BMS5010代做、代写Python/Java程序

日期：2025-04-15 08:40

2024/25 Semester B - BMS5010 - Assignment 3

Task:

1. Use Keras to implement a three-layer feedforward neural network with two hidden layers for binary

classification.

2. Select a dataset from sklearn.datasets and apply the implemented classifier to it.

3. Evaluate the model's performance on the selected dataset using all the metrics discussed in the lecture

slides.

4. Conduct experiments to analyze the impact of increasing the number of hidden layers from two to

three, using evaluation metrics to compare results.

5. Write a report detailing your code and evaluation results, including the following sections:

Introduction, Implementation, Results, Discussion, and References.

Assessment:

- You must submit both the Jupyter Notebook (5 marks) and the PDF report (10 marks) via

Canvas-Assignment by April 15th at 6:00 PM.

- The word count must be at least 1,500 words. References should be excluded from the word count.

- While there is no strict minimum number of references required, failure to properly cite sources

will result in a deduction of marks.

Notification:

- Your submitted report will undergo plagiarism detection and Artificial Intelligence-Generated

Content (AIGC) analysis using Turnitin service provided on CityU Canvas (Figure 1). The

results must meet the following thresholds:

1. Similarity Ratio < 10%

2. AI Ratio < 20%

- Failure to meet these requirements will be considered academic misconduct, resulting in a final

course grade of F (Unsatisfactory). Additionally, the case will be reported to BMS, SGS, and

ARRO.

- We will only consider the similarity ratio and AI ratio provided by the Turnitin service on CityU

Canvas (Figure 1) for assessment. Ratios generated from any third-party services will not be

accepted.

Figure 1