<commit_before>---
layout: post
title: Neural Image Addition
modified:
categories: [project]
excerpt: "Adding a series of MNIST digits using a CNN-to-RNN trained end-to-end."
tags: [Deep Learning, Machine Learning, Keras]
image:
  feature:
date: 2016-03-16T20:43:28-04:00
---

Training neural networks with both convolutional and recurrent layers end-to-end provides a nice framework to model many problems. The general framework is as follows: given a series of images, automatically learn features, model temporal patterns, and output a vector (or value) at every timestep that represents something about each image. This has a ton of very interesting applications. 

My main (longer term) motivation is exploring this is a larger project working towards automatic video summarization, using neural nets to extract scene information and figure out the frame-by-frame importance.

Anyway, as a starting point, I wanted to try a bunch of smaller models that allowed this type of end-to-end optimization. One very easy one to both understand and train is passing in a series of 1..N MNIST digits and calculating the sum of all the digit values added up.

### Problem ###<commit_msg>Another tiny change<commit_after>---
layout: post
title: Neural Image Addition
modified:
categories: [project]
excerpt: "Adding a series of MNIST digits using a CNN-to-RNN trained end-to-end."
tags: [Deep Learning, Machine Learning, Keras]
image:
  feature:
date: 2016-03-16T20:43:28-04:00
---

Training neural networks with both convolutional and recurrent layers end-to-end provides a nice framework to model many problems. The general framework is as follows: given a series of images, automatically learn features, model temporal patterns, and output a vector (or value) at every timestep that represents something about each image. This has a ton of very interesting applications. 

My main (longer term) motivation for exploring this is a larger project working towards automatic video summarization, using neural nets to extract scene information and figure out the frame-by-frame importance.

Anyway, as a starting point, I wanted to try a bunch of smaller models that allowed this type of end-to-end optimization. One very easy one to both understand and train is passing in a series of 1..N MNIST digits and calculating the sum of all the digit values added up.

### Problem ###
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