tf 2.x keras: Losses,Optimizers,metrics 损失函数优化器度量学习率排程

本文详细介绍了TensorFlow 2.x中Keras模块的主要组件，包括损失函数、优化器及度量指标等内容，并对比了PyTorch的相关功能。重点讨论了CategoricalCrossentropy等损失函数、Adam等优化器及其在深度学习中的应用。

tf 2.x keras

Losses,Optimizers,metrics

https://www.tensorflow.org/api_docs/python/tf/keras/

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/

https://keras.io/api/

1.损失函数Losses：

https://www.tensorflow.org/api_docs/python/tf/keras/losses/

https://keras.io/api/losses/

CategoricalCrossentropy，Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a one_hot representation.

SparseCategoricalCrossentropy，Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers.

BinaryCrossentropy，Use this cross-entropy loss when there are only two label classes (assumed to be 0 and 1).

KLDivergence Computes Kullback-Leibler divergence loss between y_true and y_pred.

MeanAbsoluteError: Computes the mean of absolute difference between labels and predictions.

MeanAbsolutePercentageError: Computes the mean absolute percentage error between y_true and y_pred.

MeanSquaredError: Computes the mean of squares of errors between labels and predictions.

MeanSquaredLogarithmicError: Computes the mean squared logarithmic error between y_true and y_pred.

2.优化器Optimizers：

https://www.tensorflow.org/api_docs/python/tf/keras/optimizers

https://keras.io/api/optimizers/

class Adadelta: Optimizer that implements the Adadelta algorithm.
class Adagrad: Optimizer that implements the Adagrad algorithm.
class Adam: Optimizer that implements the Adam algorithm.
class Adamax: Optimizer that implements the Adamax algorithm.
class Ftrl: Optimizer that implements the FTRL algorithm.
class Nadam: Optimizer that implements the NAdam algorithm.
class RMSprop: Optimizer that implements the RMSprop algorithm.
class SGD: Gradient descent (with momentum) optimizer.
class Optimizer: Base class for Keras optimizers.