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How do I optimize CNN Hyperparameters?
We propose the usage of a genetic algorithm to optimize the hyperparameters specifically for convolutional neural networks (CNNs). Additionally, we suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space.
What are Hyperparameters of CNN?
Hyperparameters are the variables which determines the network structure(Eg: Number of Hidden Units) and the variables which determine how the network is trained(Eg: Learning Rate). Hyperparameters are set before training(before optimizing the weights and bias).
What is learning rate in CNN?
The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. The learning rate may be the most important hyperparameter when configuring your neural network.
How to improve the performance of CNN network?
There are some suggestions which I think can improve CNN performance: Input size is (6, 3) so it is not preferable to use MaxPooling layers. Use padding = ‘same’ in all the convolution layers which output convolved image of the same dimension as input. Use Relu or LeakyRelu as activation function. Use Adam optimizer and tune the learning rate.
How can image augmentation improve the performance of CNN?
Image samples generated using image augmentation, in general existing data samples increased by the rate of nearly 3x to 4x times. One more advantage of data augmentation is as we know CNN is not rotation invariant, using augmentation we can add the images in the dataset by considering rotation.
Is there a way to change the layers of CNN?
A suggestion I give is to change the layers of CNN. You have 3 CNN layers, all sequentially added acting on the same shape and outputting the same output shape with same filter size. What you can try is to change the filter size and using multiple filter sizes to catch different features of different sizes.
How to optimize for data science Stack Exchange?
Use padding = ‘same’ in all the convolution layers which output convolved image of the same dimension as input. Use Relu or LeakyRelu as activation function. Use Adam optimizer and tune the learning rate. Thanks for contributing an answer to Data Science Stack Exchange!