I’ve been twisting and turning this both in a notebook and in python but i can’t seem to make it work. Here’s code:
from fastai.vision import * import torch.nn as nn class Mnist_Linear_NN(nn.Module): def __init__(self): super().__init__() self.lin1 = nn.Linear(784, 50, bias=True) self.lin2 = nn.Linear(50, 10, bias=True) def forward(self, xb): x = self.lin1(xb) return self.lin2(x) def main(): np.random.seed(42) path_img = Path("data/") fnames = get_image_files(path_img) pat = r'data\/mnist-\d+-(\d).png' bs = 32 data = ImageDataBunch.from_name_re(path_img, fnames, pat, bs=bs, size=28) learn = Learner(data, Mnist_Linear_NN(), metrics=accuracy) learn.fit_one_cycle(1) main()
This returns a long traceback (https://pastebin.com/zsevywL5), which ends with
RuntimeError: size mismatch, m1: [2688 x 28], m2: [784 x 50] at /opt/conda/conda-bld/pytorch_1556653114079/work/aten/src/TH/generic/THTensorMath.cpp:961.
Any help in fixing this would be much appreciated! Alternatively: is there any handy guide/info which allows me to implement a custom model into fastai? I’ve been skimming through lessons but haven’t found anything, most use transfer learning/resnets.
I’m working in w10, latest updates, got traceback in windows subsystem anaconda environment with fastai installation, but i don’t think it’s platform related.