Compiling Keras Models

Through the lens of Keras, model compilation is a step that requires method calling on your model. This process transforms your simple Python descriptions into highly optimized tensor operations, which can be executed on either GPUs or CPUs. During this time, several essential aspects such as the optimizer, the loss function and the metrics are set.

The model's configurations are specified using the compile() function in Keras, which accepts three crucial arguments:

1. An optimizer: The specific object updating the model parameters to minimize the loss function.
2. A loss function: A procedure for training the model.
3. A list of metrics: Used to monitor training and testing.

Now, let's roll the cinematography forward and begin with how to compile a Keras model.

from keras.models import Sequential
from keras.layers import Dense

# Define a model named 'avengers_model'
avengers_model = Sequential()
avengers_model.add(Dense(32, activation='relu', input_dim=100))
avengers_model.add(Dense(1, activation='sigmoid'))

In this example, we create a simple avengers_model that includes two densely connected layers.

The next step is choosing a suitable optimizer and loss function for the model.

# compile the model
avengers_model.compile(optimizer='rmsprop', 
           loss='binary_crossentropy',
           metrics=['accuracy'])

In the code above, we use RMSprop as optimizer, binary cross-entropy as loss function, and accuracy as metric for the model's performance.

Optimizer: Keras ships out with various optimizers including SGD(Stochastic Gradient Descent), RMSprop, Adam, Adamax, Adagrad, etc. Depending on the problem you are solving, you may choose a suitable optimizer. For instance, 'sgd' is a good choice when dealing with a shallow neural network, while 'adam', 'RMSprop' and 'adamax' are better options for deeper neural networks.

Loss: The loss function, or objective function is a method of evaluating how well the algorithm models your data. If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower number. As you change pieces of your algorithm to try and improve your model, your loss function will tell you if you’re getting anywhere. In the example above we used binary crossentropy, however Keras provides various loss functions like mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, mean_squared_logarithmic_error, squared_hinge, hinge, categorical_hinge, logcosh, categorical_crossentropy, sparse_categorical_crossentropy and so on.

Metrics_: Metrics are similar to loss functions, except that the results from evaluating a metric are not used when training the model. You may use any available loss function as a metric.

Now that we have seen how to compile the model, let us understand why compilation of the model is necessary.

A need for model compilation:

To make sure that the back propagation and optimization process of the model can function, we need to compile the model. We set key properties such as the optimizer and the cost function it should use, along with the metrics that we want to watch. The function of the optimizer is to adjust weights in your model to reduce training loss. The cost or loss function being used by the optimizer is a function used to compute the loss of the model so that the weights can be adjusted to minimize this loss.

After building the code, layers, and initializing the model, you can compile the model. Using compile() we can configure the learning process. It receives three key arguments:

In the avengers_model.compile() for example, the parameters include:

• Optimizer: The optimizer is defined to be 'rmsprop', a very sensible default.

• loss function (objective function) - The model will try to minimize the specified loss function.

• Metrics: Since this is a problem of binary classification, we are mostly concerned with the accuracy of the model, i.e., the proportion of all our predictions being correct.

By calling model.compile(), you're configuring the learning process for the model. Though model.compile() is some sort of a hybrid step between defining a model and training it, you'll see that Keras is very forgiving with it.

From inception, this process might shrink to the shadows and seem intimidating to wrap your head around. However, the reality is quite different. The process is straightforward when executing or writing the code. It requires you to define the model and compile it with the correct optimizer, loss function, and metrics. Then you are good to go. And be ready to get some popcorn and enjoy the masterpiece of your AI model.