在Keras中可以自定義損失函式，在自定義損失函式的過程中需要注意的一點是，損失函式的引數形式，這一點在Keras中是固定的，須如下形式：

def  my_loss(y_true, y_pred):
# y_true: True labels. TensorFlow/Theano tensor
# y_pred: Predictions. TensorFlow/Theano tensor of the same shape as y_true
    .
    .
    .
    return scalar  #返回一個標量值

然後在model.compile中指定即可，如：

model.compile(loss=my_loss, optimizer='sgd')
 

"""Built-in metrics.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six
from . import backend as K
from .losses import mean_squared_error
from .losses import mean_absolute_error
from .losses import mean_absolute_percentage_error
from .losses import mean_squared_logarithmic_error
from .losses import hinge
from .losses import logcosh
from .losses import squared_hinge
from .losses import categorical_crossentropy
from .losses import sparse_categorical_crossentropy
from .losses import binary_crossentropy
from .losses import kullback_leibler_divergence
from .losses import poisson
from .losses import cosine_proximity
from .utils.generic_utils import deserialize_keras_object
from .utils.generic_utils import serialize_keras_object


def binary_accuracy(y_true, y_pred):
    return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)


def categorical_accuracy(y_true, y_pred):
    return K.cast(K.equal(K.argmax(y_true, axis=-1),
                          K.argmax(y_pred, axis=-1)),
                  K.floatx())


def sparse_categorical_accuracy(y_true, y_pred):
    # reshape in case it's in shape (num_samples, 1) instead of (num_samples,)
    if K.ndim(y_true) == K.ndim(y_pred):
        y_true = K.squeeze(y_true, -1)
    # convert dense predictions to labels
    y_pred_labels = K.argmax(y_pred, axis=-1)
    y_pred_labels = K.cast(y_pred_labels, K.floatx())
    return K.cast(K.equal(y_true, y_pred_labels), K.floatx())


def top_k_categorical_accuracy(y_true, y_pred, k=5):
    return K.mean(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), axis=-1)


def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
    # If the shape of y_true is (num_samples, 1), flatten to (num_samples,)
    return K.mean(K.in_top_k(y_pred, K.cast(K.flatten(y_true), 'int32'), k),
                  axis=-1)


# Aliases

mse = MSE = mean_squared_error
mae = MAE = mean_absolute_error
mape = MAPE = mean_absolute_percentage_error
msle = MSLE = mean_squared_logarithmic_error
cosine = cosine_proximity


def serialize(metric):
    return serialize_keras_object(metric)


def deserialize(config, custom_objects=None):
    return deserialize_keras_object(config,
                                    module_objects=globals(),
                                    custom_objects=custom_objects,
                                    printable_module_name='metric function')


def get(identifier):
    if isinstance(identifier, dict):
        config = {'class_name': str(identifier), 'config': {}}
        return deserialize(config)
    elif isinstance(identifier, six.string_types):
        return deserialize(str(identifier))
    elif callable(identifier):
        return identifier
    else:
        raise ValueError('Could not interpret '
                         'metric function identifier:', identifier)