  We present a classification of the light curve morphologies of eclipsing
binary systems observed by ASAS-SN based on their light curve images. The data
of 16500 eclipsing systems having three different classes (detached Algol type,
$\beta$ Lyr type, and W UMa type) are collected to construct their light
curves. A deep learning algorithm containing the convolutional neural networks
is employed on the images to achieve a satisfying classification. A code called
ASEBCLASS is written in Python language, and it uses the TensorFlow platform
through Keras API to employ the mathematical libraries in the training phase of
the model. The architecture consists of four groups of convolutional,
activation, maximum pooling layers together with the additional fully connected
layers. The results show that our algorithm estimates the morphological class
of an external input image data with an accuracy value of 92%.
