#!/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。
# 
# 
# 
# > Very Deep Convolutional Networks for Large-Scale Image Recognition
# 
# > K. Simonyan, A. Zisserman
# 
# > arXiv:1409.1556

# ## 1.加载keras模块

# In[1]:


from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# ### 显示图像

# In[2]:


img_path = './data/elephant.jpg'

img = image.load_img(img_path, target_size=(224, 224))

plt.imshow(img)
plt.show()


# ### 加载VGG16模型（包含全连接层）

# In[3]:


model = VGG16(include_top=True, weights='imagenet')
print(type(model))


# In[4]:


x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

x = preprocess_input(x)

print(x.max())

scores = model.predict(x)


# In[10]:


class_table = open('./data/synset_words', 'r')
lines = class_table.readlines()
print("scores type: ", type(scores))
print("scores shape: ", scores.shape)
print(np.argmax(scores))
print('result is ', lines[np.argmax(scores)])
class_table.close()


# In[6]:


del model


# ### 加载VGG16模型（不包含包含全连接层）

# In[7]:


model = VGG16(weights='imagenet', include_top=False)

x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

x = preprocess_input(x)

features = model.predict(x)


# In[8]:


print(features.shape)


# In[9]:


model.summary()


# ### 提取block5_pool层特征

# In[10]:


model_extractfeatures = Model(input=model.input, output=model.get_layer('block5_pool').output)


# In[11]:


block5_pool_features = model_extractfeatures.predict(x)


# In[12]:


print(type(block5_pool_features))
print(block5_pool_features.shape)


# In[13]:


feature_image = block5_pool_features[:,:,:,0].reshape(7,7)
plt.imshow(feature_image)
plt.show()


# In[ ]: