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pytorch-ai/wandb/run-20200127_142807-st277t0v/diff.patch
Clemens-Dautermann 55cff9b18f Trained
2020-01-27 20:58:35 +01:00

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diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..28f9aa9 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,13 +54,15 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ torch.save(net, './nets/net_gpu_' + str(epoch) + ".pt")
correct = 0
total = 0
with torch.no_grad():
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..82d4d6e 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
@@ -1,173 +1,18 @@
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