Master thesis neural network

Master thesis neural network

Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory. Terms: Winter Instructors: There are no professors associated with this course for the academic year A Hopfield network (or Ising model of a neural network or Ising–Lenz–Little model) is a form of recurrent artificial neural network and a type of spin glass system popularised by John Hopfield in as described earlier by Little in based on Ernst Ising's work with Wilhelm Lenz on the Ising model. Hopfield networks serve as content-addressable ("associative") memory systems with It gives different probabilities of activating the neural network or not. This is very useful in the case of Cryptanalysis. Two names are used to design the same domain of research: Neuro-Cryptography and Neural Cryptography. The first work that it is known on this topic can be traced back to in an IT Master Thesis. Applications

Master of Science in Mechanical Engineering | Northeastern University

Multi-layer Recurrent Neural Networks LSTM, GRU, RNN for character-level language models in Torch. In other words the model takes one text file as input and trains a Recurrent Neural Network that learns to predict the next character in a sequence.

The RNN can then be used to generate text character by character that will look like the original training data. Master thesis neural network context of this code base is described in detail in my blog post. The code in this repo additionally: allows for multiple layers, uses an LSTM instead of a vanilla RNN, master thesis neural network, has more supporting code for model checkpointing, and is of course much more efficient since it uses mini-batches and can run on a GPU.

It's under the name torch-rnn. Master thesis neural network also avoids headaches with cloning models in this repo. In other words, torch-rnn should be the default char-rnn implemention to use now instead of the one in this code base.

This code is written in Lua and requires Torch. If you're master thesis neural network Ubuntu, installing Torch in your home directory may look something like:.

See the Torch installation documentation for more details. Master thesis neural network Torch is installed we need to get a few more packages using LuaRocks which already came with the Torch install. In particular:. If you'd like to train on an NVIDIA GPU using CUDA this can be to about 15x fasteryou'll of course need the GPU, and you will have to install the CUDA Toolkit.

Then get the cutorch and cunn packages:. If you'd like to use OpenCL GPU instead e. ATI cardsyou will instead need to install the cltorch and clnn packages, and then use the option -opencl 1 during training cltorch issues :.

I'm providing a few more datasets on this page. Your own data : If you'd like to use your own data then create a single file input. Dataset sizes : Note that if your data is too small 1MB is already considered very small the RNN won't learn very effectively, master thesis neural network. Remember that it has to learn everything completely from scratch. It will work significantly better. Start master thesis neural network the model using train.

As a sanity check, to run on the included example dataset simply try:. Notice that here we are setting the flag gpuid to -1, which tells the code to train using CPU, otherwise it defaults to GPU 0, master thesis neural network.

There are many other flags for various options. lua -help for comprehensive settings. txt exists :. While the model is training it will periodically write checkpoint files to the cv folder. master thesis neural network this is 1 then a checkpoint is written every iteration. The filename of these checkpoints contains a very important number: the loss. t7 indicates that master thesis neural network this point the model was on epoch 0.

it has almost done one full pass over the training dataand the loss on validation data was 2. This number is very important because the lower it is, the better the checkpoint works, master thesis neural network. Once you start to generate data discussed belowyou will want to use the model checkpoint that reports the lowest validation loss.

Notice that this might not necessarily be the last checkpoint at the end of training due to possible overfitting. The batch size specifies how many streams of data are processed in parallel at one time.

The sequence length specifies the length of each stream, master thesis neural network, which is also the limit at which the gradients can propagate backwards in time.

Thus, if you have a very difficult dataset where there are a lot of long-term dependencies you will want to increase this setting.

Now, if at runtime your input text file has N characters, these first all get split into chunks of size BxS. If your data is small, it's possible that with the default settings you'll only have very few chunks in total for example This is bad: In these cases you may want to decrease master thesis neural network size or sequence length. Given a checkpoint file such as those written to cv we can generate new text. Master thesis neural network example:, master thesis neural network.

Make sure that if your checkpoint was trained with GPU it is also sampled from with GPU, or vice versa. Otherwise the code will currently complain. lua -help for full options. The temperature is dividing the predicted log probabilities before the Softmax, so lower temperature will cause the model to make more likely, but also more boring and conservative predictions. Higher temperatures cause the model to take more chances and increase diversity of results, but at a cost of more mistakes.

It's also possible to prime the model with some starting text using -primetext. This starts out the RNN with some hardcoded characters to warm it up with some context before it starts generating text. a fun primetext might be -primetext "the meaning of life is ". Training with GPU but sampling on CPU. lua script to convert your GPU checkpoint to a CPU checkpoint. In near future you will not have to do this explicitly. t7 that you can use with the sample script and with -gpuid -1 for CPU mode.

If you're somewhat new to Machine Learning or Neural Networks it can take a bit of expertise to get good models. The most important quantity to keep track of is the difference between your training loss printed during training and the validation loss printed once in a while when the RNN is run on the validation data by default every iterations.

The two important quantities to keep track of here are:. These two should be about the same order of magnitude. It's a little tricky to tell.

Here are some examples:. The winning strategy to obtaining very good models if you have the compute time is to always err on making the network larger as large as you're willing to wait for it to compute and then try different dropout values between 0,1. Whatever model has the best validation performance the loss, written in the checkpoint filename, low is good is the one you should use in the end. It is very common in deep learning to run many different models with many different hyperparameter settings, and in the end take whatever checkpoint gave the best validation performance.

By the way, the size of your training and validation splits are also parameters. Make sure you have a decent amount of data in your validation set or otherwise the validation performance will be noisy and not very informative. This code was originally based on Oxford University Machine Learning class practical 6which is in turn based on learning to execute code from Wojciech Zaremba.

Chunks of it were also developed in collaboration with my labmate Justin Johnson. Skip to content. Multi-layer Recurrent Neural Networks LSTM, GRU, RNN for character-level language models in Torch Code Issues Pull requests Actions Projects Wiki Security Insights, master thesis neural network.

Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 88 commits. Failed to load latest commit information. first commit. May 21, changing default LSTM initialization to use biases of 1. Sep 20, Fix unclear errors. Oct 26, Add t7 files to. May 22, Update Readme. Apr 30, fixing a bug introduced in previous commit. We have to use doubles no….

Aug 8, add opencl to sample. lua, add link to clto…. Jul 12, fix message. Nov 24, View code. char-rnn Update: torch-rnn Requirements Usage Data Training Sampling Tips and Tricks Monitoring Validation Loss vs.

Training Loss Approximate number of parameters Best models strategy Additional Pointers and Acknowledgements License.

Jan Pisl - Master's Thesis Presentation

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Hopfield network - Wikipedia

Papers and Thesis; MARI Master's Thesis Preparation. MARI Advanced Methods in Marine Science. And level AQFI, MARI or OCEN papers to make a total of points (one may be replaced with an approved level paper in a Science subject). Thesis: MARI 5. Note: The papers are normally taken before undertaking the thesis It gives different probabilities of activating the neural network or not. This is very useful in the case of Cryptanalysis. Two names are used to design the same domain of research: Neuro-Cryptography and Neural Cryptography. The first work that it is known on this topic can be traced back to in an IT Master Thesis. Applications The Department of Mechanical and Industrial Engineering in the College of Engineering offers the Master of Science in Mechanical Engineering. At the MS level students may pursue a program preparing for advanced practice or for MS thesis research. All MS students with assistantships are required to complete a thesis