# Compute distances and output
dis = sed(x_map, protos_map)
return dis, plabels
# Build the GLVQ model
model = Model()
# Print a summary of the model
print(model)
# Optimize using Adam optimizer from `torch.optim`
optimizer = torch.optim.Adam(model.parameters(), lr=0.001_0)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=75, gamma=0.1)
criterion = GLVQLoss(squashing="identity", beta=10)
# Training loop
for epoch in range(150):
epoch_loss = 0.0 # zero-out epoch loss
optimizer.zero_grad() # zero-out gradients
for xb, yb in train_loader:
# Compute loss
distances, plabels = model(xb)
loss = criterion([distances, plabels], yb)
epoch_loss += loss.item()
# Backprop
loss.backward()
# Take a gradient descent step
optimizer.step()
scheduler.step()
eye_(self.omegas)
def forward(self, x):
protos = self.p1.prototypes
plabels = self.p1.prototype_labels
omegas = self.omegas
dis = lomega_distance(x, protos, omegas)
return dis, plabels
# Build the model
model = Model()
# Optimize using Adam optimizer from `torch.optim`
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
x_in = torch.Tensor(x_train)
y_in = torch.Tensor(y_train)
# Training loop
title = "Prototype Visualization"
fig = plt.figure(title)
for epoch in range(100):
# Compute loss
dis, plabels = model(x_in)
loss = criterion([dis, plabels], y_in)
y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
acc = accuracy_score(y_train, y_pred)
log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
log_string += f"Acc: {acc * 100:05.02f}%"
nclasses=3,
prototype_initializer='zeros')
def forward(self, x):
protos = self.p1.prototypes
plabels = self.p1.prototype_labels
dis = euclidean_distance(x, protos)
return dis, plabels
# Build the GLVQ model
model = Model()
# Optimize using SGD optimizer from `torch.optim`
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
# Training loop
fig = plt.figure('Prototype Visualization')
for epoch in range(70):
# Compute loss.
distances, plabels = model(torch.tensor(x_train))
loss = criterion([distances, plabels], torch.tensor(y_train))
print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}')
# Take a gradient descent step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Get the prototypes form the model