im getting a prediction, which makes me question my model. T_T .

I’m getting results/predictions of the form 2280x19, instead of an array of 2280 elements, `X_test.size()`

returns `torch.Size([2280, 30])`

`y_test.size()`

returns `torch.Size([2280])`

but my `cpu_pred.size()`

is of `torch.Size([2280, 19])`

I want the prediction to be of size 2280, so that i can compare with `y_test`

, for validation accuracy.

This is my script:

```
class DynamicNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(DynamicNet, self).__init__()
self.input_linear = torch.nn.Linear(D_in, H)
# self.weights_1 = torch.randn(D_in,H)
self.middle_linear = torch.nn.Linear(H, H)
# self.weights_2 = torch.randn(H, H)
self.output_linear = torch.nn.Linear(H, D_out)
def forward(self, x):
h_relu = self.input_linear(x).clamp(min=0)
for _ in range(np.random.randint(0, 3)):
h_relu = self.middle_linear(h_relu).clamp(min=0)
y_pred = self.output_linear(h_relu)
return y_pred
N, D_in, H, D_out = 64, 30, 100, 19
# model = torch.nn.Sequential(
# torch.nn.Linear(D_in, H),
# torch.nn.ReLU(),
# torch.nn.Linear(H, D_out),
# )
model = DynamicNet(D_in, H, D_out)
model = model.double()
criterion = torch.nn.CrossEntropyLoss()
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(100): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 64))
running_loss = 0.0
print('Finished Training')
```

this is how I’m making the predictions,

```
# pass it through the model
X_test_var = Variable(X_test, volatile=True)
prediction = model(X_test_var)
# get the result out and reshape it
cpu_pred = prediction.cpu()
result = cpu_pred.data.numpy()
# array_res = np.reshape(result, (28,28))
print(result)
```

I want the result to be a numpy array of shape (2280,). How should I proceed?

Im doing a multiclass classification of 19 classes, and they are not in one_hot_notation.