Building Convolutional Neural Networks with Tensorflow

Building Convolutional Neural Networks with Tensorflow

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The pictures here are from the full article. Source code is also provided.

Before you continue, make sure you understand how a convolutional neural network works. For example,

  • What is a convolutional layer, and what is the filter of this convolutional layer?
  • What is an activation layer (ReLu layer (most widely used), sigmoid activation or tanh)?
  • What is a pooling layer (max pooling / average pooling), dropout?
  • How does Stochastic Gradient Descent work?

The contents of this blog-post is as follows:

1. Tensorflow basics:

  • Constants and Variables
  • Tensorflow Graphs and Sessions
  • Placeholders and feed_dicts

2. Neural Networks in Tensorflow

  • Introduction
  • Loading in the data
  • Creating a (simple) 1-layer Neural Network:
  • The many faces of Tensorflow
  • Creating the LeNet5 CNN
  • How the parameters affect the outputsize of an layer
  • Adjusting the LeNet5 architecture
  • Impact of Learning Rate and Optimizer

3. Deep Neural Networks in Tensorflow

  • AlexNet
  • VGG Net-16
  • AlexNet Performance

4. Final words

To read this blog, click hereThe code is also available in my GitHub repository, so feel free to use it on your own dataset(s).

Published at Thu, 07 Sep 2017 13:30:00 +0000

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