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Bacteria identification can be a time-consuming process. Machine learning algorithms that use deep convolutional neural networks (CNNs) provide a promising alternative. Here, we present a deep learning based approach paired with Raman spectroscopy to rapidly and accurately detect the identity of a bacteria class. We propose a simple 4-layer CNN architecture and use a 30-class bacteria isolate dataset for training and testing. We achieve an identification accuracy of around 86% with identification speeds close to real-time. This optical/biological detection method is promising for applications in the detection of microbes in liquid biopsies and concentrated environmental liquid samples, where fast and accurate detection is crucial. This study uses a recently published dataset of Raman spectra from bacteria samples and an improved CNN model built with TensorFlow. Results show improved identification accuracy and reduced network complexity.