I am new to neural network and for sure PyTorch, I am working on simple feed forward NN to predict groundwater level form precipitation and temperature daily data.
I’m facing some problems and seeking help:
First problem: data loader
So I should be using data loader so to feed the input data in batches (for example I have 300 temperature values and I want a batch size of 4) my understanding, the dataloader will take the first four data, feed it forward and then move to the next four data, my question is, is there a way for the dataloader to take the first four and then move 1 reading ahead (to use three of the temperature data used in the previous batch, ex. First batch = temperatures 1, 2, 3 and 4, second batch= temperatures= 2, 3, 4 and 5, and so on till it get to the last reading)
Second problem: data loader and output(target) data
- Will there be a need to use dataloader for the output(target) data if there will be no batch size as I want it to take one reading only, just one output
- If I have many output nodes, with different batch size to the input data, should I construct a separate dataloader for it or is it possible to combine the input and output within the same dataloader function.
Third problem: forward function
In defining forward function within the class (nn.module), and struggling with the input data; if I am using data loader and batches, should I use dataloader as input, if so how. or should I use the entire data frame.
This is my code, and Im asking about xin(xinput),
def forward(self, xin):
xinhi = self.fc1(xin)
xhi = self.Sigmoid(xinhi)
xhiout = self.fc2(xhi)
xout = self.Sigmoid(hiout)