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DBN.py
DBN.py
 
 
README.md
README.md
 
 
SdA.py
SdA.py
 
 
bdt_predict.py
bdt_predict.py
 
 
bdt_regression_test_spec.py
bdt_regression_test_spec.py
 
 
congrid.py
congrid.py
 
 
convolutional_mlp.py
convolutional_mlp.py
 
 
dA.py
dA.py
 
 
dA_predict_and_plot.py
dA_predict_and_plot.py
 
 
handle_data.py
handle_data.py
 
 
logistic_sgd.py
logistic_sgd.py
 
 
mlp.py
mlp.py
 
 
plot_tree_regression.py
plot_tree_regression.py
 
 
predict_and_plot.py
predict_and_plot.py
 
 
predict_from_model_mlp.py
predict_from_model_mlp.py
 
 
rbm.py
rbm.py
 
 
residcorr_v5_4_45.dat
residcorr_v5_4_45.dat
 
 
theanets_mlp.py
theanets_mlp.py
 
 
train_conv_from_stacked.py
train_conv_from_stacked.py
 
 
train_conv_mlp_layered.py
train_conv_mlp_layered.py
 
 
train_convolutional.py
train_convolutional.py
 
 
train_mlp.py
train_mlp.py
 
 
utils.py
utils.py
 
 

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QuasarContANN

This project trys to create and train an artificial neural network (starting with a simple feed-forward-network) to obtain the continuum of noisy quasar spectra within the Lyman alpha forest. The implementation is based on Pythons Theano library.

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