https://github.com/jsflo/convolutionalneuralnetwork-tf

Different convolutional nerual network architectures in TensorFlow
https://github.com/jsflo/convolutionalneuralnetwork-tf

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Different convolutional nerual network architectures in TensorFlow

• Host: GitHub
• URL: https://github.com/jsflo/convolutionalneuralnetwork-tf
• Owner: JsFlo
• Created: 2017-09-15T15:40:58.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-09-16T18:07:51.000Z (over 7 years ago)
• Last Synced: 2024-04-20T17:21:01.122Z (8 months ago)
• Language: Python
• Homepage:
• Size: 1.11 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ConvolutionalNeuralNetwork-TF

## Deep MNIST CNN without Pooling

### DeepMnistCnnNoPooling.py

Convolutional Nerual Network without Pooling.

* 3 Convolutional layers

* 2 Fully Connected Layers

Adds **non-linearity** after each of the above layers.

[conv -> relu -> conv -> relu -> conv -> relu -> fc -> relu -fc -> relu]

This model does not use pooling to reduce the size. 

The **3rd hidden layer** (conv3) uses a **stride** of **2** to reduce the size.

Not using pooling is an idea seen here:[ Striving for Simplicity: The All Convolutional Net](https://arxiv.org/abs/1412.6806)

Accuracy: **99.1**

### Notes

These **convolutional** layers do not use **zero-padding** so

the sizes of the output volume changes. This means that I had to carefully

track the output volumes.

The output volumes can be calculated with

#### ((W - F + 2P)/ (S)) + 1

* W = Input Volume size

* F = Filter Size ("receptive field size")

* P = Zero Padding used

* S = Stride

Because we use 0 zero-padding it can be simplified to:

#### ((W - F)/(S)) + 1

Another **disadvantage** to not using **zero-padding** is that 

the edges at each convolutional don't get **as many neurons looking** at them

compared to a traditional architecture with **zero-padding** at every conv layer 

to keep the volume the same( usually reduced by max-pooling).

## Traditional CNN

A more **traditional CNN** that relies on **2x2 pooling** to **reduce dimensions**.

The **convolutions don't** reshape the **dimensions** because it **zero-pads**.