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Neural Network Classifier | Machine Learning Assignment Help | Realcode4you

Machine learning assignment help


In this homework assignment, you need to implement a simple 2-hidden-layer Multi-Layer

Neural Network using Python and Numpy. You are given data generated from three blackboxes:

blackbox21, blackbox22, and blackbox23. The description and tasks for each blackbox are the same. The following instruction is for blackbox21 as an example, and you can apply the same to the blackbox22 and blackbox23 as well.

1. Homework Description:

Please use Python 3 to implement your homework assignment.

1.1 Data Description

In this assignment, you are given a set of training data and a set of testing data generated from a specific blackbox, say blackbox21, which you may not know the secret function inside. You can use the training set to develop your network and use the testing set to measure the accuracy of your network during the development. For grading, we will generate our hidden testing data from the same blackbox to test the performance of your submitted NN. For blackbox21, you are given:

● blackbox21_train.csv : labeled training data generated from a blackbox21

● blackbox21_test.csv : unlabeled testing data

● blackbox21_example_predictions.csv : example output, which is also the true

class labels for blackbox21_test.csv so you can get to know the format of output

and measure your model’s performance while you are developing your program.

This data format is similar to the last homework assignment. However, The label (target value) of data in this homework assignment is not binary . So this means in this homework

assignment, you need to implement a Multi-Class Classifier. Meanwhile, there are three

features in the blackboxes, not two.

1.2 Task Description

Your task is to implement a multi-hidden-layer neural network learner (see 1.3 model description part for details of neural network you need to implement), named as , that will

(1) Construct a neural network classifier from the given training data,

(2) Use the learned classifier to classify the unlabeled test data, and

(3) Output the predictions of your classifier on the test data into a file named

blackbox2*_predictions.csv in the same directory as the . py,

(4) Finish in 5 minutes (to train one model for one blackbox) .

Your program will take two input files and produce one output file as follows:

python3 training_data_path testing_data_path

⇒ prediction_file

For example,

python3 blackbox21_train.csv blackbox21_test.csv

⇒ blackbox21_predictions.csv

Note: input files may not be in the same directory as your python script 1.

In other words, your algorithm file will take labeled training data , unlabeled testing data as input, and output your classification predictions on testing data as

output. In your implementation, please do not use any existing machine learning library call .

You must implement the algorithm yourself. Please develop your code yourself and do not copy from other students or from the Internet.

The format of blackbox2 *_train.csv looks like:

x1, x2, x3, y

Where x1, x2, and x3 are the attribute values and y is the label, and blackbox2 *_test.csv are

unlabeled. Notice that the data here has 3 (not 2) attributes .

Your output blackbox2*_predictions.csv will look like





… (A single column indicates the predicted class labels for each unlabeled sample in the

input test file) The format of your blackbox2*_predictions.csv file is crucial. It has to be in the exact same name and format so that it can be parsed correctly to compare with true labels by grading scripts.

When we grade your algorithm, we will use the same training data but some unlabeled hidden testing data (generated from the same blackbox21) instead of the testing data that was given to you. Your code will be autograded for technical correctness. Please name your file correctly, or you will wreak havoc on the autograder. The maximum running time to train a model is 5 minutes (for a single blackbox) , so please make sure your program finishes in 5 minutes.

1.3 Model Description

The basic structure model of neural network in this homework assignment is as below.

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