Deep Learning Image Classification Code with Deep-net Model

Deep-net is a deep learning architecture composed of Self-Encoder Algorithm (SEA) for feature extraction and a softmax classifier for categorization. SEA automatically captures the most significant features from input data, and when combined with softmax, enables accurate image classification. This model demonstrates superior performance compared to other approaches in image classification tasks, with implementation typically involving stacked autoencoders for unsupervised pre-training followed by supervised fine-tuning of the classification layer.