tensorflow 1.0 學習：參數和特徵的提取

在tf中，參與訓練的參數可用 tf.trainable_variables()提取出來，如：

#取出全部參與訓練的參數
params=tf.trainable_variables()
print("Trainable variables:------------------------")

#循環列出參數
for idx, v in enumerate(params):
     print("  param {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))

這裏只能查看參數的shape和name,並無具體的值。若是要查看參數具體的值的話，必須先初始化，即：

sess=tf.Session()
sess.run(tf.global_variables_initializer())

同理，咱們也能夠提取圖片通過訓練後的值。圖片通過卷積後變成了特徵，要提取這些特徵，必須先把圖片feed進去。

具體看實例：

# -*- coding: utf-8 -*-
"""
Created on Sat Jun  3 12:07:59 2017

@author: Administrator
"""

import tensorflow as tf
from skimage import io,transform
import numpy as np

#-----------------構建網絡----------------------
#佔位符
x=tf.placeholder(tf.float32,shape=[None,100,100,3],name='x')
y_=tf.placeholder(tf.int32,shape=[None,],name='y_')

#第一個卷積層（100——>50)
conv1=tf.layers.conv2d(
      inputs=x,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu,
      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool1=tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

#第二個卷積層(50->25)
conv2=tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu,
      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool2=tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

#第三個卷積層(25->12)
conv3=tf.layers.conv2d(
      inputs=pool2,
      filters=128,
      kernel_size=[3, 3],
      padding="same",
      activation=tf.nn.relu,
      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool3=tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)

#第四個卷積層(12->6)
conv4=tf.layers.conv2d(
      inputs=pool3,
      filters=128,
      kernel_size=[3, 3],
      padding="same",
      activation=tf.nn.relu,
      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
pool4=tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2)

re1 = tf.reshape(pool4, [-1, 6 * 6 * 128])

#全鏈接層
dense1 = tf.layers.dense(inputs=re1, 
                      units=1024, 
                      activation=tf.nn.relu,
                      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                      kernel_regularizer=tf.nn.l2_loss)
dense2= tf.layers.dense(inputs=dense1, 
                      units=512, 
                      activation=tf.nn.relu,
                      kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                      kernel_regularizer=tf.nn.l2_loss)
logits= tf.layers.dense(inputs=dense2, 
                        units=5, 
                        activation=None,
                        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                        kernel_regularizer=tf.nn.l2_loss)

#---------------------------網絡結束---------------------------
#%%
#取出全部參與訓練的參數
params=tf.trainable_variables()
print("Trainable variables:------------------------")

#循環列出參數
for idx, v in enumerate(params):
     print("  param {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))

#%%
#讀取圖片
img=io.imread('d:/cat.jpg')
#resize成100*100
img=transform.resize(img,(100,100))
#三維變四維（100，100，3）-->(1,100,100,3)
img=img[np.newaxis,:,:,:]
img=np.asarray(img,np.float32)
sess=tf.Session()
sess.run(tf.global_variables_initializer()) 

#提取最後一個全鏈接層的參數 W和b
W=sess.run(params[26])
b=sess.run(params[27])

#提取第二個全鏈接層的輸出值做爲特徵    
fea=sess.run(dense2,feed_dict={x:img})

最後一條語句就是提取某層的數據輸出做爲特徵。

注意：這個程序並無通過訓練，所以提取出的參數只是初始化的參數。