学习使用Tensorflow 2.0进行图像分类
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2019-06-15
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A Beginner Image Classification
Walkthrough With Tensorflow 2.0
From housing price prediction, music recognition and face detection
to the recreation of Van Gogh’s artworks, neural networks are quite
taking over the data science scene and becoming an essential tool in
the toolbox of the modern day data scientist. Neural networks are not
only wielded to solve highly complex problems, they also have higher
computational power and greater performance ability than common
algorithms when dealing with larger datasets. Furthermore, the
recent release of Tensorflow 2.0 has enabled beginners like you and I
to learn how to build models easily.
This article isn’t going to show you how to build the best model, but
my hope is that you walk away with a deepened understanding and a
richer appreciation of neural networks.
For this, we will work through the dog versus cats dataset. The goal is
to build a deep learning model that will take in an image and
differentiate between a cat image and a dog image. The criteria for a
good model is a high accuracy. There is also multiclass classification,
e.g. differentiating between different dog species, and the interested
learner can delve deeper into that after covering the foundations. But
for simplicity’s sake, we will work through a binary classification
problem.
Jagerynn Ting Verano
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Jun 6
·
8 min read
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