python-kerasHow do I use Python Keras loss functions?

Keras is a popular library for deep learning in Python. It provides a variety of loss functions to optimize models. To use a Keras loss function, you must first create a model instance. Here is an example of creating a model and using the mean squared error (MSE) loss function:

from keras.models import Sequential
from keras.layers import Dense

# Create model
model = Sequential()
model.add(Dense(1, input_dim=1))

# Compile model
model.compile(optimizer='sgd', loss='mean_squared_error')

The model.compile() function takes two arguments: an optimizer and a loss function. The optimizer is used to update the weights of the model, while the loss function is used to measure the accuracy of the model. In this example, we used the sgd optimizer and the mean_squared_error loss function.

Keras provides a variety of loss functions, such as mean absolute error (MAE), binary cross entropy (BCE), categorical cross entropy (CCE), and others. Each loss function has its own set of parameters that can be used to customize the optimization process. For example, the BCE loss function takes a from_logits argument that can be used to control whether the input is a logit or a probability.

For more information, see the Keras documentation and the Keras Losses API guide.

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