I am trying to train a CNN to identify arrhythmias in ECG (normal, atrial fibrillation, other, noisy). At first, the loss was reaming the same, I tried changing the loss function and the loss started declining by only .001. I soon found that my net was only outputting zeros.
I am using Google Colab (https://colab.research.google.com/drive/1fR29ffY9iJW-to7fgsjRVmAKo4jtImP1#scrollTo=cl0jdeW9IlUf 1) - w/ CrossEntropyLoss function.
Here is my code (w/ MSELoss function):
import os
import cv2
import numpy as np
from tqdm import tqdm
from scipy.io import loadmat
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

if torch.cuda.is_available():
 device = torch.device("cuda:0")
 print("Running on GPU -", device )
else:
 device = torch.device("cpu")
 print("Running on CPU -", device )


REBUILD_DATA = True # processing data

class ECG_DATA():
 ECG_LENGTH = 3000
 LABEL_SIZE = 485
 DATA = "physionet.org/files/challenge-2017/1.0.0/training/"
 NORMAL = "physionet.org/files/challenge-2017/1.0.0/training/RECORDS-normal" 
 AF = "physionet.org/files/challenge-2017/1.0.0/training/RECORDS-af"
 OTHER = "physionet.org/files/challenge-2017/1.0.0/training/RECORDS-other"
 NOISY = "physionet.org/files/challenge-2017/1.0.0/training/RECORDS-noisy"
 LABELS = {NORMAL: 0, AF: 1, OTHER:2, NOISY: 3}
 trainingData = []
 dataCount = {NORMAL: 0, AF: 0, OTHER: 0, NOISY: 0}

 def make_training_data(self):
   for records in self.LABELS:
     with open(records) as label:
       for ecgFile in tqdm(label):
         ecg = loadmat(self.DATA+ecgFile[:-1]+".mat")["val"][0].tolist()

         if records == self.NOISY:
           #self.zero_padding(ecg)
           for x in range(self.ECG_LENGTH, len(ecg), self.ECG_LENGTH):
             if self.dataCount[records] <= self.LABEL_SIZE and x <= len(ecg):
               self.trainingData.append([np.array(ecg[x-self.ECG_LENGTH:x]), np.eye(len(self.LABELS))[self.LABELS[records]]])
               self.dataCount[records] += 1

         elif self.dataCount[records] <= self.LABEL_SIZE and self.ECG_LENGTH <= len(ecg):
           self.trainingData.append([np.array(ecg[:self.ECG_LENGTH]), np.eye(len(self.LABELS))[self.LABELS[records]]])
           self.dataCount[records] += 1    
   
   print(self.dataCount)
   np.random.shuffle(self.trainingData)
   np.save("training_Data.npy", self.trainingData)
 
 def zero_padding(self, ecg):
   ecg += [0] * (self.ECG_LENGTH-(len(ecg)%self.ECG_LENGTH))
class Net(nn.Module):
   def __init__(self):
       super().__init__() # just run the init of parent class (nn.Module)
       self.conv1 = nn.Conv1d(1, 32, 5) # input is 1 image, 32 output channels, 5x5 kernel / window
       self.conv2 = nn.Conv1d(32, 64, 5) # input is 32, bc the first layer output 32. Then we say the output will be 64 channels, 5x5 kernel / window
       self.conv3 = nn.Conv1d(64, 128, 5)

       x = torch.randn(1,3000).view(-1,1,3000)
       self._to_linear = None
       self.convs(x)

       self.fc1 = nn.Linear(self._to_linear, 512) #flattening.
       self.fc2 = nn.Linear(512, 4) # 512 in, 2 out bc we're doing 2 classes (dog vs cat).

   def convs(self, x):
       x = F.max_pool1d(F.relu(self.conv1(x)), 1) # adjust shape of pooling?
       x = F.max_pool1d(F.relu(self.conv2(x)), 1) # x = F.max_pool1d(F.relu(self.conv1(x)), (2, 2))
       x = F.max_pool1d(F.relu(self.conv3(x)), 1)

       if self._to_linear is None:
           self._to_linear = x[0].shape[0]*x[0].shape[1]
       return x

   def forward(self, x):
       x = self.convs(x)
       x = x.view(-1, self._to_linear)  # .view is reshape ... this flattens X before 
       x = F.relu(self.fc1(x))
       x = self.fc2(x) # bc this is our output layer. No activation here.
       return F.softmax(x, dim=1)


net = Net().to(device)
print(net)

if REBUILD_DATA:
 ECG = ECG_DATA()
 ECG.make_training_data()

training_data = np.load("training_Data.npy", allow_pickle=True)
print(len(training_data))

optimizer = optim.Adam(net.parameters(), lr = 0.01)
loss_function = nn.MSELoss().to(device)

X = torch.Tensor([i[0] for i in training_data])
y = torch.Tensor([i[1] for i in training_data])

VAL_PCT = 0.1
val_size = int(len(X)*VAL_PCT)
print(val_size)

train_X = X[:-val_size]
train_y = y[:-val_size]

test_X = X[-val_size:]
test_y = y[-val_size:]
print(len(train_X), len(test_X))

BATCH_SIZE = 100
EPOCHS = 1
plot = []

for epoch in range(EPOCHS):
   for i in tqdm(range(0, len(train_X), BATCH_SIZE)): # from 0, to the len of x, stepping BATCH_SIZE at a time. [:50] ..for now just to dev
       #print(f"{i}:{i+BATCH_SIZE}")
       batch_X = train_X[i:i+BATCH_SIZE].view(-1,1,3000).to(device)
       batch_y = train_y[i:i+BATCH_SIZE].to(device)

       net.zero_grad()

       outputs = net(batch_X)
       loss = loss_function(outputs, batch_y)
       loss.backward()
       optimizer.step()    # Does the update
   
   plot.append([epoch, float(loss)])
   print(f"\nEpoch: {epoch}. Loss: {loss}")

plot = list(map(list, zip(*plot)))
plt.plot(plot[0], plot[1])