def train(self):
if self.find_lr == True:
# Navigate to output folder in parent directory
go_up_three_dirs()
# Plot learning rate finder plot
self.lr_finder.plot_loss(
"output/lr_finder_plot_A1.png")
else:
# Plot training loss accuracy and learning rate change
# Navigate to output folder in parent directory
go_up_three_dirs()
plot_train_loss_acc_lr(
self.history,
int(self.epochs/2),
self.schedule,
self.schedule_type,
"A1",
"output/train_loss_acc_A1_xception.png",
"output/lr_A1_xception.png")
# Get the training accuracy
training_accuracy = self.history.history['val_acc'][-1]
return training_accuracy
def train(self):
if self.find_lr == True:
# Navigate to output folder in parent directory
go_up_three_dirs()
print("[INFO] Creating learning rate finder plot...")
# Plot learning rate finder plot
self.lr_finder.plot_loss(
"output/lr_finder_plot_B2.png")
else:
# Plot training loss accuracy and learning rate change
# Navigate to output folder in parent directory
go_up_three_dirs()
plot_train_loss_acc_lr(
self.history,
self.epochs,
self.schedule,
self.schedule_type,
"B2",
"output/train_loss_acc_B2_cnn.png",
"output/lr_B2_cnn.png")
# Get the training accuracy
training_accuracy = self.history.history['val_acc'][-1]
return training_accuracy
def test(self):
# Go back to image folder
os.chdir("data/dataset_test_AMLS_19-20/celeba_test")
# Split ImageDataGenerator object for the test set into separate X and y test sets
X_test, y_test = get_X_y_test_sets(self.test_gen)
# Navigate to output folder in parent directory
go_up_three_dirs()
# Plot top losses
plot_top_losses(self.model, X_test, y_test, "output/plot_top_losses_A1_mlp.png")
# Get the test accuracy
test_accuracy = self.model.evaluate(X_test, y_test)[-1]
return test_accuracy
def test(self):
# Go back to image folder
os.chdir("data/dataset_test_AMLS_19-20/cartoon_set_test")
# Split ImageDataGenerator object for the test set into separate X and y test sets
X_test, y_test = get_X_y_test_sets(self.test_gen)
# Navigate to output folder in parent directory
go_up_three_dirs()
# Plot top losses
plot_top_losses(self.model, X_test, y_test, "output/plot_top_losses_B2_xception.png")
# Plot GradCam
plot_grad_cam(self.model, X_test, y_test, 3, "block14_sepconv2", "output/plot_top_5_gradcam_B2_xception.png")
# Get the test accuracy
test_accuracy = self.model.evaluate(X_test, y_test)[-1]
return test_accuracy
