keras 官方例子，深度学习专用，机器学习专用，代码简单，

主要介绍了使用Keras预训练好的模型进行目标类别预测详解，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

LeNet-5手写字体识别-Keras函数式模型完整代码，可进入网址 https://www.cnblogs.com/ailex/p/9617534.html 直接查看

图像分类 使用Tensorflow和Keras API开发了深度学习模型，以通过卷积神经网络对动物的图像进行分类。 使用Flask将开发的模型集成到Web应用程序上，并将该Web应用程序部署在Heroku上。

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning process Use advanced topics of deep learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.

[keras]如何解决MNIST 数据集下载不了的问题-附件资源

本文介绍了如何使用Keras框架，搭建一个小型的神经网络-多层感知器，并通过给定数据进行计算训练，最后将训练得到的模型提取出参数，在51单片机上进行部署运行。 目录 0 – 楔子 1 – 训练模型 1-1 模型的设计 1-2 数据集 1-3 模型的训练 1-4 模型的保存与再载入 2 – 部署模型 2-1 模型参数的提取 2-2 矩阵的运算方式 2-3 NNLayer 2-4 在单片机上运行 0 – 楔子 在前一篇文章（Keras #0 – 搭建Keras环境，跑一个例程），介绍了如何使用 Anaconda Navigator 进行方便快捷的 Keras 安装与部署，并且运行了一个例程来进行验

主要介绍了关于Keras Dense层整理，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

今天小编就为大家分享一篇keras获得某一层或者某层权重的输出实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

该文件虽然是在github上可以找到的， 把该文件下载后保存到/root/.keras/models目录下即可