NIT6004 Assignment Help
Introduction: Image classification is the most crucial domain in which neural networks play a significant role. The neural network accepts the input images and provides an output classification. In this project, develop an image classifier using basic neural networks with a few different optimizers.
Dataset: The MNIST digits dataset provided by Yann Le Cun, is a well-known dataset for classification. It includes images with 255 pixels 28 x 28 greyscale and labelled with a number from 0 to 9, denoting the appropriate digit. There are 60,000 training images and 10,000 testing datasets.
Task 1: Import Libraries:
- Import required libraries (e.g., import tensorflow as tf)
Task 2: Import Dataset:
- Load dataset using Keras API.
- A dataset must be pre-processed before training the network, if you check the image in the training, you will see pixel value range from 0 to 255, scale these values range from 0 to 1. [note it is important that both training and testing dataset pre-processed same way].
Task 3: Build a Classifier using MLP
- The layer is the most fundamental component of a neural network. Data is put into layers, and they extract representations from it. Choose the number of stacking layers so that model representations should, be useful for the given task.
- Task 4: Compile the Model
- A few more parameters are required before the model can be used for training. These are added at the build step of the model: Choose these parameters. a.1 Loss function
Task 5: Train and Test the model.
- Feed the training data to the built model.
- Ask the model to make predictions about a test set.
- Verify that the predictions match the labels from the test labels.
Prepare a report: (1000-1200 words)
Your report should contain the following:
2) Literature Review (In a literature review provide a comprehensive summary of previous work).
3) The methods applied for solving each task and the reason for choosing. 4) Results: Take a screenshot of the results and attach it to the document. 5) Challenges and problems during the project
6) References: cite all the information used in this report.
7) Applies integrity system and ethical theories to evaluate all possible ethical issues in this scenario, and demonstrate your understanding of IT Professional ethics by proposing solutions for the ethical issues discussed.