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feat: do not print info about results if not in interactrive mode

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This commit is contained in:
Krzysztof Rudnicki 2023-05-16 19:46:33 +02:00
parent f85f8ee482
commit ebe16a206d
1 changed files with 28 additions and 14 deletions
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42
lab5/code/main.py
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@ -105,14 +105,14 @@ def single_train_iteration(
loss.backward()
training_parameters['optimizer'].step()
# Print loss value for every learning step
if (batch_idx + 1) % 100 == 0:
"""if (batch_idx + 1) % 100 == 0:
print(
f'''
Epoch [{epoch+1}/{training_parameters['hyperparameters']["num_epochs"]}],
Step [{batch_idx+1}/{len(training_parameters['loaders']['train_loader'])}],
Loss: {loss.item():.4f}
'''
)
)"""
# Append loss value for every learning step
loss_values.append(loss.item())
return data, training_parameters['optimizer']
@ -144,7 +144,7 @@ def set_training_parameters(hyperparameters, loaders, model, criterion, optimize
}
def training_loop(training_parameters):
def training_loop(training_parameters, print_info=True):
"""
Train network for all epochs
"""
@ -156,13 +156,13 @@ def training_loop(training_parameters):
data, training_parameters, targets, batch_idx, epoch
)
calculate_accuracy_epoch(
training_parameters, epoch)
training_parameters, epoch, print_info)
calculate_validation_set_accuracy(
training_parameters, epoch)
training_parameters, epoch, print_info)
return epoch, training_parameters['loaders']['train_loader']
def calculate_accuracy_epoch(training_parameters, epoch):
def calculate_accuracy_epoch(training_parameters, epoch, print_info=True):
""" Calculate accuracy on train set after each epoch """
correct = 0
total = 0
@ -173,12 +173,13 @@ def calculate_accuracy_epoch(training_parameters, epoch):
total += targets.size(0)
correct += (predicted == targets).sum().item()
train_accuracy = 100 * correct / total
print(
f"Accuracy on Train Set after Epoch {epoch+1}: {train_accuracy:.2f}%")
if print_info:
print(
f"Accuracy on Train Set after Epoch {epoch+1}: {train_accuracy:.2f}%")
train_acc_values.append(train_accuracy)
def calculate_validation_set_accuracy(training_parameters, epoch):
def calculate_validation_set_accuracy(training_parameters, epoch, print_info=True):
""" Calculate accuracy on validation set after each epoch """
correct = 0
total = 0
@ -190,10 +191,11 @@ def calculate_validation_set_accuracy(training_parameters, epoch):
correct += (predicted == targets).sum().item()
validation_accuracy = 100 * correct / total
print(
f"Accuracy on Validation Set after Epoch {epoch+1}: {validation_accuracy:.2f}%"
)
print("---")
if print_info:
print(
f"Accuracy on Validation Set after Epoch {epoch+1}: {validation_accuracy:.2f}%"
)
print("---")
val_acc_values.append(validation_accuracy)
@ -211,7 +213,7 @@ def main_part(show_plot=True):
TRAIN_LOADER, TEST_LOADER)
TRAINING_PARAMETERS = set_training_parameters(
HYPERPARAMETERS, LOADERS, MODEL, CRITERION, OPTIMIZER)
training_loop(TRAINING_PARAMETERS)
training_loop(TRAINING_PARAMETERS, show_plot)
file = open("results.txt", "a")
file.write(
"-------------------------------------------------------------------------------------")
@ -260,29 +262,41 @@ if __name__ == "__main__":
OPTIMIZER_TYPE = 'Adam'
learning_rate_values = [0.1, 0.01, 0.001]
i = 0
MAX_TESTS = 17
for lr in learning_rate_values:
LEARNING_RATE = lr
main_part(False)
i += 1
print(f"Test {i}/{MAX_TESTS} ran")
LEARNING_RATE = 0.001
batch_size_values = [64, 128, 256]
for bs in batch_size_values:
BATCH_SIZE = bs
main_part(False)
i += 1
print(f"Test {i}/{MAX_TESTS} ran")
BATCH_SIZE = 64
hidden_layers_values = [1, 2, 3]
for hl in hidden_layers_values:
NUM_HIDDEN_LAYERS = hl
main_part(False)
i += 1
print(f"Test {i}/{MAX_TESTS} ran")
NUM_HIDDEN_LAYERS = 2
width_values = [64, 128, 256, 512, 1024]
for width in WIDTH:
WIDTH = width
main_part(False)
i += 1
print(f"Test {i}/{MAX_TESTS} ran")
WIDTH = 128
for optimizer in ['SGD', 'SGD_Momentum', 'Adam']:
OPTIMIZER_TYPE = optimizer
main_part(False)
i += 1
print(f"Test {i}/{MAX_TESTS} ran")