ValueError：检查输入时出错：预期input_2的形状为（8，），但数组的形状为（1，）

我是一名正在与keras学习自动编码器的学生。我使用mnist数据集作为输入（784个节点），并制作了8个节点作为隐藏层。我想做的是任意调整隐藏层的值。但是，当我在隐藏层中任意输入shape（8，）的输入时，

ValueError：检查输入时出错：预期input_2的形状为（8，），但数组的形状为（1，）

发生错误。我输入的矩阵是np.array（[0,0,0,0,0,0,0,0,0]）的形式，显然是（8，）而不是（1,0）的形式。

下面是代码的全文。请帮忙。谢谢。

from keras.layers import Input, Dense
from keras.models import Model
import matplotlib.pyplot as plt
import numpy as np
import sys

# size of hidden layer
encoding_dim = 8

# input place holder
input_img = Input(shape=(784,))
# "encoded"
encoded = Dense(encoding_dim, activation='relu')(input_img)
# "decoded" (lossy reconstruction)
decoded = Dense(784, activation='sigmoid')(encoded)

# input -> recomstructed model
autoencoder = Model(input_img, decoded)

# encoder model
encoder = Model(input_img, encoded)

encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
# decoder model
decoder = Model(encoded_input, decoder_layer(encoded_input))

autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

from keras.datasets import mnist

np.set_printoptions(threshold=sys.maxsize)
(x_train, train_labels), (x_test, test_labels) = 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:])))
autoencoder.fit(x_train, x_train,
            epochs=30,
            batch_size=256,
            shuffle=True,
            validation_data=(x_test, x_test))

# encoding, decodeing
encoded_imgs = encoder.predict(x_test)
decoded_imgs = decoder.predict(encoded_imgs)
# I want to change this as decoded_imgs = decoder.predict([0,0,0,0,0,0,0,0])


n = 10
plt.figure(num=1, figsize=(20, 3))
for i in range(n):
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(encoded_imgs[i].reshape(2, 4).T)
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()


plt.figure(num=2, figsize=(20, 3))
for i in range(n):
    # original data plot
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_yaxis().set_visible(False)
    ax.get_xaxis().set_visible(False)

    # reconstructed data plot
    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()