Compilation of the module

Compilation configure the learning process of the module.

It is done via compile method.

It takes 3 arguments.

    Optimizer

    Can use string identifier of an existing optimizer (rmsprop | adagrad) or instance of an existing Optimizer.

    Loss Function

Model always tries to minimize the loss.
Can use existing string identifier of an loss function ( mse | categorical_crossentropy)or an objective function.

    A list of metrics

For any classification problem set metrics to accuracy.

metrics=['accuracy']

metric could be a string identifier , existing metric or custom metric function.

# For a binary classification problem
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# For a mean squared error regression problem
model.compile(optimizer='rmsprop',
              loss='mse')

# For custom metrics
import keras.backend as K

def mean_pred(y_true, y_pred):
    return K.mean(y_pred)

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