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In blue we show the recurrent connections – the output'm' at time (t – 1) is fed back to the memory at time't' via the three gates; the cell value is fed back via the forget gate; the predicted word at time (t – 1) is fed back in addition to the memory output'm' at time't' into the Softmax for tag prediction. In spite of this fact, when we test images with multiple clothing type, our trained model generates tags for these unseen test images quite accurately ( 80% accurate). Prediction accuracy of our model improves quickly with increasing number of training iterations and stabilizes after about 20,000 iterations. Moreover, combining DCNN-RNN model helps us extend the trained model to solve completely different problem like fashion image tag generation.