Reusable Deep Neural Networks:

Applications to Biomedical Data

Generate script to run DEEP TRANSFER LEARNING

Enter target dataset and path used for the experiment

Type the path where all the datasets located in Theano pickled format:
Target_dataset:
Type the folder name of target dataset:
Source_dataset:
Type the folder name of source dataset:
Type the source reuse mode Eg: (PT or PT+FT):
Enter 1 for the retrain layers during fine-tuning: Eg ([0,0,1])
Enter 1 to tranfer hidden layer from source: Eg ([1,1,1])
Type the folder name to store the results:
Type the text file name to store the console output:
Enter number times to repeate the experiment:
Enter the GPU number used:

Enter Stacked Denoising Autoencoders traning parameters

Fine-tuning learning rate :
Max number of Fine-tuning epochs :
Pre-training learning rate :
Max number of Pre-training epochs :
Number of neurons at each hidden layer comma seperated for each layer eg: [500,500,500]
Mini batch size :
Random initial seed number :
Train fraction of the total training data :

Now Copy and Paste the following into terminal and hit return to prepare tiles:


taskset -c 0 nohup python online_input.py TL data_path target_data fold target_data fold target_data fold fold results_dir nr_reps gpu_nr finetune_lr training_epochs pretrain_lr pretraining_epochs hidden_layers_sizes batch_size rng_seed training_data_fraction > results_dir result_file_name 2>&1 &

This project is financed by FEDER funds through the Programa Operacional Factores de Competitividade COMPETE and by Portuguese funds through FCT Fundação para a Ciência e a Tecnologia in the framework.