def autoencoder_subtask_deep_fashion():
# train AET with DeepFashion
encoder_trained, losses = train_autoencoder_deep_fashion()
plot_n_curves(losses,
"Train losses",
"Loss training autoencoder DeepFashion",
axis2="Loss")
# transfer learning with DeepFashion
encoder_ft, train_losses, val_losses, train_acc, val_acc = fine_tune_autoencoder_deep_fashion(
encoder_trained)
# test with DeepFashion
average_test_loss, average_test_accuracy = test_autoencoder_deep_fashion(
encoder_ft)
plot_summary([train_acc, val_acc],
average_test_accuracy,
["train accuracy", "val accuracy", "test accuracy"],
"Accuracy autoencoder DeepFashion",
axis2="Accuracy")
plot_summary([train_losses, val_losses],
average_test_loss,
["train loss", "val loss", "test average loss"],
"Loss autoencoder DeepFashion",
axis2="Loss")
return average_test_loss, average_test_accuracy
def autoencoder_subtask_fashion_mnist():
# train autoencoder with FashionMNIST
encoder_trained, losses = train_autoencoder_mnist()
plot_n_curves(losses,
"Train losses",
"Loss train autoencoder Fashion MNIST",
axis2="Loss")
# transfer learning with FashionMNIST
encoder_ft, train_losses, val_losses, train_acc, val_acc = fine_tune_autoencoder_mnist(
encoder_trained)
# test with FashionMNIST
average_test_loss, average_test_accuracy = test_autoencoder_mnist(
encoder_ft)
plot_summary([train_acc, val_acc],
average_test_accuracy,
["train accuracy", "val accuracy", "test accuracy"],
"Accuracy test autoencoder FashionMNIST",
axis2="Accuracy")
plot_summary([train_losses, val_losses],
average_test_loss,
["train loss", "val loss", "test average loss"],
"Loss test autoencoder FashionMNIST",
axis2="Loss")
return average_test_loss, average_test_accuracy
def exemplar_cnn_subtask_deep_fashion():
# train exemplar cnn with DeepFashion
ex_cnn_trained, losses, accuracies = train_exemplar_cnn_deep_fashion()
plot_n_curves([losses], ["train loss"],
"Loss train ExemplarCNN DeepFashion",
axis2="Loss")
plot_n_curves([accuracies], ["train accuracy"],
"Accuracy train ExemplarCNN DeepFashion",
axis2="Accuracy")
# fine tune exemplar cnn with DeepFashion
ex_cnn_finetuned, train_losses, val_losses, train_acc, val_acc = fine_tune_exemplar_cnn_deep_fashion(
ex_cnn_trained)
# test with DeepFashion
average_test_loss, average_test_accuracy = test_classification_on_exemplar_cnn_deep_fashion(
ex_cnn_finetuned)
plot_summary([train_acc, val_acc],
average_test_accuracy,
["train accuracy", "val accuracy", "test accuracy"],
"Accuracy Test ExemplarCNN Deep Fashion",
axis2="Accuracy")
plot_summary([train_losses, val_losses],
average_test_loss, ["train loss", "val loss", "test loss"],
"Loss Test ExemplarCNN Deep Fashion",
axis2="Loss")
return average_test_loss, average_test_accuracy
def rotation_subtask_fashion_mnist():
# train rotation net with FashionMNIST
rotnet_trained, train_losses, val_losses, train_acc, val_acc = train_rotation_net(
)
plot_n_curves([train_losses, val_losses], ["train loss", "val loss"],
"Loss rotation FashionMNIST",
axis2="Loss")
plot_n_curves([train_acc, val_acc], ["train accuracy", "val accuracy"],
"Accuracy rotation FashionMNIST",
axis2="Accuracy")
# fine tune rotation net with FashionMNIST
rotnet_ft, train_losses_ft, val_losses_ft, train_acc_ft, val_acc_ft = fine_tune_rotation_model(
rotnet_trained)
# test with FashionMNIST
average_test_loss, average_test_accuracy = test_classification_on_rotation_model(
rotnet_ft)
plot_summary([train_acc_ft, val_acc_ft],
average_test_accuracy,
["train accuracy", "val accuracy", "test accuracy"],
"Accuracy Test Rotation FashionMNIST",
axis2="Accuracy")
plot_summary([train_losses_ft, val_losses_ft],
average_test_loss, ["train loss", "val loss", "test loss"],
"Loss Test Rotation FashionMNIST",
axis2="Loss")
return average_test_loss, average_test_accuracy