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.
More of Python Keras
- How do I use Python and Keras to create a tutorial?
- How do I use zero padding in Python Keras?
- How to load a model in Python Keras?
- How do Python Keras and TensorFlow compare in developing machine learning models?
- How do I uninstall Keras from my Python environment?
- How do I use keras.utils.to_categorical in Python?
- How do I use the to_categorical function in Python Keras?
- How can I use the Adam optimizer in TensorFlow?
- How do I use Python Keras to create a regression example?
- How can I use Python Keras to create a neural network with zero hidden layers?
See more codes...