):

    with tf.GradientTape() as tape:
        # 打平操作，[b, 28, 28] => [b, 784]
        x = tf.reshape(x, (-1, 28*28))
        # Step1. 得到模型输出output [b, 784] => [b, 10]
        out = model(x)
        # [b] => [b, 10]
        y_onehot = tf.one_hot(y, depth=10)
        # 计算差的平方和，[b, 10]
        loss = tf.square(out-y_onehot)
        # 计算每个样本的平均误差，[b]
        loss = tf.reduce_sum(loss) / x.shape[0]


    acc_meter.update_state(tf.argmax(out, axis=1), y)

    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))


    if step % 200==0:

        print(step, 'loss:', float(loss), 'acc:', acc_meter.result().numpy())
        acc_meter.reset_states()

参考文献：

《tensorflow深度学习》