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8 changes: 4 additions & 4 deletions examples/mnist/reservoir.py
Original file line numberDiff line numberDiff line change
Expand Up@@ -180,7 +180,7 @@ def forward(self, x):


# Create and train logistic regression model on reservoir outputs.
model = NN(n_neurons, 10).to(device_id)
model = NN(n_neurons, 10).to(device)
criterion = torch.nn.MSELoss(reduction="sum")
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)

Expand All@@ -193,7 +193,7 @@ def forward(self, x):
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(s)
label = torch.zeros(1, 1, 10).float().to(device_id)
label = torch.zeros(1, 1, 10).float().to(device)
label[0, 0, l] = 1.0
loss = criterion(outputs.view(1, 1, -1), label)
avg_loss += loss.data
Expand All@@ -211,7 +211,7 @@ def forward(self, x):
for (i, dataPoint) in pbar:
if i > n_iters:
break
datum = dataPoint["encoded_image"].view(time, 1, 1, 28, 28).to(device_id)
datum = dataPoint["encoded_image"].view(time, 1, 1, 28, 28).to(device)
label = dataPoint["label"]
pbar.set_description_str("Testing progress: (%d / %d)" % (i, n_iters))

Expand DownExpand Up@@ -250,7 +250,7 @@ def forward(self, x):
outputs = model(s)
_, predicted = torch.max(outputs.data.unsqueeze(0), 1)
total += 1
correct += int(predicted == label.long().to(device_id))
correct += int(predicted == label.long().to(device))

print(
"\n Accuracy of the model on %d test images: %.2f %%"
Expand Down