不能在Keras / Tensorflow中使用两种不同的add_loss方法吗？

Question

我编写了以下代码，以通过Autoencoder进行简单的实验，我只想使用两个损失，第一个损失是输入和输出的传统MSE损失，它是根据AE的潜矢量重建的，第二个损失是编码器和解码器中对称层的两个输出之间的MSE损失，也就是说，如果AE有5层，我想在第二层和第四层之间增加一个MSE损失，它们是对称的。代码在这里：

from time import time
import numpy as np
import random
from keras.models import Model
import keras.backend as K
from keras.engine.topology import Layer, InputSpec
from keras.layers import Dense, Input, GaussianNoise, Layer, Activation
from keras.models import Model
from keras.optimizers import SGD, Adam
from keras.utils.vis_utils import plot_model
from keras.callbacks import EarlyStopping

#build vae model

input_place = Input(shape=(128,))

e_layer1 = Dense(64,activation='relu')(input_place)
e_layer2 = Dense(32,activation='relu')(e_layer1)
hidden = Dense(16,activation='relu')(e_layer2)

d_layer1 = Dense(32,activation='relu')(hidden)
d_layer2 = Dense(64,activation='relu')(d_layer1)

output_place = Dense(128,activation='sigmoid')(d_layer2)

model = Model(inputs=input_place,outputs=output_place)

loss = K.mean(K.square(d_layer1 - e_layer2),axis = -1)

model.add_loss(loss)

model.compile(optimizer = 'adam',
              loss=['mse'],
              metrics=['accuracy'])

input_data = np.random.randn(400,128)

model.fit(input_data,
          input_data,
          batch_size = 32,
          epochs=5)

但是当我运行此代码时，它会发生关于]的错误>

Epoch 1/5
 32/400 [=>............................] - ETA: 12s - loss: 1.6429 - acc: 0.0000e+00Traceback (most recent call last):

  File "<ipython-input-49-eac3a65824ec>", line 1, in <module>
    runfile('/Users/jishilun/Desktop/keras_loss_test.py', wdir='/Users/jishilun/Desktop')

  File "/anaconda3/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py", line 704, in runfile
    execfile(filename, namespace)

  File "/anaconda3/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py", line 108, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/Users/jishilun/Desktop/keras_loss_test.py", line 49, in <module>
    epochs=5)

  File "/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 1039, in fit
    validation_steps=validation_steps)

  File "/anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py", line 204, in fit_loop
    callbacks.on_batch_end(batch_index, batch_logs)

  File "/anaconda3/lib/python3.7/site-packages/keras/callbacks.py", line 115, in on_batch_end
    callback.on_batch_end(batch, logs)

  File "/anaconda3/lib/python3.7/site-packages/keras/callbacks.py", line 236, in on_batch_end
    self.totals[k] += v * batch_size

ValueError: operands could not be broadcast together with shapes (32,) (16,) (32,) 
并且如果我删除add_loss，代码可以运行，因此我认为Keras / Tensorflow中的add_loss的两种方法不能简单地一起使用，或者可能会有一些变化（也许问题出在小批量生产中？ ）请帮我！任何意见将受到欢迎！非常感谢！

我编写了以下代码，以通过自动编码器进行简单的实验，我只想使用两个损失，第一个损失是输入和输出的传统MSE损失，即...

Answer 1

问题不是来自add_loss()，而是来自您的batch_size。您的输入数据为(400,128)，但batch_size为32。请尝试将其更改为400的分解因数，例如40或20，它将可以使用。