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Steve Renals 2015-10-12 09:49:50 +01:00
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02_MNIST_SLN.ipynb
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"source": [
"# Introduction\n",
"\n",
"This tutorial is an introduction to the coursework about multi-layer preceptron (MLP) models, or Deep Neural Networks (DNNs). Here, we will show how to build a single layer linear model (similar to the one from the previous lab) for MNIST classification using the provided code-base. \n",
"This tutorial is an introduction to the first coursework about multi-layer networks (also known as Multi-Layer Perceptrons - MLPs - or Deep Neural Networks - DNNs). Here, we will show how to build a single layer linear model (similar to the one from the previous lab) for MNIST digit classification using the provided code-base. \n",
"\n",
"The principal purpose of this introduction is to get you familiar with how to connect the provided blocks (and what operations each of them implements) to set up an experiment, including 1) build the model structure 2) optimise the model's parameters and 3) evaluate the model on test data. \n",
"The principal purpose of this introduction is to get you familiar with how to connect the code blocks (and what operations each of them implements) in order to set up an experiment that includes 1) building the model structure 2) optimising the model's parameters (weights) and 3) evaluating the model on test data. \n",
"\n",
"## For those affected by notebook kernel issues\n",
"\n",
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" + and if you have chosen the **comfy** way type: `workon mlpractical`\n",
" + and if you have chosen the **generic** way, `source` your virutal environment using `source` and specyfing the path to the activate script (you need to localise it yourself, there were not any general recommendations w.r.t dir structure and people have installed it in different places, usually somewhere in the home directories. If you cannot easily find it by yourself, use something like: `find . -iname activate` ):\n",
"\n",
"## Syncing repos\n",
"## Syncing the git repository\n",
"\n",
"Look <a href=\"https://github.com/CSTR-Edinburgh/mlpractical/blob/master/gitFAQ.md\">here</a> for more details. But in short, we recommend to create a separate branch for the coursework, as follows:\n",
"\n",
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"source": [
"# Multi Layer Models\n",
"\n",
"Today, we are going to build the models with an arbitrary number of hidden layers, please have a look at the below diagram and the corresponding computations (which have an *exact* matrix form as expected by numpy and row-wise orientation, $\\circ$ denotes an element-wise product). Below the diagram, we briefly describe how each comptation relates to the code we have provided.\n",
"Today, we shall build models which can have an arbitrary number of hidden layers. Please have a look at the diagram below, and the corresponding computations (which have an *exact* matrix form as expected by numpy, and row-wise orientation; note that $\\circ$ denotes an element-wise product). In the diagram, we briefly describe how each comptation relates to the code we have provided.\n",
"\n",
"![Making Predictions](res/code_scheme.svg)\n"
]