What is the operation principle of Keras?

Keras is an advanced neural network API that is built on top of deep learning frameworks such as TensorFlow and Theano. Its operation can be broken down into several key steps as follows:

1. Define model structure: With the Keras API, users can specify the architecture of the neural network model, including the types of layers and their parameters.
2. Compile model: After defining the model structure, it is necessary to compile the model by calling the compile method. During the compilation process, users can specify the loss function, optimizer, evaluation metrics, etc.
3. Training model: By calling the fit method, users can input data into the model for training. During the training process, the model will update parameters based on the loss function and optimizer.
4. Model evaluation: After the model training is completed, users can assess the model by calling the evaluate method to obtain the model’s performance metrics.
5. The model can be used to predict new data once it has been trained. By calling the predict method, users can use the trained model to make predictions on new data.

In general, Keras offers a simple and user-friendly API that allows users to easily build, train, and evaluate neural network models. The underlying deep learning framework handles the actual calculations and optimization process.