Keras，多层自动编码器，不同数量的隐藏层具有相同的精度

Question

我目前正在尝试使用Keras实现多层自动编码器，但是我开始相信我的实现是错误的，因为通过使用2或3个隐藏层在准确性和方差上都没有任何区别。我尝试使用400/180的两个隐藏层，然后尝试使用节点400/180/30的三个隐藏层，实际上得到了完全相同的结果。我的代码当前看起来像这样：

from keras.layers import Input, Dense, initializers
import numpy as np
from Dataset import Dataset
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.optimizers import Adam
import time

#global variables
d = Dataset()
num_features = d.X_train.shape[1]
#input = [784, 400, 100, 10, 100, 400]
#output = [400, 100, 10, 100, 400, 784]
#names = ['hidden1', 'hidden2', 'hidden3', 'hidden4', 'hidden5', 'hidden6']

list_of_nodes = [784, 400, 180]

def generate_hidden_nodes(list_of_nodes):
    input = []
    for j in range(len(list_of_nodes)):
        input.append(list_of_nodes[j])
    for i in range(len(list_of_nodes)-2):
        input.append(list_of_nodes[-2-i])
    output = input[::-1]
    return input, output

input,output = generate_hidden_nodes(list_of_nodes)





def autoencoder(epochs):
    w = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None)
    model = Sequential()
    input, output = generate_hidden_nodes(list_of_nodes)
    for j in range(len(input)):
        if j == (len(input)-1):
            model.add(Dense(output[j], activation='sigmoid', kernel_initializer=w, input_dim=input[j]))
            #model.add(Dropout(0.45))
        else:
            model.add(Dense(output[j], activation='relu', kernel_initializer=w, input_dim=input[j],
                            ))
            #model.add(Dropout(0.45))
    model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['acc'])
    history = model.fit(d.X_train, d.X_train,
                        epochs=epochs,
                        batch_size=50,
                        shuffle=True,
                        validation_split = 0.2)
                        #validation_data=(d.X_test, d.X_test))
    #print(history.history.keys())
    #plt.plot(history.history['val_acc'])
    #print(history.history['val_acc'])
    plt.show()
    return model

def cv():
    accuracy = 0
    size = 5
    epochs = 20
    variance = 0
    storage = np.zeros((size, epochs))
    for j in range(size):
        ae = autoencoder(epochs)
        #print(ae.history.history['val_acc'])
        storage[j] = ae.history.history['val_acc']
    for i in range(size):
        accuracy += storage[i][-1]
    mean = accuracy/size
    for k in range(size):
        variance += ((storage[k][-1] - mean)**2)
    variance = variance/size
    return mean, variance

mean, variance = cv()
print(mean)
print(variance)
time.sleep(10)

def finding_index():
    elements, index = np.unique(d.Y_test, return_index=True)
    return elements, index

def plotting():
    ae = autoencoder(20)
    elements, index = finding_index()
    y_proba = ae.predict(d.X_test)
    plt.figure(figsize=(20, 4))
    # size = 20
    for i in range(len(index)):
        ax = plt.subplot(2, len(index), i + 1)
        plt.imshow(d.X_test[index[i]].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)

        ax = plt.subplot(2, len(index), i + 1 + len(index))
        plt.imshow(y_proba[index[i]].reshape(28, 28))
        plt.gray()
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
    plt.show()


plotting()

我期望通过将其最终发送到仅30个节点（并将其解码回去）而产生显着差异。我的代码中是否有明显的错误，我当前使用的数据集是Mnist数据集（手写数字）。预先感谢！