49275 - Design Convolutional Neural Network (CNN) And Three Hidden Layer Neurons - IT/Computer Science Assignment Help

Assignment Task -

QUESTION 1

i) Calculate the output vector z which is generated from the above input. How  would the neural network classify this feature input? 

ii) Suggest one solution if we want to improve the testing classification performance.  Please provide a reason and testing result.

QUESTION 2

Design a Convolutional Neural Network (CNN) to classify dog and cat images where each  class contains 300 images. Firstly, scale all the images to the uniform size. Then, use the  designed CNN to classify them, the ratio of training and testing is 7:3.

2.1 In this CNN, there are total 4 convolution layers with 24, 28, 32, and 36 filters, respectively.  The filter size of the convolution layers is 5, stride is set at 1, padding is set at 2. Batch  normalization is used and the active function is ReLU. After each convolutional layer, a  max pooling layer is followed with filter size of 2 and the stride is set at 2. Adam (adaptive  learning rate method) is used for training and the initial learning rate is set at 0.001. 

Train the network for 50 epochs and show that the network is able to achieve >70%  accuracy on the test dataset.

2.2 Optimise the CNN hyperparameters and show that the final testing accuracy is improved  to 75% or above. Briefly analysis should be given.

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