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Functions149 in github.com/cugzj/KT-pFL

⨍ Functions 149◇ Types & classes 30

(self, in_channels, out_channels, stride, t=6, num_classes=100)

src/model.py:257

(self, num_classes=10)

src/model.py:288

(self, in_channels, out_channels, stride)

src/model.py:375

(self, ratio=1, num_classes=10)

src/model.py:434

src/data_utils.py:32

src/data_utils.py:49

Functionbatch_data

data is a dict := {'x': [numpy array], 'y': [numpy array]} (on one client) returns x, y, which are both numpy array of length: batch_size

src/model_utils.py:30

Functioncifar_iid

Sample I.I.D. client data from CIFAR10 dataset :param dataset: :param num_users: :return: dict of image index

src/sampling.py:145

src/new_model.py:18

src/new_model.py:32

src/new_model.py:49

src/new_model.py:91

src/new_model.py:119

src/new_model.py:165

src/new_model.py:243

src/new_model.py:281

src/new_model.py:383

src/new_model.py:430

src/model.py:15

src/model.py:33

src/model.py:59

src/model.py:73

src/model.py:90

src/model.py:108

src/model.py:148

src/model.py:189

src/model.py:235

src/model.py:277

src/model.py:315

src/model.py:417

src/model.py:464

Functiongenerate_EMNIST_writer_based_data

(X, y, writer_info, N_priv_data_min = 30, N_parties = 5, classes_in_use

src/data_utils.py:275

Functiongenerate_imbal_CIFAR_private_data

(X, y, y_super, classes_per_party, N_parties, samples_per_class=7)

src/data_utils.py:320

Functionget_batch_sample

(data, batch_size)

src/model_utils.py:65

Functionget_random_batch_sample

(data_x, data_y, batch_size)

src/model_utils.py:52

Functionmnist_iid

Sample I.I.D. client data from MNIST dataset :param dataset: :param num_users: :return: dict of image index

src/sampling.py:9

Functionmnist_noniid_unequal

Sample non-I.I.D client data from MNIST dataset s.t clients have unequal amount of data :param dataset: :param num_users: :return

src/sampling.py:55

Functionmobilenetv2

(num_classes=10)

src/new_model.py:309

Functionmobilenetv2

(num_classes=10)

src/model.py:343

FunctionpartitionOfK

(numbers, start, end, k)

src/engine_cos.py:97

src/plot_data.py:15

Functionplot_save

(name,x,save_path)

src/plot_data.py:6

Functionshufflenetv2

(num_classes=10)

src/new_model.py:452

Functionshufflenetv2

(num_classes=10)

src/model.py:486

Functionsuffer_data

src/model_utils.py:19

Functiontrain_models

Train an array of models on the same dataset. We use early termination to speed up training.

src/engine_topk.py:206

Functiontrain_models

Train an array of models on the same dataset. We use early termination to speed up training.

src/engine_kt_pfl.py:217

Functiontrain_models

Train an array of models on the same dataset. We use early termination to speed up training.

src/engine_cos.py:232

Functiontrain_models

Train an array of models on the same dataset. We use early termination to speed up training.

src/engine_normal.py:136

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