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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).
I tried help(tf.keras.Model.call) and it shows that
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.
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.
In a nutshell, I don't know what is the influence of this argument. And what if I missed this argument when training?
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keras. Model . To call a model on an input, always use the __call__() method, i.e. model(inputs) , which relies on the underlying call() method. Input tensor, or dict/list/tuple of input tensors.
trainable to False moves all the layer's weights from trainable to non-trainable. This is called "freezing" the layer: the state of a frozen layer won't be updated during training (either when training with fit() or when training with any custom loop that relies on trainable_weights to apply gradient updates).
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). Help on function call in module tensorflow.python.keras.engine.network: call (self, inputs, training=None, mask=None) Calls the model on new inputs.
Description: Complete guide to writing low-level training & evaluation loops. Keras provides default training and evaluation loops, fit () and evaluate () . Their usage is covered in the guide Training & evaluation with the built-in methods.
(optionally) 3D visualizations of the embedding spaces learned by your Embedding layers If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line: The easiest way to use TensorBoard with a Keras model and the fit () method is the TensorBoard callback.
metrics: List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a tf.keras.metrics.Metric instance. See tf.keras.metrics.
Some neural network layers behave differently during training and inference, for example Dropout and BatchNormalization layers. For example
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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Training indicating whether the layer should behave in training mode or in inference mode.
training=True: The layer will normalize its inputs using the mean and variance of the current batch of inputs.
training=False: The layer will normalize its inputs using the mean and variance of its moving statistics, learned during training.
Usually in inference mode training=False, but in some networks such as pix2pix_cGAN At both times of inference and training, training=True.
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