Я хочу использовать функцию потери DSSIM, и я поместил код этой функции потери в свой код, но она выдает эту ошибку
Traceback (последний вызов был последним):
Файл "", строка 218, в файле w_extraction.compile (оптимизатор = opt, loss = {'decoder_output': 'DSSIMObjective', 'wprim': 'binary_crossentropy'}, loss_weights = {'decoder_output': 1.0, 'wprim': 1.0}, metrics = ['mae'])
Файл "D: \ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ engine \ training.py", строка 129, в компиляции loss_functions.append (loss.get (loss.get (name)))
Файл "D: \ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ loss.py", строка 133,в get return десериализовать (идентификатор)
Файл "D: \ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ loss.py", строка 114, в десериализации: printable_module_name = 'loss function')
Файл "D: \ software \ Anaconda3 \ envs \ py36 \ lib \ site-packages \ keras \ utils \ generic_utils.py", строка 165, в deserialize_keras_object ':' + имя-функции)
ValueError: Неизвестная функция потерь: DSSIMObjective
и я не знаю, где я должен поместить определение этой функции потерь?Я поместил этот код поверх структуры моей сети.
import keras_contrib.backend as KC
class DSSIMObjective:
"""Difference of Structural Similarity (DSSIM loss function).
Clipped between 0 and 0.5
Note : You should add a regularization term like a l2 loss in addition to this one.
Note : In theano, the `kernel_size` must be a factor of the output size. So 3 could
not be the `kernel_size` for an output of 32.
# Arguments
k1: Parameter of the SSIM (default 0.01)
k2: Parameter of the SSIM (default 0.03)
kernel_size: Size of the sliding window (default 3)
max_value: Max value of the output (default 1.0)
"""
def __init__(self, k1=0.01, k2=0.03, kernel_size=3, max_value=1.0):
self.__name__ = 'DSSIMObjective'
self.kernel_size = kernel_size
self.k1 = k1
self.k2 = k2
self.max_value = max_value
self.c1 = (self.k1 * self.max_value) ** 2
self.c2 = (self.k2 * self.max_value) ** 2
self.dim_ordering = K.image_data_format()
self.backend = K.backend()
def __int_shape(self, x):
return K.int_shape(x) if self.backend == 'tensorflow' else K.shape(x)
def __call__(self, y_true, y_pred):
# There are additional parameters for this function
# Note: some of the 'modes' for edge behavior do not yet have a
# gradient definition in the Theano tree
# and cannot be used for learning
kernel = [self.kernel_size, self.kernel_size]
y_true = K.reshape(y_true, [-1] + list(self.__int_shape(y_pred)[1:]))
y_pred = K.reshape(y_pred, [-1] + list(self.__int_shape(y_pred)[1:]))
patches_pred = KC.extract_image_patches(y_pred, kernel, kernel, 'valid',
self.dim_ordering)
patches_true = KC.extract_image_patches(y_true, kernel, kernel, 'valid',
self.dim_ordering)
# Reshape to get the var in the cells
bs, w, h, c1, c2, c3 = self.__int_shape(patches_pred)
patches_pred = K.reshape(patches_pred, [-1, w, h, c1 * c2 * c3])
patches_true = K.reshape(patches_true, [-1, w, h, c1 * c2 * c3])
# Get mean
u_true = K.mean(patches_true, axis=-1)
u_pred = K.mean(patches_pred, axis=-1)
# Get variance
var_true = K.var(patches_true, axis=-1)
var_pred = K.var(patches_pred, axis=-1)
# Get std dev
covar_true_pred = K.mean(patches_true * patches_pred, axis=-1) - u_true * u_pred
ssim = (2 * u_true * u_pred + self.c1) * (2 * covar_true_pred + self.c2)
denom = ((K.square(u_true)
+ K.square(u_pred)
+ self.c1) * (var_pred + var_true + self.c2))
ssim /= denom # no need for clipping, c1 and c2 make the denom non-zero
return K.mean((1.0 - ssim) / 2.0)