Python 从load_model（）加载模型时在Keras中获取错误

我是Keras的初学者，我正在为MNIST编写一个简单的程序，但当我尝试加载模型时，我遇到了以下错误：

ValueError: You are trying to load a weight file containing 2 layers into a model with 0 layers.

这是我的代码：

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils

#fixing random number seed
np.random.seed(7)

(X_train, Y_train),(X_test, Y_test) = mnist.load_data("D:\MY CODE PROJECT\CNN\datasets\mnist.npz")
num_pixel   =  X_train.shape[1] * X_train.shape[2]

#converting image to vector
X_train = X_train.reshape(X_train.shape[0],num_pixel).astype('float32')
X_test  = X_test.reshape(X_test.shape[0],num_pixel).astype('float32')

# Normalizing Input from 0-255 to 0-1
X_train = X_train/255
X_test  = X_test/255

#As output is multiclass so change output labels to 'ONE-HOT' ecodings Form
Y_train = np_utils.to_categorical(Y_train)
Y_test  = np_utils.to_categorical(Y_test)

#defining simple Neural Network with one hidden layer
num_classes = Y_test.shape[1]
#creating model
model = Sequential()
model.add(Dense(num_pixel,activation = 'relu',kernel_initializer='normal'))
model.add(Dense(num_classes, kernel_initializer='normal',activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
#Fitting the model
model.fit(X_train,Y_train,batch_size=200,epochs=10,verbose=2,validation_data=(X_test,Y_test))
scores = model.evaluate(X_test,Y_test,verbose=0)
#Printing Error
print("baseline Error: %f" %(100-scores[1]*100))

model.save('mnist_nn_keras.h5')
del model

model = load_model('mnist_nn_keras.h5')

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有人能解释代码中的错误吗？我使用的是Keras 2.2.0版。

在添加图层实例时，需要向模型中添加输入形状。阅读添加函数的文档，它清楚地说明了错误细节。 请看下面的截图