我正在使用有关自动编码器的本教程：https ://blog.keras.io/building-autoencoders-in-keras.html
所有代码都在工作，但是当我设置正则化参数时，性能非常差（结果模糊）10e-5，这是教程代码中定义的参数。事实上，我需要减少正则化以10e-8获得正确的输出。
我的问题如下：为什么结果与教程如此不同？数据一样，参数一样，没想到差别很大。
我怀疑 Keras 函数的默认行为已从 2016 年 5 月 14 日开始更改（在所有情况下都执行自动批量标准化？）。
输出
- 带
10e-5正则化（模糊）；val_loss在0.296750 个 epoch 和0.2774100 个 epoch 之后。 - 正则化
10e-8：在50 个 epoch 和100 个 epoch 之后。val_loss0.10800.1009 - 没有正则化：
val_loss在0.101850 个 epoch 和0.0944100 个 epoch 之后。
完整代码（供参考）
# Source: https://blog.keras.io/building-autoencoders-in-keras.html
import numpy as np
np.random.seed(2713)
from keras.layers import Input, Dense
from keras.models import Model
from keras import regularizers
encoding_dim = 32
input_img = Input(shape=(784,))
# add a Dense layer with a L1 activity regularizer
encoded = Dense(encoding_dim, activation='relu',
activity_regularizer=regularizers.l1(10e-5))(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)
# create a placeholder for an encoded (32-dimensional) input
encoded_input = Input(shape=(encoding_dim,))
# retrieve the last layer of the autoencoder model
decoder_layer = autoencoder.layers[-1]
# create the decoder model
decoder = Model(encoded_input, decoder_layer(encoded_input))
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
from keras.datasets import mnist
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print(x_train.shape)
print(x_test.shape)
autoencoder.fit(x_train, x_train,
epochs=100,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
# encode and decode some digits
# note that we take them from the *test* set
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)
# use Matplotlib (don't ask)
import matplotlib.pyplot as plt
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()