Core Module (radionets.core)

Introduction

radionets.core contains core modules, classes and functions for the deep-learning framework.

Submodules

• Callbacks (radionets.core.callbacks)
• Data (radionets.core.data)
• Learner (radionets.core.learner)
• Logging (radionets.core.logging)
• Loss Functions (radionets.core.loss_functions)
• Model (radionets.core.model)
• Core Utils (radionets.core.utils)

Reference/API

radionets.core Package

Functions

define_learner(data, arch, train_conf[, ...])
get_bundles(path)	returns list of bundle paths located in a directory
get_dls(train_ds, valid_ds, batch_size, **kwargs)
get_learner(data, arch, lr[, loss_func, ...])
init_cnn(m[, uniform])
load_data(data_path, mode[, fourier])	Load data set from a directory and return it as H5DataSet.
load_pre_model(learn, pre_path[, visualize, ...])	Loads a previously saved model as pre-model.
open_bundle(path)	open radio galaxy bundles created in first analysis step
open_bundle_pack(path)
open_fft_bundle(path)	open radio galaxy bundles created in first analysis step
save_bundle(path, bundle, counter[, name])
save_fft_pair(path, x, y[, z, name_x, ...])	write fft_pairs created in second analysis step to h5 file
save_model(learn, model_path)
setup_logger(**kwargs)	Basic logging setup.
symmetry(x)

Classes

AvgLossCallback()	Save the same average Loss for training and validation as printed to the terminal.
CometCallback(name, test_data, ...)
CudaCallback(*[, after_create, before_fit, ...])
DataAug(*[, after_create, before_fit, ...])
DataBunch(train_dl, valid_dl[, num_classes])
GradientCallback(num_epochs, test_data, ...)
H5DataSet(bundle_paths, tar_fourier)
Normalize(conf)
PredictionImageGradient(test_data, model, ...)
SaveTempCallback(model_path)
SwitchLoss(second_loss, when_switch)