def evaluate(self) -> MetricLogger:
obj_test = MetricLogger(one_hot=True)
self.model.eval()
for (x, y) in self.test_data:
preds_test = self.model.forward(x)
obj_test.log(preds_test, y)
return obj_test
def train(self):
model_name = type(self.model).__name__
since = datetime.now()
start_time = since.strftime("%H:%M:%S")
print(f"Training is started for {model_name} model at {start_time}")
self.model.fit(self.X_train, self.y_train)
time_elapsed = datetime.now() - since
print(f"Training for {model_name} model completed in {time_elapsed}")
pred = self.model.predict(self.X_val)
obj = MetricLogger(one_hot=False)
obj.log(pred, self.y_val)
print(f"Accuracy of {model_name} model:", obj.accuracy, sep='\n')
print(f"Precision of {model_name} model:", obj.precision, sep='\n')
print(f"Recall of {model_name} model:", obj.recall, sep='\n')
def evaluate(self):
pred = self.model.predict(self.X_test)
obj = MetricLogger(one_hot=False)
obj.log(pred, self.y_test)
return obj
def train(self, epochs: int, patience=None):
model_name = type(self.model).__name__
since = datetime.now()
start_time = since.strftime("%H:%M:%S")
print(
f"Training and Validating is started for {model_name} at {start_time}"
)
count = 0
obj_train = MetricLogger(one_hot=True)
obj_val = MetricLogger(one_hot=True)
if patience is None:
print("patience is None")
for e in range(epochs):
print('epoch', e)
obj_train.reset()
obj_val.reset()
for i, (x, y) in enumerate(self.train_data):
preds_train = self.model.forward(x)
loss = F.cross_entropy(preds_train, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
obj_train.log(preds_train, y)
count += 1
if i % 100 == 0:
self.logger.add_scalar("train loss (batches)",
loss.item(), count)
self.logger.add_scalar("train accuracy (batches)",
obj_train.accuracy, count)
for (x, y) in self.val_data:
preds_val = self.model.forward(x)
obj_val.log(preds_val, y)
count += 1
self.logger.add_scalar("validation accuracy (epochs)",
obj_train.accuracy, count)
time_elapsed = datetime.now() - since
print(
f"Training and Validating for {model_name} completed in {time_elapsed}"
)
if patience is not None and patience > 0:
losses = []
for e in range(epochs):
print('epoch', e)
obj_train.reset()
obj_val.reset()
self.model.train()
for i, (x, y) in enumerate(self.train_data):
preds_train = self.model.forward(x)
loss = F.cross_entropy(preds_train, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
obj_train.log(preds_train, y)
count += 1
if i % 100 == 0:
self.logger.add_scalar("train loss (batches)",
loss.item(), count)
self.logger.add_scalar("train accuracy (batches)",
obj_train.accuracy, count)
self.model.eval()
val_loss = 0
for (x, y) in self.val_data:
preds_val_Regu = self.model.forward(x)
loss = F.cross_entropy(preds_val_Regu, y)
val_loss += loss
obj_val.log(preds_val_Regu, y)
if len(losses) == 0 or val_loss < losses[-1]:
losses = [val_loss]
else:
losses.append(val_loss)
if len(losses) == patience:
print("Early stopping")
break
count += 1
self.logger.add_scalar("validation accuracy (epochs)",
obj_train.accuracy, count)
time_elapsed = datetime.now() - since
print(
f"Training and Validating for {model_name} completed in {time_elapsed}"
)
if patience is not None and patience <= 0:
time_elapsed = datetime.now() - since
print(
f"Training and Validating for {model_name} completed in {time_elapsed}"
)
raise ValueError("patience must be positive!")