While deep models are effectively trained based on a softmax cross-entropy loss, a cosine-based softmax loss also works for producing favorable feature embedding.In the cosine-based softmax, temperature plays a crucial role in properly scaling the logits of cosine similarities, though being manually tuned in ad-hoc ways as there is less prior knowledge about the temperature.In this paper, we address the challenging problem to adaptively estimate the temperature of cosine-based softmax in the framework of supervised image classification.By analyzing the cosine-based softmax representation from a geometrical viewpoint regarding features and classifiers, we construct a criterion in a least-square fashion which enables us to optimize the temperature at each sample via simple greedy search.Besides, our thorough analysis about temperature clarifies that feature embedding by the cosine-based softmax loss is endowed with diverse characteristics which are controllable by the temperature in an explainable way.The experimental results demonstrate that our optimized temperature contributes to determine a feasible range of temperature to control the feature characteristics and produces favorable performance on various image classification tasks.