Keras之自定义损失(loss)函数用法说明

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 #返回一个标量值

"""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)