Keras之自定义损失(loss)函数用法说明

Keras之自定义损失之自定义损失(loss)函数用法说明函数用法说明

主要介绍了Keras之自定义损失(loss)函数用法说明，具有很好的参考价值，希望对大家有所帮助。一起跟随小

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在Keras中可以自定义损失函数，在自定义损失函数的过程中需要注意的一点是，损失函数的参数形式，这一点在Keras中是

固定的，须如下形式：

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

具体参考Keras官方metrics的定义keras/metrics.py：

"""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 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):

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())

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