Keras ValueError while compiling a loaded model

I trained the following net and saved it. When compiling the reloaded network, the error appears:

ValueError: Error when checkingModelTarget: expected dense_3 to haveFast (None, 1) but got array with shape (10000, 10)

What can be the reason? The solutions of many similar problems do not really help me.

code:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.models import model_from_json

K.set_image_dim_ordering('th')

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

def larger_model():
    # create model
    model = Sequential()
    model.add(Convolution2D(30, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(15, 3, 3, activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# build the model
model = larger_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=1, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))


# save model and weights
print("Saving model...")
model_json = model.to_json()
with open('mnist_model.json', 'w') as json_file:
    json_file.write(model_json)
model.save_weights("mnist_weights.h5")
print("model saved to disk")

# load model and weights
print("Laoding model...")
with open('mnist_model.json') as json_file:
    model_json = json_file.read()

model = model_from_json(model_json)
model.load_weights('mnist_weights.h5')
print("mode loaded from disk")

print("compiling model...")
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))