Hello,

I’m new to pytorch and run into first problem right away and hope to get some help here.

So this is my data generating function:

```
n_samples = 100
X = np.random.normal(size=(n_samples, 1))
y = np.random.normal(np.cos(5.*X) / (np.abs(X) + 1.), 0.1).ravel()
X_pred = np.atleast_2d(np.linspace(-3., 3., num=100)).T
X = np.hstack((X, X**2, X**3))
X_pred = np.hstack((X_pred, X_pred**2, X_pred**3))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train, y_train, X_test, y_test, X_pred = map(torch.tensor, (X_train, y_train, X_test, y_test, X_pred))
train_ds = TensorDataset(X_train, y_train)
train_dl = DataLoader(train_ds, batch_size=6)
```

My first neural network looks like this:

```
input_dim = 3
hidden_dim = 100
output_dim = 1
model = torch.nn.Sequential(
torch.nn.Dropout(p=0.1),
torch.nn.Linear(input_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Dropout(p=0.1),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Dropout(p=0.1),
torch.nn.Linear(hidden_dim, output_dim),
)
loss_fn = torch.nn.MSELoss()
learning_rate = 1e-3
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.004)
```

This is m training loop:

```
for epoch in range(40):
for i, data in enumerate(train_dl, 0):
# get the inputs
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs.float())
loss = loss_fn(outputs, labels.float())
loss.backward()
optimizer.step()
# print statistics
if epoch % 10 == 0:
print(loss_fn(outputs, labels.float()))
print(loss_fn(outputs, labels.float()))
print('Finished Training')
```

My problem is that the network doesn’t fit at all. When i plot the results it is just a straight line. I tried for hours tune different parameters and look for errors in my code but unfortunatly I did not come to better result.

Hope someone has an idea or hint what do change in the model.