def train_one_step():
with tf.GradientTape() as tape:
a = tf.random.normal([1, 3, 1])
b = tf.random.normal([1, 3, 1])
loss = mse(a, b)
tf.print('inner tf print', loss)
print("inner py print", loss)
return loss
@tf.function
def train():
loss = train_one_step()
tf.print('outer tf print', loss)
print('outer py print', loss)
return loss
loss = train()
tf.print('outest tf print', loss)
print("outest py print", loss)
我试图更多地了解 tf.function 。我用不同的方法在四个地方打印了损失。它产生这样的结果
inner py print Tensor("mean_absolute_error/weighted_loss/value:0", shape=(), dtype=float32)
outer py print Tensor("mean_absolute_error/weighted_loss/value:0", shape=(), dtype=float32)
inner tf print 1.82858419
outer tf print 1.82858419 …