我在浏览Keras博客时，发现了一个简单的自编码器。这个自编码器是用Keras编写的，并且运行正常。
我在代码中做了一些修改，以使用Tensorflow 2的Keras函数式API。现在的问题是，代码没有报错，但没有按预期工作（验证损失大于0.6）。
我无法在代码中找到任何错误。以下是修改后的代码：
from tensorflow.keras.layers import Dense, Inputfrom tensorflow import kerasfrom tensorflow.keras.datasets import mnistimport numpy as npencoding_dim = 32input_img = Input(shape=(784,))encoded = Dense(encoding_dim, activation='relu')(input_img)decoded = Dense(784, activation='sigmoid')(encoded)autoencoder = keras.Model(input_img, decoded)encoder = keras.Model(input_img, encoded)encoded_input = Input(shape=(encoding_dim,))decoder_layer = autoencoder.layers[-1]decoder = keras.Model(encoded_input, decoder_layer(encoded_input))autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')(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=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))encoded_imgs = encoder.predict(x_test)decoded_imgs = decoder.predict(encoded_imgs)# use Matplotlib (don't ask)import matplotlib.pyplot as pltn = 10 # how many digits we will displayplt.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()
回答：
如果你将优化器改为adam，损失函数会收敛。同时请查看这个问题：
