Compiling a Keras model involves configuring essential settings for training, such as the loss function, optimizer, and evaluation metrics. Here’s how to compile a Keras model:

# Import necessary libraries
import keras
from keras.models import Sequential
from keras.layers import Dense

# Create a Keras model (for demonstration)
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

# Compile the model
model.compile(
    optimizer='adam',  # Specify the optimizer (e.g., Adam, SGD)
    loss='categorical_crossentropy',  # Specify the loss function
    metrics=['accuracy']  # Specify evaluation metrics (can be a list)
)

In this code:

1. Import the necessary Keras libraries.
2. Create a Keras model. Replace this with your specific model architecture.
3. Use the compile method to set the configuration for training:
   • optimizer: Specify the optimization algorithm, e.g., ‘adam’, ‘sgd’, ‘rmsprop’, or others.
   • loss: Define the loss function that your model will optimize, depending on your task (e.g., ‘categorical_crossentropy’ for classification, ‘mean_squared_error’ for regression).
   • metrics: Specify the evaluation metrics you want to track during training. You can use a list of metrics, such as ['accuracy'] for classification tasks.

After compiling the model, it’s ready for training using your dataset. The compiled configuration ensures that the model updates its weights and biases based on the specified loss and optimization algorithm while monitoring the specified metrics for evaluation.