use divider inbetween steps
@@ -84,7 +84,7 @@ def debug_model(model: Model, *, print_settings: Optional[Dict[str, Any]] = None
# STEP 0: Printing before training
msg.info(f"Analysing model with ID {model.id}")
if print_settings.get("print_before_training"):
- msg.info(f"Before training:")
+ msg.divider(f"STEP 0 - before training")
_print_model(model, print_settings)
# STEP 1: Initializing the model and printing again
@@ -94,7 +94,7 @@ def debug_model(model: Model, *, print_settings: Optional[Dict[str, Any]] = None
with data_validation(False):
model.initialize(X=_get_docs(), Y=Y)
if print_settings.get("print_after_init"):
- msg.info(f"After initialization:")
+ msg.divider(f"STEP 1 - after initialization")
_print_model(model, print_settings)
# STEP 2: Updating the model and printing again
@@ -106,13 +106,14 @@ def debug_model(model: Model, *, print_settings: Optional[Dict[str, Any]] = None
get_dX(dY)
model.finish_update(optimizer)
if print_settings.get("print_after_training"):
- msg.info(f"After training:")
+ msg.divider(f"STEP 2 - after training")
_print_model(model, print_settings)
# STEP 3: the final prediction
prediction = model.predict(_get_docs())
if print_settings.get("print_prediction"):
- msg.info(f"Prediction:", str(prediction))
+ msg.divider(f"STEP 3 - prediction")
+ msg.info(str(prediction))
def get_gradient(model, Y):
