I am relatively new to CNNs, and I’m working on a machine learning classification problem with chemical data. I’m looking for advice on 1) how to structure the input data, and 2) architecture for the neural network classifier.

For this task I have raman spectra to use as input. I’ve already removed the baseline and normalized the spectra to lie between 0 and 1. Next I want to input them into the model. I have been operating under the assumption that the data should be input into the model in the shape [batch, 1, length_of_spectra], that is if each spectrum is length 1000, and I have a batch size of 32, then the input data shape would be [32, 1, 1000]. Is this the correct shape? I’m a little confused about the difference between the number of (input) channels and the signal length. Should I consider each wavenumber to be a channel or is the entire spectrum a single channel? Effectively I want the neural net operators to operate on each spectrum independently of the other spectra in a particular batch. That is, if I do a 1-D convolution layer, I want the kernel to operate only on a single spectrum at a time (not convolve multiple spectra together).

I’ve so far tried two different structures, all relatively simple and taken from literature on using CNNs with raman spectroscopy data. Structure 1 (taken from Deep learning-based component identification for the Raman spectra of mixtures - Analyst (RSC Publishing)):

```
layer1 = Dropout(p=0.5)(
Maxpool1d(kernel_size=2, stride=2, padding=1)(
leaky_relu(
Conv1d(in_channels=1, out_channels=32, kernel_size=5, stride=2, padding=2)
)
)
)
layer2 = Dropout(p=0.5)(
Maxpool1d(kernel_size=2, stride=2, padding=1)(
leaky_relu(
Conv1d(in_channels=32, out_channels=64, kernel_size=5, stride=2, padding=2)
)
)
)
layer3 = Linear(8128, 1024)(
torch.flatten(start_dim=1)
)
layer4 = Linear(1024, 1)
```

When I’m training this, I do get `nan`

loss after some time, and I’m not entirely sure why. If you have any suggestions on that, I’d very much appreciate it. Structure 2 (taken from Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network - ScienceDirect):

```
layer1 = maxpool1d(kernel_size=2, stride=2)(
leaky_relu(
batchnorm1d(num_features=5)(
conv1d(in_channels=1, out_channels=5, kernel_size=10, stride=2)
)
)
)
layer2 = dropout(p=0.5)(
leaky_relu(
batchnorm1d(num_features=5)(
Linear(in_features=2125, out_features=5)(torch.flatten(start_dim=1))
)
)
)
layer3 = Linear(in_features=5, out_features=1)
```

This model also reached `nan`

loss after some time, and I’m not sure why. Anyhow, this is what I’ve done so far, and I appreciate any advice on either of these models, but especially on the data input format/shape. Thanks!