是否可以将tensorflow代码转换为theano代码?
问题描述:
我有一个使用tensorflow函数的函数。我需要Theano的这个功能,因为在平台上我想使用这个代码只有Theano安装而不是tensorflow。我主要和Keras一起工作,所以tensorflow对我来说很神秘。 功能如下:是否可以将tensorflow代码转换为theano代码?
class WeightedBinaryCrossEntropy(object):
def __init__(self, pos_ratio):
neg_ratio = 1. - pos_ratio
self.pos_ratio = tf.constant(pos_ratio, tf.float32)
self.weights = tf.constant(neg_ratio/pos_ratio, tf.float32)
self.__name__ = "weighted_binary_crossentropy({0})".format(pos_ratio)
def __call__(self, y_true, y_pred):
return self.weighted_binary_crossentropy(y_true, y_pred)
def weighted_binary_crossentropy(self, y_true, y_pred):
# Transform to logits
epsilon = tf.convert_to_tensor(K.common._EPSILON, y_pred.dtype.base_dtype)
y_pred = tf.clip_by_value(y_pred, epsilon, 1 - epsilon)
y_pred = tf.log(y_pred/(1 - y_pred))
cost = tf.nn.weighted_cross_entropy_with_logits(y_true, y_pred, self.weights)
return K.mean(cost * self.pos_ratio, axis=-1)
model.compile(loss=WeightedBinaryCrossEntropy(0.05), optimizer=optimizer, metrics=['accuracy'])
的平台上安装Tensorflow是不可能的。 我从这里得到的代码https://github.com/fchollet/keras/issues/2115
那么Theano中的函数是否像Tensorflow中的函数一样工作?
答
也许你应该只使用keras,有一个便携机型:
(Keras功能:https://keras.io/backend/)
class WeightedBinaryCrossEntropy(object):
def __init__(self, pos_ratio):
neg_ratio = 1. - pos_ratio
self.pos_ratio = K.constant([pos_ratio])
self.weights = K.constant([neg_ratio/pos_ratio])
self.__name__ = "weighted_binary_crossentropy({0})".format(pos_ratio)
def __call__(self, y_true, y_pred):
return self.weighted_binary_crossentropy(y_true, y_pred)
def weighted_binary_crossentropy(self, y_true, y_pred):
# Transform to logits
epsilon = K.epsilon()
y_pred = K.clip(y_pred, epsilon, 1 - epsilon)
y_pred = K.log(y_pred/(1 - y_pred))
#for the crossentropy, you can maybe (make sure, please)
#use K.binary_crossentropy and multiply the weights later
cost = self.approach1(y_true,y_pred)
#or you could simulate the same formula as in tensorflow:
#https://www.tensorflow.org/api_docs/python/tf/nn/weighted_cross_entropy_with_logits
cost = self.approach2(y_true,y_pred)
return K.mean(cost * self.pos_ratio, axis=-1)
#I use a similar thing in my codes, but I'm not sure my weights are calculated the same way you do
def approach1(self,y_true,y_pred):
weights = (y_true * self.weights) + 1 #weights applied only to positive values
return K.binary_crossentropy(y_true, y_pred,from_logits=True)*weights
#seems more trustable, since it's exactly the tensorflow formula
def approach2(self,y_true,y_pred):
posPart = y_true * (-K.log(K.sigmoid(y_pred))) * self.weights
negPart = (1-y_true)*(-K.log(1 - K.sigmoid(y_pred)))
return posPart + negPart
model.compile(loss=WeightedBinaryCrossEntropy(0.05), optimizer=optimizer, metrics=['accuracy'])
谢谢你,这将需要一些时间来测试:测试后,我可能会接受它。 –