Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
I have the following code in time series multivariate classification problem:
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from tsai.models.MINIROCKETPlus_Pytorch import *
from tsai.models.utils import *
X.shape #(2783, 10, 20000)
mrf = MiniRocketFeaturesPlus(X.shape[1], X.shape[2]).to(default_device(),dtype=torch.float32)
mrf.fit(X_train.astype(np.float32), chunksize = 64) #time = 5s
X_feat = get_minirocket_features(X.astype(np.float32), mrf, chunksize = 16, to_np = True) #time = 4 min
X_feat.shape #(2783, 9996, 1)
# Using tsai/fastai, create DataLoaders for the features in X_feat.
tfms = [None, TSClassification()]
batch_tfms = TSStandardize(by_sample=True)
dls = get_ts_dls(X_feat, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)
# model is a linear classifier Head
model = build_ts_model(MiniRocketHead, dls=dls) # <== this line raises the error: TypeError: __init__() got an unexpected keyword argument 'custom_head'
model.head
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I have the following code in time series multivariate classification problem:
How to solve this issue?