--- title: Label-mixing transforms keywords: fastai sidebar: home_sidebar summary: "Callbacks that perform data augmentation by mixing samples in different ways." description: "Callbacks that perform data augmentation by mixing samples in different ways." nb_path: "nbs/018_data.mixed_augmentation.ipynb" ---
from tsai.models.utils import *
from tsai.models.ResNet import *
from tsai.learner import *
dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
tfms = [None, Categorize()]
batch_tfms = TSStandardize()
dls = get_ts_dls(X, y, tfms=tfms, splits=splits, batch_tfms=batch_tfms)
model = build_model(ResNet, dls=dls)
learn = Learner(dls, model, cbs=MixUp1d(0.4))
learn.fit_one_cycle(1)
dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
tfms = [None, Categorize()]
batch_tfms = TSStandardize()
dls = get_ts_dls(X, y, tfms=tfms, splits=splits, batch_tfms=batch_tfms)
model = build_model(ResNet, dls=dls)
learn = Learner(dls, model, cbs=CutMix1d(1.))
learn.fit_one_cycle(1)
dsid = 'NATOPS'
X, y, splits = get_UCR_data(dsid, return_split=False)
tfms = [None, Categorize()]
batch_tfms = TSStandardize()
dls = get_ts_dls(X, tfms=tfms, splits=splits, batch_tfms=batch_tfms)
model = build_model(ResNet, dls=dls)
learn = Learner(dls, model, loss_func=L1LossFlat(), cbs=CutMix1d(1.))
learn.fit_one_cycle(1)