Tensorflow 2.0 学习资料

这篇博客汇总了TensorFlow 2.0的学习资源,包括官方教程、Keras crash course以及深入视频讲解。此外,还介绍了TensorFlow 2.0中的Keras API,强调其易用性和逐步增加复杂性的特性。同时,提到了TensorBoard等库和扩展,以及Keras Functional API的优势。文章还涵盖了训练和评估模型、自定义层、损失函数、指标和模型保存等内容。

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  • https://www.tensorflow.org/tutorials/text/word_embeddings
  • https://www.tensorflow.org/tutorials/text/nmt_with_attention
  • https://www.tensorflow.org/tutorials/text/transformer
  • https://www.tensorflow.org/tutorials/text/image_captioning

A machine learning platform from the future.
A core principle of Keras is “progressive disclosure of complexity”: it’s easy to get started, and you can gradually dive into workflows where you write more and more logic from scratch, providing you with complete control. This applies to both model definition, and model training.Model definition: spectrum of workflowsModel training: spectrum of workflows

Resources to learn TensorFlow 2.0

Libraries and extensions

Tensorboard:https://www.tensorflow.org/tensorboard

部分笔记:

  • @tf.function: compile the training function into a static graph to speedup over 40%.
  • custom layers:
    class Linear(Layer):
      """y = w.x + b"""
    
      # 定义全局变量
      def __init__(self, units=32
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