--- title: Learner keywords: fastai sidebar: home_sidebar summary: "This contains fastai Learner extensions." description: "This contains fastai Learner extensions." nb_path: "nbs/052_learner.ipynb" ---
arch_name = 'InceptionTimePlus'
test_eq(get_arch('InceptionTimePlus').__name__, arch_name)
from tsai.data.all import *
from tsai.data.core import *
from tsai.models.FCNPlus import *
dsid = 'OliveOil'
X, y, splits = get_UCR_data(dsid, verbose=True, split_data=False)
tfms = [None, [Categorize()]]
dls = get_ts_dls(X, y, splits=splits, tfms=tfms)
learn = ts_learner(dls, FCNPlus)
for p in learn.model.parameters():
p.requires_grad=False
test_eq(count_parameters(learn.model), 0)
learn.freeze()
test_eq(count_parameters(learn.model), 1540)
learn.unfreeze()
test_eq(count_parameters(learn.model), 264580)
learn = ts_learner(dls, 'FCNPlus')
for p in learn.model.parameters():
p.requires_grad=False
test_eq(count_parameters(learn.model), 0)
learn.freeze()
test_eq(count_parameters(learn.model), 1540)
learn.unfreeze()
test_eq(count_parameters(learn.model), 264580)
learn.show_batch();
learn.fit_one_cycle(2, lr_max=1e-3)
dsid = 'OliveOil'
X, y, splits = get_UCR_data(dsid, split_data=False)
tfms = [None, [TSClassification()]]
dls = get_ts_dls(X, y, tfms=tfms, splits=splits)
learn = ts_learner(dls, FCNPlus, metrics=accuracy)
learn.fit_one_cycle(2)
learn.plot_metrics()
learn.show_probas()
if not os.path.exists("./models"): os.mkdir("./models")
if not os.path.exists("./data"): os.mkdir("./data")
np.save("data/X_test.npy", X[splits[1]])
np.save("data/y_test.npy", y[splits[1]])
learn.export("./models/test.pth")