def train(self, model, train_loader, valid_loader, workers):
criterion = BCELoss() # binary cross-entropy
# for RMSprop in PySyft each worker needs its own optimizer
worker_ids = [worker.id for worker in workers]
optims = Optims(worker_ids,
optim=RMSprop(model.parameters(),
lr=self.model_config.learning_rate))
early_stopping = EarlyStopping(
patience=self.model_config.early_stopping_patience)
epochs_finished = 0
for _ in range(self.model_config.epochs):
model.train()
for data, target in train_loader:
# At least two samples are needed for training.
# This may cause loosing up to N examples in training where N is number of workers.
if len(data) < 2:
continue
model.send(data.location)
opt = optims.get_optim(data.location.id)
opt.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
opt.step()
model.get()
model.eval()
valid_losses = []
for data, target in valid_loader:
model.send(data.location)
output = model(data)
loss = criterion(output, target)
valid_losses.append(loss.get().item())
model.get()
valid_loss = np.average(valid_losses)
epochs_finished += 1
if early_stopping.should_early_stop(valid_loss, model):
break
model.load_state_dict(early_stopping.best_model_state)
return model, epochs_finished
def train(self, model, train_loader, valid_loader):
criterion = BCELoss() # binary cross-entropy
optimizer = RMSprop(model.parameters(), lr=self.config.learning_rate)
early_stopping = EarlyStopping(
patience=self.config.early_stopping_patience)
epochs_finished = 0
for _ in range(self.config.epochs):
model.train()
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
model.eval()
valid_losses = []
for data, target in valid_loader:
output = model(data)
loss = criterion(output, target)
valid_losses.append(loss.item())
valid_loss = np.average(valid_losses)
epochs_finished += 1
if early_stopping.should_early_stop(valid_loss, model):
break
model.load_state_dict(early_stopping.best_model_state)
return model, epochs_finished