import torch.utils.data
import NumericalDataLoad
import Discriminator
db = NumericalDataLoad.NumericalData('Tesla.csv', 6)
train = torch.utils.data.DataLoader(db, batch_size=32, shuffle=True)
a = next(iter(train))
t = torch.randn(2, 6)
model = Discriminator.BaseDiscriminator(6)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = torch.nn.MSELoss()
out1 = model(a[0])
loss = criterion(out1, a[1])
loss.backward()
optimizer.step()
print(loss)
