In Keras, a neural network is composed of different types of layers, each serving a specific purpose. The main layers in a typical neural network include:
1. **Input Layer**: This layer specifies the input shape of the data that will be fed to the network. It’s the first layer in the network and is responsible for accepting the input data.
2. **Hidden Layers**: These are the layers between the input and output layer where most of the computations occur. They consist of multiple layers of neurons, each processing input data in a particular way by applying a non-linear activation function.
3. **Output Layer**: This layer produces the final output of the neural network. The number of neurons in the output layer depends on the type of problem you are trying to solve. For instance, in a binary classification problem, there would be a single output neuron, while in a multi-class classification problem with 10 classes, there would be 10 output neurons.
4. **Activation functions**: Activation functions are responsible for introducing non-linearity to the neural network. Non-linear activation functions allow the neural network to learn complex patterns in the data. Common activation functions used in Keras include ReLU, Sigmoid, and Tanh.
5. **Loss function**: The loss function is a measure of how well the neural network is performing. It calculates the difference between the actual output and the predicted output of the neural network. The goal of training the neural network is to minimize the loss function.
6. **Optimizer**: The optimizer is responsible for updating the weights of the neural network during the training process, to minimize the loss function. Common optimizers used in Keras include Stochastic Gradient Descent (SGD), Adam, and RMSprop.
Overall, the composition of a neural network in Keras depends on the problem you are trying to solve, the structure of the data, and the complexity of the model.