An image classification method and apparatus, an electronic device and a computer readable storage medium. The image classification method comprises: constructing a TELU activation function according to a function corresponding to an ELU negative half axis and a TLU function; performing convolution calculation on input image data to obtain feature data; obtaining target data subjected to mean square normalization processing corresponding to the c-th channel and the b-th batch in the current layer of a neural network according to the feature data; obtaining a feature map corresponding to the target data by means of the TELU activation function; and obtaining the image classification result according to all the feature maps. The present application can solve the problem of deviation of activation far away from a value 0 caused by lack of mean value centering of the FRN, and the problem of neuronal death caused by vanishing gradient of an input part, has the characteristic of soft saturation when a small value is input, reduces the forward propagation changes and information, improves the overall performance of the neural network, and thus, the image classification result has higher accuracy.
IMAGE CLASSIFICATION AND APPARATUS, AND RELATED COMPONENTS