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