[人工智能]WARNING:tensorflow:The dtype of the target tensor must be floating

WARNING:tensorflow:The dtype of the target tensor must be floating (e.g. tf.float32) when calling GradientTape.gradient, got tf.int32

报错代码如下：

x = tf.random.normal([1,3])
b = tf.ones([1])
w = tf.ones([3,1])
y = tf.constant([1])

with tf.GradientTape() as tape:
    tape.watch([w,b])
    prob = tf.sigmoid(x @ w + b)    
    loss = tf.reduce_mean(tf.losses.MSE(prob,y))

grads = tape.gradient(loss,[w,b])
grads[0]

报错意思是目标张量必须要是浮点类型，但得到的是int32型。
我们知道MSE的计算中存在平方，所以x_val和x_pred的顺序是没有关系的，但是tf.reduce_mean(tf.losses.MSE())要求第二位必须是tf.float32型，所有我们可以这样改：

x = tf.random.normal([1,3])
b = tf.ones([1])
w = tf.ones([3,1])
#将y改为tf.float32型即可
y = tf.constant([1.])

with tf.GradientTape() as tape:
    tape.watch([w,b])
    prob = tf.sigmoid(x @ w + b)    
    loss = tf.reduce_mean(tf.losses.MSE(prob,y))

grads = tape.gradient(loss,[w,b])
grads[0]

或者

x = tf.random.normal([1,3])
b = tf.ones([1])
w = tf.ones([3,1])
y = tf.constant([1])

with tf.GradientTape() as tape:
    tape.watch([w,b])
    prob = tf.sigmoid(x @ w + b)    
    loss = tf.reduce_mean(tf.losses.MSE(y,prob))

grads = tape.gradient(loss,[w,b])
grads[0]