Research Article
Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images
Table 2
Hyperparameters and their search space.
| Parameter | Search space | Description |
| Optimizer | Adam, RMSProp, SGD, AdaDelta | To optimize the input weights by comparing the prediction and the loss function | Learning rate | 1e − 3, 1e − 4, 1e − 5, 1e − 6 | To determine the step size at each iteration while minimizing the loss function | Activation function | ReLu, Elu and Tanh, Leaky ReLu | To introduce nonlinearity into the output of neurons | Number of neurons in customized layers | 64,128, 56, 512,1024 | To compute the weighted average of the input | Batch size | 32,64,128 | Number of training examples utilized in one iteration |
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