One of the most important hyper-parameters for
model training and generalization is the learning rate. Recently,
many research studies have shown that optimizing the learning
rate schedule is very useful for training deep neural networks to
get accurate and efficient results. In this paper, different learning
rate schedules using some comprehensive optimization techniques
have been compared in order to measure the accuracy of a
convolutional neural network CNN model to classify four
ophthalmic conditions. In this work, a deep learning CNN based
on Keras and TensorFlow has been deployed using Python on a
database that contains 1692 images, which consists of four types
of ophthalmic cases: Glaucoma, Myopia, Diabetic retinopathy,
and Normal eyes. The CNN model has been trained on Google
Colab. GPU with different learning rate schedules and adaptive
learning algorithms. Constant learning rate, time-based decay,
step-based decay, exponential decay, and adaptive learning rate
optimization techniques for deep learning have been addressed.
Adam adaptive learning rate method. has outperformed the other
optimization techniques and achieved the best model accuracy of
92.58% for training set and 80.49% for validation datasets,
respectively.
Stability Analysis of Neural Networks-Based System Identification
