diff --git a/src/utils.py b/src/utils.py
index 23a3547..d5cdb60 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -584,9 +584,9 @@ def calc_iterations( epochs, lines, batch_size ):
 	iterations = int(epochs * lines / float(batch_size))
 	return iterations
 
+EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
 def schedule_learning_rate( iterations ):
-	schedule = [ 9, 18, 25, 33 ]
-	return [int(iterations * d) for d in schedule]
+	return [int(iterations * d) for d in EPOCH_SCHEDULE]
 
 def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate_schedule, mega_batch_factor, print_rate, save_rate, resume_path, voice ):
 	name = f"{voice}-finetune"
@@ -611,12 +611,12 @@ def optimize_training_settings( epochs, batch_size, learning_rate, learning_rate
 	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
 	messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
 
-	if iterations < print_rate:
-		print_rate = iterations
+	if epochs < print_rate:
+		print_rate = epochs
 		messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {print_rate}")
 	
-	if iterations < save_rate:
-		save_rate = iterations
+	if epochs < save_rate:
+		save_rate = epochs
 		messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_rate}")
 
 	if resume_path and not os.path.exists(resume_path):
diff --git a/src/webui.py b/src/webui.py
index 6d775f7..bc64e27 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -209,6 +209,9 @@ def save_training_settings_proxy( epochs, batch_size, learning_rate, learning_ra
 	iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
 	messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
 
+	print_rate = int(print_rate * iterations / epochs)
+	save_rate = int(save_rate * iterations / epochs)
+
 	messages.append(save_training_settings(
 		iterations=iterations,
 		batch_size=batch_size,
@@ -352,13 +355,13 @@ def setup_gradio():
 				with gr.Row():
 					with gr.Column():
 						training_settings = [
-							gr.Slider(label="Epochs", minimum=0, maximum=500, value=10),
-							gr.Slider(label="Batch Size", minimum=2, maximum=128, value=64),
+							gr.Number(label="Epochs", value=10, precision=0),
+							gr.Number(label="Batch Size", value=64, precision=0),
 							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
-							gr.Textbox(label="Learning Rate Schedule", placeholder="[ 100, 200, 300, 400 ]"),
-							gr.Slider(label="Mega Batch Factor", minimum=1, maximum=16, value=4, step=1),
-							gr.Number(label="Print Frequency", value=50),
-							gr.Number(label="Save Frequency", value=50),
+							gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
+							gr.Number(label="Mega Batch Factor", value=4, precision=0),
+							gr.Number(label="Print Frequency per Epoch", value=5, precision=0),
+							gr.Number(label="Save Frequency per Epoch", value=5, precision=0),
 							gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
 						]
 						dataset_list = gr.Dropdown( get_dataset_list(), label="Dataset", type="value" )