FCN

This is an unofficial PyTorch implementation created by Ignacio Oguiza (oguiza@timeseriesAI.co) based on:

Wang, Z., Yan, W., & Oates, T. (2017, May). Time series classification from scratch with deep neural networks: A strong baseline. In 2017 international joint conference on neural networks (IJCNN) (pp. 1578-1585). IEEE.
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4), 917-963.

Official FCN TensorFlow implementation: https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py.

Note: kernel filter size 8 has been replaced by 7 (since we believe it’s a bug).

FCN

 FCN (c_in, c_out, layers=[128, 256, 128], kss=[7, 5, 3])

Same as nn.Module, but no need for subclasses to call super().__init__

bs = 16
nvars = 3
seq_len = 128
c_out = 2
xb = torch.rand(bs, nvars, seq_len)
model = FCN(nvars, c_out)
test_eq(model(xb).shape, (bs, c_out))
model

FCN(
  (convblock1): ConvBlock(
    (0): Conv1d(3, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (convblock2): ConvBlock(
    (0): Conv1d(128, 256, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
    (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (convblock3): ConvBlock(
    (0): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
    (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU()
  )
  (gap): GAP1d(
    (gap): AdaptiveAvgPool1d(output_size=1)
    (flatten): Flatten(full=False)
  )
  (fc): Linear(in_features=128, out_features=2, bias=True)
)