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The task is to implement a simple neural network that achieves an adequate performance on a real data set for predicting a real valued parameter

Regression with neural networks

  1. Introduction
    The task is to implement a simple neural network that achieves an adequate performance on a real data set for predicting a real valued parameter. The dataset consists of chemical properties (representation) of superconductors, and the parameter value to be predicted is their critical temperature in Kelvins (?K).
    Further information on the dataset is available at: http://archive.ics.uci.edu/ml/datasets/Superconductivty+Data
    The task is utilize the training dataset consisting of the properties of superconductors and their critical temperatures in order to learn a regression model. Then this model should be applied on the test dataset to predict the critical temperature of the superconductors in that dataset.
  2. Assignment
    Implement a simple neural network and the backpropagation algorithm in Java or Python! Use the trained model to predict the critical temperature for each test sample.
    2.1. Java
    The code must contain a Main class, and within this, a main() function. It will receive all inputs on the standard input, and should output the solution to the standard output. Upload the zipped source code files of your application to the BME MIT HomeWork portal. (https://hf.mit.bme.hu).
    2.2. Python
    The code must be a single python file, that will be run and receives all inputs onto the standard input, and it should write the solution to the standard output. Upload the zipped single python file to the BME MIT Homework portal. Use Python3.x, and only standard libraries are available (e.g. no numpy!) (https://hf.mit.bme.hu).
    2.3. Input
    The program receives all inputs via the standard input. The input consists of the representation of training samples, the corresponding critical temperatures, and also
    VIMIAC10 2019 3rd Major homework
    the representation of test samples. The character ’ ’ is used as a line separator. The input is structured according to the following:
  3. The first 17011 lines each contain a representation of a chemical compound, that is 81 parameters as real numbers separated by the ’ ’ character. These are the training samples.
  4. These are followed by 17011 temperature values, i.e. the target value to be learned (i.e. a single temperature value in each row).
  5. Lastly, 4252 test samples (chemical compound representations) for which the critical temperature has to be predicted. These are the test samples.
    The solution should implement the backpropagation algorithm. The scaling/normalization of data is recommended before learning. Note that the available CPU time for the code is approximately 120 CPU secs.
    2.4. Output
    The output contains the predictions for the test samples, i.e. a predicted temperature for each sample. The output should be formatted such that each row contains only one prediction, the order corresponds to the order of test samples. Rows should be separated by the character, the output should be written to standard output.
  6. Evaluation
    The evaluation is based on RMSE (root mean squared error):
    ,
    where ???? is the real value, and is the predicted value. A solution reaching a RMSE lower than 17.0 gets 12 points, however a solution above 23.0 gets 0 points. Between these two endpoints the evaluation is linear (the score is rounded to the nearest integer)

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  • The task is to implement a simple neural network that achieves an adequate performance on a real data set for predicting a real valued parameter
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    Writers Solution

    perceptron learning method and design an artificial neural network

    The objective of this project is to use the perceptron learning method and design an artificial neural network (ANN) to train a simple system (single layer perceptron) for the recognition of handwritten digits (0, 1, …, 9).

    Design a fully connected network structure of 784 input nodes and 10 output nodes.The input to your single layer network architecture will be a set of binary pixels representing a 28×28 image of handwritten digits. The output should indicate which of the digits (0,….,9) is in the input image.

    Use the MNIST database of handwritten digits available on Blackboard-Homepage-Handwritten Digits Dataset.

    Select a subset of the MNIST database consisting around 500 images of handwritten digits (0,…,9) for training the system, and use another 100 images for testing the system. Create binary or bipolar images of handwritten digits from gray scale images available in MNIST by simple thresholding (indicate the threshold value you used).

    Plot a learning curve that illustrates the mean square error versus iterations.

    (One iteration: apply all the training inputs once to the network and compute the mean square error).

    Plot the percentage error in testing your handwritten digit recognition system as a bar chart.

    (Mean error occurred while testing each digit with the test data).

    Task #1: Repeat this experiment for different learning rate parameters (at least 3 experiments. Start

    with a large value and gradually decrease to a small value).

    Task #2: Repeat Task #1 with a large database. The first 10000 images for training (image indexes

    from 0-9999) and test with another 1000 images (image indexes from 20001-21000).

    Task #3: Repeat Task #2 with multilevel data (without thresholding the input data, normalize the

    input data, use sigmoid function for output thresholding). What can you note comparing with part 2?

    Task #4: Compare your results with the SVM results (what you have got from the last project).

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