Tensorflow error(二):x and y must have the same dtype, got tf.float32 != tf.int32

原代码

    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] x.shape[0]相当于N
        loss = tf.reduce_sum(loss) / x.shape[0]

错图:

错因：除数和被除数的数据类型不一致

解决方式:将x.shape[0]通过tf.cast改变数据类型即可

• loss = tf.reduce_sum(loss) / x.shape
  改为
  X = tf.cast(x.shape[0],dtype=float)
  loss = tf.reduce_sum(loss) / X