Stacked-Autoencoder | Stacked Autoencoder exercise in the Stanford | Machine Learning library
kandi X-RAY | Stacked-Autoencoder Summary
kandi X-RAY | Stacked-Autoencoder Summary
-> This is a solution to the Stacked Autoencoder exercise in the Stanford UFLDL Tutorial(-> The code has been written in Python using Scipy and Numpy -> The code is bound by The MIT License (MIT). -> Download the gunzip data files and the code file 'stackedAutoencoder.py' -> Put them in the same folder, extract the gunzips and run the program by typing in 'python stackedAutoencoder.py' in the command line -> You should get two text outputs as follows -> The first one should say 'Accuracy after greedy training : 0.87', which signifies an accuracy of 87% -> The second one should say 'Accuracy after finetuning : 0.97', which signifies an accuracy of 97% -> The code takes about 150 minutes to execute on an i3 processor.
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Top functions reviewed by kandi - BETA
- Execute stacked autoencoder .
- Calculates the stacked autoencoder cost .
- Compute the sparse autoencoder cost function .
- Loads MNIST images from a file .
- Compute the softmax cost function .
- Performs a stacked autoencoder prediction .
- Load the label data .
- r Convert layer parameters to layer stack .
- Feed - forward autoencoder .
- Converts a network layer into a list of parameters .
Stacked-Autoencoder Key Features
Stacked-Autoencoder Examples and Code Snippets
Community Discussions
Trending Discussions on Stacked-Autoencoder
QUESTION
For my project , i’m trying to predict the ratings that a user will give to an unseen movie, based on the ratings he gave to other movies. I’m using the movielens dataset.The Main folder, which is ml-100k contains informations about 100,000 movies.
Before processing the data, the main data (ratings data) contains user ID, movie ID, user rating from 0 to 5 and timestamps(not considered for this project).I then split the data into Training set(80%) and test data(20%) using sklearn Library.
To create the recommendation systems, the model ‘Stacked-Autoencoder’ is being used. I’m using PyTorch and the code is implemented on Google Colab. The project is based on this https://towardsdatascience.com/stacked-auto-encoder-as-a-recommendation-system-for-movie-rating-prediction-33842386338
I'm new to deep Learning and I want to compare this model(Stacked_Autoencoder) to another Deep Learning model. For Instance,I want to use Multilayer Perception(MLP). This is for research purposes. This is the code below for creating Stacked-Autoencoder model and training the model.
...ANSWER
Answered 2020-Jul-25 at 22:40An MLP is not suited for recommendations. If you want to go this route, you will need to create an embedding for your userid and another for your itemid and then add linear layers on top of the embeddings. Your target will be to predict the rating for a userid-itemid pair.
I suggest you take a look at variational autoencoders (VAE). They give state-of-the-art results in recommender systems. They will also give a fair comparaison with your stacked-autoencoder. Here's the research paper applying VAE for collaborative filtering : https://arxiv.org/pdf/1802.05814.pdf
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Install Stacked-Autoencoder
You can use Stacked-Autoencoder like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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