正确的损失函数方式 [英] Correct way of loss function
问题描述
我一直在尝试在keras中实现损失功能.但是我无法找到一种方法来传递除loss(y_true,y_predict)以外的2个以上参数,因此我想到了使用lambda层作为最后一层,并在lambda层itslef中进行计算,并简单地返回y_predict的值像这样的损失功能
Hi I have been trying to implement a loss function in keras. But i was not able to figure a way to pass more than 2 arguments other than loss(y_true, y_predict) so I thought of using a lambda layer as the last layer and doing my computation in lambda layer itslef and simply returning the value of y_predict in loss function like this
def loss_function(x):
loss = some calculations
return loss
def dummy_loss(y_true, y_pred):
return y_pred
def primary_network():
global prim_binary_tensor
x = VGG16(weights='imagenet', include_top=True, input_shape=image_shape)
last_layer = Dense(k_bit, activation='tanh', name='Dense11')(x.layers[-1].output)
last_layer, x = basic_model()
lambda_layer = Lambda(loss_function)([last_layer, prim_binary_tensor])
model = Model(inputs=[x.input, prim_binary_tensor], outputs=[lambda_layer])
model.compile(optimizer="adam", loss=dummy_loss,metrics=['accuracy'])
return model
所以我的问题是:
1)我是用正确的方法来计算损失吗?是否保证为每个图像(input_data)调用lambda图层函数?
1) Am I doing it the right way to calculate the loss? Is it guranteed that the lambda layer function is called for each and every image(input_data)?
2)有人可以建议我如何将多个参数传递给损失函数吗?
2) Can someone suggest me how to pass multiple arguments to a loss function?
3)损失函数的最终结果可以是标量还是必须是向量或矩阵?
3) Can the final outcome of a loss function be a scalar or it has to be a vector or matrix?
推荐答案
回答您的问题:
-
我不知道您的方法是否可行,但是有一个更简单的解决方案.
I don't know whether your approach works, but there is an easier solution.
您可以通过定义部分函数来传递多个参数.
You can pass multiple arguments by defining a partial function.
损失函数的输出是标量.
The output of a loss function is a scalar.
这里是一个示例,演示了如何将多个参数传递给损失函数:
Here is an example that demonstrates how to pass multiple arguments to a loss function:
from keras.layers import Input, Dense
from keras.models import Model
import keras.backend as K
def custom_loss(arg1, arg2):
def loss(y_true, y_pred):
# Use arg1 and arg2 here as you wish and return loss
# For example:
return K.mean(y_true - y_pred) + arg1 + arg2
return loss
x = Input(shape=(1,))
arg1 = Input(shape=(1,))
arg2 = Input(shape=(1,))
out = Dense(1)(x)
model = Model([x, arg1, arg2], out)
model.compile('sgd', custom_loss(arg1, arg2))
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