![]() ![]() ![]() Machine learning classification is the process of assigning discrete labels to groups in data based on the characteristics of that group. In all of these situations, the deep learning models fall under a category of machine learning models called classification. Finally, streaming services like Netflix use these models to analyze subscriber data to suggest new, relevant content to viewers. Further, Tesla’s self-driving cars employ deep learning models for image recognition. For example, deep learning can solve problems in healthcare like predicting patient readmission. These models have a wide range of applications in healthcare, robotics, streaming services and much more. Don’t forget to download the source code for this tutorial on my GitHub.Deep learning models are a mathematical representation of the network of neurons in the human brain. To learn the actual implementation of _categorical_crossentropy and sparse_categorical_accuracy, you can find it on TensorFlow repository. So, the output of the model will be in softmax one-hot like shape while the labels are integers. This tutorial explores two examples using sparse_categorical_crossentropy to keep integer as chars' / multi-class classification labels without transforming to one-hot labels. Need to call reset_states() before prediction to reset LSTMs' initial states.įor more implementation detail of the model, please refer to my GitHub repository.The prediction model loads the trained model weights and predicts five chars at a time, it is, By making it stateful, the LSTMs’ last state for each sample in a batch will be used as the initial state for the sample in the following batch, or put it simply, those five characters predicted at a time and following predicted batches are characters in one sequence. Once the model is trained, we can make it “stateful” and predict five characters at a time. ![]()
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