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What is batch normalization in keras?

Furthermore, batch normalization regularizes the network such that it is easier to generalize, and it is thus unnecessary to use dropout to mitigate overfitting. Right after calculating the linear function using say, the Dense () or Conv2D () in Keras, we use BatchNormalization () which calculates the linear function in a layer and then we add ...

What is Keras functional API?

The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. The main idea is that a deep learning model is usually a directed acyclic graph (DAG) of layers.

What are keras preprocessing layers?

The Keras preprocessing layers allow you to build Keras-native input processing pipelines, which can be used as independent preprocessing code in non-Keras workflows, combined directly with Keras models, and exported as part of a Keras SavedModel.

What is batch normalization?

BatchNormalization class. Layer that normalizes its inputs. Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1. Importantly, batch normalization works differently during training and during inference. During training (i.e. when using fit () or when calling ...


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