I know I can specify the input_size in the __init__() method. The problem is we instantiate the model and this remains fixed. Is there a way to change the input_size during training?
Here is my situation, I am training with data from multiple CSV files. The problem is the tables in these CSV files have varying width (and I am passing this data through an LSTM). Can I change that input_width as the loader passes a new batch with new tensor widths ?
I am thinking of something along the lines of:
class Net(nn.Module):
def __init__(self, input_size):
super(Net, self).__init__()
self.input_size = input_size
self.encoding_layer = nn.Linear(self.input_size, 12)
def forward(self, x):
self.input_size = x.size()
x = self.fc(x)
return x
or even calling the attribute from the training loop:
for epoch in range(no_epochs):
# load data
model.input_size = data.size()
output = model(data)
# loss and the rest
would anything like this work?