ELEG 6183 Deep Learning Python Assignment


ELEG 6183 Deep Learning Python Assignment

Simple convolution neural network:

Project Title: COVID-19 Recognition on Medical Images via Convolutional Neural Networks (CNN).

Goal: Compare performance differences generated with three CNNs that have different structures.

Detailed steps: 

  • Building three CNNs with the following three different CNN structures on the training set (x_train.npy, y_train.npy)
    • CNN structure 1: CONV1(16, (3*3))-Relu1-Maxpooling1(2*2)-Dropout1(0.25)-CONV2(32, (3*3))-Relu2-Maxpooling2(2*2)-Dropout2(0.25)-Dense(512)-Relu3- Dropout3(0.5)-Output(Softmax)
    • CNN structure 2:

CONV1(16, (3*3))-CONV2(16, (3*3))-Relu1-Maxpooling1(2*2)-Dropout1(0.25)-CONV3(32, (3*3))-Relu2-Maxpooling2(2*2)-Dropout2(0.25)-Dense(512) -Relu3- Dropout3(0.5)-Output(Softmax)

    • CNN structure 3:

CONV1(16, (3*3))-Relu1-Maxpooling1(2*2)-Dropout2(0.25)-CONV2(16, (3*3))-CONV3(32, (3*3))-Relu2-Maxpooling2(2*2)-Dropout2(0.25)-Dense1(512) -Relu3- Dropout3(0.5)-Output(Softmax)

  • Testing these three implemented CNNs on the testing dataset (x_test.npy, y_test.npy), respectively, with the same hyper-parameters: learning rate: 0.0001, epoch: 10.
  • Calculate and compare the training accuracies
  • Calculate and compare the testing accuracies
  • Plot the learning curves for these three CNNs with their losses, respectively, and compare and analyze their differences.


  • Load COVID-19 Data: https://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html


    • Submit the source code (.py files) and the data you use for the project, where the codes have no bugs.
    • Write and submit the project report with the following parts:
      • Project description
      • Model description
      • Result analysis
        • Make a table to show the comparison of Precision, Recall, Fscore, Accuracy, and total training time (seconds)
        • Analyze the observations on the comparison, for example, why these observations are obtained.
        • List 3 prediction results
    • Presentation
      • 10 ~ 15 minutes’ presentation with slides (around 15 pages)
      • Slide Outline
        • Project description
        • Model description
        • Result analysis


Please submit all required materials (source codes, data, report, and slides) within one package (.zip file) before the due.

ELEG 6183 Deep Learning Python Assignment

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