#!/usr/bin/env python
# coding: utf-8

# # 程序说明
# 时间：2016年11月16日
# 
# 说明：该程序是一个包含两个隐藏层的神经网络。演示如何加载一个保存好的模型。
# 
# 数据集：MNIST

# ## 1.加载keras模块

# In[1]:


from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility


# In[2]:


from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils


# ### 需要加载load_model

# In[3]:


from keras.models import load_model


# ## 2.变量初始化

# In[4]:


batch_size = 128 
nb_classes = 10
nb_epoch = 20 


# ## 3.准备数据

# In[5]:


# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')


# ### 转换类标号

# In[6]:


# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)


# ## 4.建立模型
# ### 在现有的文件中加载模型

# In[7]:


model = load_model('mnist-mpl.h5')


# ### 打印模型

# In[8]:


model.summary()


# ## 5.训练与评估
# ### 编译模型

# In[9]:


model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])


# ### 模型评估

# In[10]:


score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])


# In[ ]: