Saving a settings file

In order to launch a training, you need to save the setting file using the interactive tools contained in the folder notebooks: offline_settings_training.ipynb. In this page, we present the different parameters that you will be asked to define. This description will also allow to you to completely understand how flexible is the model implemented (i.e. what you can do using directly this project and what you can’t).

The following screen shot presents an example of the interactive tool to define the settings for a training.

General parameters for training

• name_model String Name of the architecture of the network. The name is chosen among the architecture defined in the package MRF.models.

If the chosen model starts by a projection :

• normalization String Type of normalization of the data. The possible types of normalization that can be used are defined in the class Normalization of the module Training_parameters.

• initilization

  String

  Type of initilization for the weights of the first linear layer which should perform the projection. The possible types of initialization that can be used are defined in the class Initialization of the module Training_parameters.
• dimension_projection

  Int

  Dimension of the subspace on which the projection will be performed.
• namepca

  str

  Absolute path to the file containing the basis functions obtained with an online PCA algorithm in order to initalize or define the first linear layer supposed to perform the projection. This file needs to be a .mat file with the basis functions saved with the key word basis. The basis functions need to be saved with the size (number of functions) x (length fingerprint).
• optimizer

  String

  Name of the optimizer used for the training. The possible names to use are defined in the class Optimizer of the module Training_parameters.
• lr

  Float

  Learning rate.
• noise_type

  String

  Type of noise that you want to use. The different types of noise are defined in the module Training_parameters.
• noise_level

  Float

  Level of noise that you want to use.
• batch_size

  Int

  Batch size.
• nb_iterations

  Int

  Number of iterations.
• nb_epochs

  Int

  Number of epochs.
• complex

  Bool

  Set to True if the input signals are complex valued.
• loss

  Array of String

  Type of loss used. The array has a length 5 corresponding to the 5 parameters (\(m_{0}^s\), \(T_1^f\), \(T_{2}^f\), \(R\) and \(T_{2}^s\)). The i-th element of this array gives the type of transformation applied to the parameter i. The different type of losses are defined in the class Loss in the module Training_parameters.
• params

  Array of Int

  Define the parameters that you want to estimate. Number 0 corresponds to \(m_0^s\), number 1 to \(T_1^f\), etc. (The details are defined in the the module Training_parameters with the vectors nametoparam and paramtoname).
• minpd

  Float

  Minimum proton density.
• maxpd

  Float

  Maximum proton density.
• save_name

  String

  Name given to your training.
• validation

  Bool

  Boolean which is True if you want to track validation loss and errors during the training.

  If validation is True :

  * `validation_size`
      **Int**
      Number of fingerprints used for the validation dataset.
  

• nb_files

  String

  Number of files used for training and validation. I recall that your files needs to be saved in a folder with a name that you can choose and that should be located in MRF/Offline/loading_data.
• path_files

  String

  Name that you gave to the folder located in MRF/Offline/loading_data which contains the files. Two different type of files should be saved in this folder:

  • the files fingerprints1.npy, fingerprints2.npy, … Each file contains an array of size n x (length fingerprint) where n is an integer that should be greater than the batch size chosen. Each fingerprint should be computed considering that the proton density is equal to 1.

  • the files params1.npy, params2.npy, … Each file contains an array of size n x (length fingerprint) and gives the parameters used to compute the fingerprints saved in the files fingerprints1.npy, fingerprints2.npy, …

  • If your loss requires the knowledge of the Cramer Rao Bound, you also need to save n this folder the files CRBs1.npy, CRBs2.npy, … Each file contains an array of size n x 6 and gives the Cramer Rao Bounds for the parameters \(m_{0}^s\), \(T_1^f\), \(T_{2}^f\), \(R\), \(T_{2}^s\) and \(M_0\) in this order.

If validation is True :

• small_validation_size Int Number of fingerprints used for the smaller validation dataset.

Data Generation Training