Starting Out with Python (4th Edition)
4th Edition
ISBN: 9780134444321
Author: Tony Gaddis
Publisher: PEARSON
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Chapter 10, Problem 2TF
Object reusability has been a factor in the increased use of object-oriented
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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN).
graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
Please provide me with the output image of both of them . below are the diagrams code
I have two diagram :
first diagram code
graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…
Chapter 10 Solutions
Starting Out with Python (4th Edition)
Ch. 10.1 - What is an object?Ch. 10.1 - Prob. 2CPCh. 10.1 - Why is an object's internal data usually hidden...Ch. 10.1 - What are public methods? What are private methods?Ch. 10.2 - You hear someone make the following comment: "A...Ch. 10.2 - In this chapter, we use the metaphor of a cookie...Ch. 10.2 - What is the purpose of the _ _init_ _ method? When...Ch. 10.2 - Prob. 8CPCh. 10.2 - In a Python class, how do you hide an attribute...Ch. 10.2 - What is the purpose of the _ _str_ _ method?
Ch. 10.2 - Prob. 11CPCh. 10.3 - What is an instance attribute?Ch. 10.3 - Prob. 13CPCh. 10.3 - What is an accessor method? What is a mutator...Ch. 10.4 - Prob. 15CPCh. 10.4 - Prob. 16CPCh. 10.4 - When designing an object-oriented application, who...Ch. 10.4 - How do you identify the potential classes in a...Ch. 10.4 - What are a classs responsibilities?Ch. 10.4 - What two question should you ask to determine a...Ch. 10.4 - Will all of a class's action always be directly...Ch. 10 - The _______ programming practice is centered on...Ch. 10 - The ___________ programming practice is centered...Ch. 10 - A(n) _____ is a component of a class that...Ch. 10 - Prob. 4MCCh. 10 - By doing this, you can hide a classs attribute...Ch. 10 - Prob. 6MCCh. 10 - A(n) ________ method stores a value in a data...Ch. 10 - Prob. 8MCCh. 10 - If a class has a method named _ _str_ _ , which of...Ch. 10 - A set of standard diagrams for graphically...Ch. 10 - In one approach to identifying the classes in a...Ch. 10 - Prob. 12MCCh. 10 - The practice of procedural programming is centered...Ch. 10 - Object reusability has been a factor in the...Ch. 10 - It is a common practice in object-oriented...Ch. 10 - Prob. 4TFCh. 10 - Starting an attribute name with two underscores...Ch. 10 - You cannot directly call the _ _ str _ _ method.Ch. 10 - One way to find the classes needed for an...Ch. 10 - Prob. 1SACh. 10 - Why should an object's data attributes be hidden...Ch. 10 - What is the difference between a class and an...Ch. 10 - The following statement calls an object's method....Ch. 10 - Prob. 5SACh. 10 - In a Python class, how do you hide an attribute...Ch. 10 - Prob. 7SACh. 10 - Suppose my_car is the name of a variable that...Ch. 10 - Prob. 2AWCh. 10 - Look at the following description of a problem...Ch. 10 - Pet Class The Pet class Write a class named Pet,...Ch. 10 - Car Class Write a class named Car that has the...Ch. 10 - Personal Information Class Design a class that...Ch. 10 - Employee Class Write a class named Employee that...Ch. 10 - RetailItem Class Write a class named RetailItem...Ch. 10 - Patient Charges Write a class named Patient that...Ch. 10 - Employee Management System This exercise assumes...Ch. 10 - Cash Register This exercise assumes you have...Ch. 10 - Trivia Game In this programming exercise, you will...
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- I'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…arrow_forwardThe knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity. For reference, I will attach a second image…arrow_forwardNote : please avoid using AI answer the question by carefully reading it and provide a clear and concise solutionHere is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns…arrow_forward
- Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…arrow_forwardhere is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…arrow_forwarddetails explanation and background We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…arrow_forward
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