Category: 01. Models API

Configures the model for training. Example Arguments fit method Trains the model for a fixed number of epochs (dataset iterations). Arguments Unpacking behavior for iterator-like inputs: A common pattern is to pass an iterator like object such as a tf.data.Dataset or a keras.utils.PyDataset to fit(), which will in fact yield not only features (x) but optionally targets (y) and sample weights…

Sequential groups a linear stack of layers into a Model. Examples add method Adds a layer instance on top of the layer stack. Arguments pop method

A model grouping layers into an object with training/inference features. There are three ways to instantiate a Model: With the “Functional API” You start from Input, you chain layer calls to specify the model’s forward pass, and finally you create your model from inputs and outputs: Note: Only dicts, lists, and tuples of input tensors are supported.…