While there are many, many different neural network architectures, the most common architecture is the feedforward network: Figure 1: An example of a feedforward neural network with 3 input nodes, a hidden layer with 2 nodes, a second hidden layer with 3 nodes, and a final output layer with 2 nodes. Our final step will be to build a test script that will load images and classify them with OpenCV, Keras, and our trained model. We’ll review the results of our simple neural network architecture and discuss methods to improve it. The goal of this challenge is to correctly classify whether a given image contains a dog or a cat. We’ll then discuss our project structure followed by writing some Python code to define our feedforward neural network and specifically apply it to the Kaggle Dogs vs. To start this post, we’ll quickly review the most common neural network architecture - feedforward networks. Looking for the source code to this post? Jump Right To The Downloads Section A simple neural network with Python and Keras
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