Training Evaluation With The Built In Methods Tensorflow

This guide covers training, evaluation, and prediction (inference) models when using built-in APIs for training & validation (such as Model.fit(), Model.evaluate() and Model.predict()). If you are interested in leveraging fit() while specifying your own training step function, see the Customizing what happens in fit() guide. If you are interested in writing your own training & evaluation loops from scratch, see the guide "writing a training loop from scratch". In general, whether you are using built-in loops or writing your own, model training & evaluation works strictly in the same way across every kind of Keras model -- Sequential models, models built with... This guide doesn't cover distributed training, which is covered in our guide to multi-GPU & distributed training. This guide covers training, evaluation, and prediction (inference) models when using built-in APIs for training & validation.

If you are interested in leveraging fit() while specifying your own training step function, see the Customizing what happens in fit() guide. If you are interested in writing your own training & evaluation loops from scratch, see the guide “writing a training loop from scratch”. In general, whether you are using built-in loops or writing your own, model training & evaluation works strictly in the same way across every kind of Keras model – Sequential models, models built with... This guide doesn’t cover distributed training, which is covered in our guide to multi-GPU & distributed training. Author: fchollet Date created: 2019/03/01 Last modified: 2023/06/25 Description: Writing low-level training & evaluation loops in TensorFlow. Keras provides default training and evaluation loops, fit() and evaluate().

Their usage is covered in the guide Training & evaluation with the built-in methods. If you want to customize the learning algorithm of your model while still leveraging the convenience of fit() (for instance, to train a GAN using fit()), you can subclass the Model class and implement... Now, if you want very low-level control over training & evaluation, you should write your own training & evaluation loops from scratch. This is what this guide is about. Let's train it using mini-batch gradient with a custom training loop. Evaluation metrics accesses the performance of machine learning models.

In TensorFlow, these metrics help quantify how well the model is performing during training and after it has been trained. TensorFlow provides a wide variety of built-in metrics for both classification and regression tasks, allowing you to choose the most appropriate one for your specific problem. Accuracy is one of the most widely used evaluation metrics, particularly in classification problems. It measures the percentage of correct predictions out of all predictions made. It’s suitable for balanced datasets but may not be the best choice for imbalanced datasets, as it can give misleading results. Precision is a metric used in classification tasks that measures how many of the predicted positive labels were actually positive.

It’s particularly useful when the cost of false positives is high. Recall is another important metric in classification, especially in situations where false negatives are more costly than false positives. It measures how many of the actual positive labels were correctly identified by the model. There was an error while loading. Please reload this page. Keras 提供了默认的训练与评估循环 fit() 和 evaluate()。使用内置方法进行训练和评估指南中介绍了它们的用法。

如果想要自定义模型的学习算法，同时又能利用 fit() 的便利性（例如，使用 fit() 训练 GAN），则可以将 Model 类子类化并实现自己的 train_step() 方法，此方法可在 fit() 中重复调用。自定义 fit() 的功能指南对此进行了介绍。 现在，如果您想对训练和评估进行低级别控制，则应当从头开始编写自己的训练和评估循环。这正是本指南要介绍的内容。 在 GradientTape 作用域内调用模型使您可以检索层的可训练权重相对于损失值的梯度。利用优化器实例，您可以使用上述梯度来更新这些变量（可以使用 model.trainable_weights 检索这些变量）。 在这种从头开始编写的训练循环中，您可以轻松重用内置指标（或编写的自定义指标）。下面列出了具体流程： Author: fchollet Date created: 2019/03/01 Last modified: 2023/06/25 Description: Complete guide to training & evaluation with fit() and evaluate(). This guide covers training, evaluation, and prediction (inference) models when using built-in APIs for training & validation (such as Model.fit(), Model.evaluate() and Model.predict()).

If you are interested in leveraging fit() while specifying your own training step function, see the guides on customizing what happens in fit(): If you are interested in writing your own training & evaluation loops from scratch, see the guides on writing training loops: In general, whether you are using built-in loops or writing your own, model training & evaluation works strictly in the same way across every kind of Keras model – Sequential models, models built with...

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