def run_model(model, training_data, testing_data):
#Trains the model for 5 epochs in batches of 10
epochs = 5
training_loss = []
for epoch in range(epochs):
train_loss = 0
#Work with the training data in batches of 10 to make program more responsive
for batch, (data,target) in enumerate(training_data):
optimizer.zero_grad()
output=model(data)
loss=criterion(output,target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if batch%10==9:
print("Epoch: {}, Total Images: {}\nAverage Training Loss: {}".format(epoch+1,batch+1,train_loss/10))
training_loss.append(train_loss)
train_loss=0.0
print("Total training loss {}".format(training_loss))
#Test the model using the test dataset
test_loss = 0
correct = 0
attempted = 0
model.eval()
for data,target in testing_data:
output = None
with torch.no_grad():
output = model(data)
processed_output = post_processing(output)
for i in range(len(processed_output)):
attempted += 1
if processed_output[i] == target[i]:
correct += 1
print("Predictions: {}".format(processed_output))
print("Target: {}".format(target))
print("-----")
loss = criterion(output,target)
test_loss += loss.item()
avg_loss=test_loss/attempted
print("Average total loss is {:.6f}".format(avg_loss))
print("{} correct predictions out of {} total images".format(correct, attempted))
def run_model_demo(model, training_data, image_path, image_transformer):
#Trains the model for 5 epochs in batches of 10
epochs = 5
training_loss = []
for epoch in range(epochs):
train_loss = 0
#Work with the training data in batches of 10 to make program more responsive
for batch, (data, target) in enumerate(training_data):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
if batch % 10 == 9:
print(
"Epoch: {}, Total Batches: {}\nAverage Training Loss: {}".
format(epoch + 1, batch + 1, train_loss / 10))
training_loss.append(train_loss)
train_loss = 0.0
print("Total training loss {}".format(training_loss))
#Run the model demo
print("The model is trained and ready to make predictions!")
model.eval()
while True:
image = input(
"Enter the file name of the image to be evaluated or E to exit: ")
if image == "E":
print("Thanks for participating!")
break
user_image = UserData(image_path, image, image_transformer)
user_data = DataLoader(user_image)
for data in user_data:
output = None
with torch.no_grad():
output = model(data)
processed_output = post_processing(output)
guess = ints_to_label[processed_output.item()]
print("The guess is: {}".format(guess))
visualize_guess(image, guess)