tensorflow 2.0 随机梯度下降 之 多输出感知机梯度

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多输出感知机

x=tf.random.normal([1,3])

w=tf.ones([3,2])

b=tf.ones([2])

y = tf.constant([0, 1])


with tf.GradientTape() as tape:

	tape.watch([w, b])
	logits = tf.sigmoid(x@w+b) 
	loss = tf.reduce_mean(tf.losses.MSE(y, logits))

grads = tape.gradient(loss, [w, b])
print('w grad:', grads[0])

print('b grad:', grads[1])

w grad: tf.Tensor(
[[ 0.0929644  -0.01531836]
 [ 0.01146083 -0.00188848]
 [-0.02067768  0.0034072 ]], shape=(3, 2), dtype=float32)
b grad: tf.Tensor([ 0.10427201 -0.01718159], shape=(2,), dtype=float32)