Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic algorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a appropriate preprocess step for each leakage interval. This degenerates the quality of profiling traces due to the noise and non-primary components. In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately. Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model. We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.