Getting Nan values after first ietration

Hi, I am performing multilabel classification task. I am implementing the concept of Graph Variational Autoencoders. My target is to predict the correct adjacency matrix. From what i understand duringh the backward propogation it is returning Nans. I have run anomoly detection and it is throwing this error.

RuntimeError: Function ‘MmBackward0’ returned nan values in its 0th output. Any idea what is the cause of this?

Could you share a minimal reproducible example?

import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch_geometric.loader import DataLoader
from import Data
from torch_geometric.nn import GINConv, VGAE
from torch_geometric.nn.models import InnerProductDecoder
import matplotlib.pyplot as plt

device = torch.device(‘cuda:0’ if torch.cuda.is_available() else ‘cpu’)
batch_size = 32

class Encoder(nn.Module):

def __init__(self):
    super(Encoder, self).__init__()
    dim1 = 30
    dim2 = 25
    dim3 = 20
    dim4 = 15
    dim5 = 10
    self.mlp1 = nn.Sequential(nn.Linear(dim1, dim2), nn.ReLU(),\
                              nn.Linear(dim2, dim3))   
    self.mlp2 = nn.Sequential(nn.Linear(dim1, dim2), nn.ReLU(),\
                              nn.Linear(dim2, dim3))
    self.mlp3 = nn.Sequential(nn.Linear(dim3, dim5), nn.ReLU(),\
                              nn.Linear(dim5, dim5))
    self.conv1 = GINConv(self.mlp1)
    self.conv2 = GINConv(self.mlp2)
    self.conv3 = GINConv(self.mlp3) = True

def reparametrize(self, mu, log_var):
        std = torch.exp(0.5*log_var)
        eps = torch.randn_like(std)
        sample = mu+(eps*std)
        sample = mu
    return sample
def forward(self, x, edge_index ):
    # print('edge_index is', edge_index)
    x = x.float()
    mu = self.conv1(x,edge_index)
    log_var = torch.tanh(self.conv2(x,edge_index)
    lat_fea = self.reparametrize(mu, log_var)

    return lat_fea, mu, log_var

class AdjacencyMatrix(nn.Module):

  def forward_all(self, lat_fea, sigmoid = True):

        output from encoder
    sigmoid : TYPE, optional
        DESCRIPTION. The default is True.

    decodes the latent variables into a probablistic dense adjacency matrix

    adj_pred = torch.matmul(lat_fea, lat_fea.t())
    pred_adjacency = torch.sigmoid(adj_pred)
    return torch.sigmoid(adj_pred) if sigmoid else adj_pred

class VGAE(nn.Module):

def __init__(self):
    super(VGAE, self).__init__()
    self.encoder = Encoder()
    self.adjacency = AdjacencyMatrix()
    self.ns_loss = nn.BCELoss()

def forward(self, x, edge_index):
    z = self.encoder.forward(x,edge_index)[0]
    adj_pred = self.adjacency.forward_all(z) 
    return self.adjacency.forward_all(self.encoder(x, edge_index)[0])

def recons_loss(self, adj, adj_pred):
    recon_loss = self.ns_loss(adj_pred, adj)
    return self.ns_loss(adj_pred, adj)
def loss(self, ns_loss):
    mu, log_var = self.encoder.forward(x, edge_index)[1], self.encoder.forward(x, edge_index)[2]
    kld_new = -0.5 * torch.mean(torch.sum(1+2*log_var - mu**2 -log_var.exp()**2, dim = 1))
    loss = ns_loss + kld_new
    return ns_loss+ kld_new  

batch_size = 32
states = torch.load(‘’).to(device)

x_o = states[:,:270].reshape(-1,9,30).to(device)
edge_index_o = states[:,391:431].reshape(len(x_o),2,-1).to(torch.long).to(device)
prev_job_o = states[:,270:390].reshape(-1,4,30).to(device)
cur_mach = states[:,390].reshape(len(x_o),1).to(device)
active_edges_o = states[:,431].reshape(len(x_o),1).to(device)

dataset = []
for _x,_edg,_active_edges in
adj = torch.zeros(9,9).to(device)
edges = _edg[:,:int(_active_edges)]
for i in edges.t():
adj[i[0]][i[1]] = 1
dataset.append(Data(x =_x, edge_index = _edg[:,:int(_active_edges)],
adj = adj))
split = int(len(dataset)*0.7)
train_dataset = dataset[:split]
test_dataset = dataset[split:]
test_loader = DataLoader(test_dataset, batch_size = 32, drop_last = True,
shuffle = True)

model = VGAE().to(device)
model_optim = optim.Adam(model.parameters(), lr = 8e-04)

epoch = 1
epoch_counter = 0
tot_ns_loss = []
tot_acc = []
for ep in range(epoch):

train_loader = DataLoader(train_dataset, batch_size=1, drop_last = True,\
                      shuffle = True)
ep_acc = []
ep_ns_loss = 0
# ep_rew_loss = 0
count = 0
tot_loss = 0
print('\rEpoch Number:', epoch_counter, end = "")
for data in train_loader:
    x, edge_index, adj = data.x, data.edge_index, data.adj
    z = model.forward(x, edge_index)
    adj_pred = model.adjacency.forward_all(z)
    print('adj_pred is', adj_pred)
    print('adj is', adj)
    adj =
    ns_loss = model.recons_loss(adj_pred, adj)
    ns_loss =  model.loss(ns_loss)
    ep_ns_loss += ns_loss.item()


sure! I can share my code here. but my dataset is bit large, I am not able to share the dataset. Do you have any alternatives how i can share my dataset.

My dataset is one hot encoded arrays of size (len(dataset), 432). from there slicing the data I need and processing it.

This is how the error is.