调用TensorFlow Keras模型时，``training = True''是什么意思? [英] What does `training=True` mean when calling a TensorFlow Keras model?

问题描述

在TensorFlow的官方文档中，当在训练循环中调用Keras模型(例如logits = mnist_model(images, training=True))时，它们总是通过training=True.

In TensorFlow's offcial documentations, they always pass training=True when calling a Keras model in a training loop, for example, logits = mnist_model(images, training=True).

我尝试了help(tf.keras.Model.call)，它表明了

Help on function call in module tensorflow.python.keras.engine.network:

call(self, inputs, training=None, mask=None)
    Calls the model on new inputs.

    In this case `call` just reapplies
    all ops in the graph to the new inputs
    (e.g. build a new computational graph from the provided inputs).

    Arguments:
        inputs: A tensor or list of tensors.
        training: Boolean or boolean scalar tensor, indicating whether to run
          the `Network` in training mode or inference mode.
        mask: A mask or list of masks. A mask can be
            either a tensor or None (no mask).

    Returns:
        A tensor if there is a single output, or
        a list of tensors if there are more than one outputs.

它表示training是布尔或布尔标量张量，指示是在 training模式还是推理模式中运行Network.但是我没有找到有关这两种模式的任何信息.

It says that training is a Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode. But I didn't find any information about this two modes.

简而言之，我不知道该论点的影响.而且如果我在训练时错过了这个论点怎么办?

In a nutshell, I don't know what is the influence of this argument. And what if I missed this argument when training?

推荐答案

某些神经网络层在训练和推理期间的行为有所不同，例如Dropout和BatchNormalization层.例如

Some neural network layers behave differently during training and inference, for example Dropout and BatchNormalization layers. For example

• 在训练期间，辍学将随机辍学单位，并相应地扩大剩余单位的激活范围.
• 在推断过程中，它什么也不做(因为您通常不希望在这里丢弃单元的随机性).

training参数使图层知道应采用的两个路径"中的哪一个.如果您对此设置不正确，则您的网络可能无法正常运行.

The training argument lets the layer know which of the two "paths" it should take. If you set this incorrectly, your network might not behave as expected.

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