Hey,
I am new to PyTorch and I hope this is the right place to ask because I am sure this question would receive downvotes on StackOverflow.
However, this Code:
import torch
import torch.nn as nn
import numpy as np

#Initializing neuron_count and batchsize
n_in, n_hdn0, n_hdn1, n_hdn2, n_hdn3, n_hdn4, n_out, batchsize = 2,200,50,50,50,50,1,100;

input_arr = []
output_arr = []

print("Initializing numbers to train with...")
for i in range(10000):
    a = np.random.randint(0,1000)
    b = np.random.randint(0,1000)
    c = a*b
    input_arr.append([a,b])
    output_arr.append(c)
print("Initialized numbers to train with...")

print("Converting to tensors...")
input_arr = torch.IntTensor(input_arr)
output_arr = torch.IntTensor(output_arr)
print("Converting to tensors...")

print("Creating model...")
model = nn.Sequential(
    nn.Linear(n_in,n_hdn0), nn.Sigmoid,
    nn.Linear(n_hdn0, n_hdn1), nn.Sigmoid,
    nn.Linear(n_hdn1, n_hdn2), nn.Sigmoid,
    nn.Linear(n_hdn2, n_hdn3), nn.Sigmoid,
    nn.Linear(n_hdn3, n_hdn4), nn.Sigmoid,
    nn.Linear(n_hdn4, n_out), nn.Linear
)

print("Created model...")

print("Setting up for backpropagation...")
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
print("Set up for backpropagation...")

for epoch in range(50):
    # Forward Propagation

    y_pred = model(input_arr)
    # Compute and print loss
    loss = criterion(y_pred, output_arr)
    print('epoch: ', epoch, ' loss: ', loss.item())
    # Zero the gradients
    optimizer.zero_grad()

    # perform a backward pass (backpropagation)
    loss.backward()

    # Update the parameters
    optimizer.step()


print("Done.")

Throws an unexpected Error:

Traceback (most recent call last):
File “/Users/niclas/PycharmProjects/AI/Multiplier.py”, line 32, in 
nn.Linear(n_hdn4, n_out), nn.Linear
File “/usr/local/lib/python3.7/site-packages/torch/nn/modules/container.py”, line 53, in init
self.add_module(str(idx), module)
File “/usr/local/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 173, in add_module
torch.typename(module)))
TypeError: torch.nn.modules.activation.Sigmoid is not a Module subclass

Also, I am wondering if there is a proper way to automatically pass the whole input data into a function, which will divide the tensor into many tensors with correct batch_sizes automatically? (Although I cannot imagine this is possible…)
Yours sincerely,
Niclas