一、預備知識
（1）tensor : 符號化的句柄，指向操作的運算結果。
（2）operation : 符號化的運算過程，即圖中的節點。
（3）graph : 模型結構的流程圖，tensor 和 operation 都是圖中的對象。
（4）tf.gfile.FastGFile(path,decodestyle)
函數功能：實現對圖片的讀取。
函數參數：(1)path：圖片所在路徑 (2)decodestyle:圖片的解碼方式。(‘r’:UTF-8編碼; ‘rb’:非UTF-8編碼)。
import matplotlib.pyplot as plt
import tensorflow as tf
#tf.gfileGFile()函數：讀取圖像
image_jpg = tf.gfile.FastGFile('dog.jpg','rb').read()
image_png = tf.gfile.FastGFile('lizard.png','rb').read()
with tf.Session() as sess:
image_jpg = tf.image.decode_jpeg(image_jpg) #圖像解碼
print(sess.run(image_jpg))#打印解碼後的圖像（即爲一個三維矩陣[w,h,3]）
image_jpg = tf.image.convert_image_dtype(image_jpg,dtype=tf.uint8) #改變圖像數據類型
image_png = tf.image.decode_png(image_png)
print(sess.run(image_jpg))
image_png = tf.image.convert_image_dtype(image_png,dtype=tf.uint8)
plt.figure(1)
plt.imshow(image_jpg.eval())
plt.figure(2)
plt.imshow(image_png.eval()) 二、使用 Inception V3 做圖像分類
# coding: utf-8
import tensorflow as tf
import os
import numpy as np
import re
from PIL import Image
import matplotlib.pyplot as plt
class NodeLookup(object):
def __init__(self):
label_lookup_path = 'inception_model/imagenet_2012_challenge_label_map_proto.pbtxt'
uid_lookup_path = 'inception_model/imagenet_synset_to_human_label_map.txt'
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
def load(self, label_lookup_path, uid_lookup_path):
# 加載分類字符串n********對應分類名稱的文件
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
#一行一行讀取數據
for line in proto_as_ascii_lines :
#去掉換行符
line=line.strip('\n')
#按照'\t'分割
parsed_items = line.split('\t')
#獲取分類編號
uid = parsed_items[0]
#獲取分類名稱
human_string = parsed_items[1]
#保存編號字符串n********與分類名稱映射關係
uid_to_human[uid] = human_string
# 加載分類字符串n********對應分類編號1-1000的文件
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
node_id_to_uid = {}
for line in proto_as_ascii:
if line.startswith(' target_class:'):
#獲取分類編號1-1000
target_class = int(line.split(': ')[1])
if line.startswith(' target_class_string:'):
#獲取編號字符串n********
target_class_string = line.split(': ')[1]
#保存分類編號1-1000與編號字符串n********映射關係
node_id_to_uid[target_class] = target_class_string[1:-2]
#建立分類編號1-1000對應分類名稱的映射關係
node_id_to_name = {}
for key, val in node_id_to_uid.items():
#獲取分類名稱
name = uid_to_human[val]
#建立分類編號1-1000到分類名稱的映射關係
node_id_to_name[key] = name
return node_id_to_name
#傳入分類編號1-1000返回分類名稱
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]
#創建一個圖來存放google訓練好的模型
with tf.gfile.FastGFile('inception_model/classify_image_graph_def.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
with tf.Session() as sess:
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
#遍歷目錄
for root,dirs,files in os.walk('images/'):
for file in files:
#載入圖片
image_data = tf.gfile.FastGFile(os.path.join(root,file), 'rb').read()
predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)#把結果轉爲1維數據
#打印圖片路徑及名稱
image_path = os.path.join(root,file)
print(image_path)
#顯示圖片
img=Image.open(image_path)
plt.imshow(img)
plt.axis('off')
plt.show()
top_k = predictions.argsort()[-5:][::-1]
node_lookup = NodeLookup()
for node_id in top_k:
#獲取分類名稱
human_string = node_lookup.id_to_string(node_id)
#獲取該分類的置信度
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
print()建一個文件夾將圖片放進去就可以，運行結果就不演示了。
三、解讀模型
# coding: utf-8
import tensorflow as tf
import os
import tarfile
import requests
# inception模型下載地址
inception_pretrain_model_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# 模型存放地址
inception_pretrain_model_dir = "inception_model"
if not os.path.exists(inception_pretrain_model_dir):
os.makedirs(inception_pretrain_model_dir)
# 獲取文件名，以及文件路徑
filename = inception_pretrain_model_url.split('/')[-1]
filepath = os.path.join(inception_pretrain_model_dir, filename)
# 下載模型
if not os.path.exists(filepath):
print("download: ", filename)
r = requests.get(inception_pretrain_model_url, stream=True)
with open(filepath, 'wb') as f:
#一塊一塊地下載數據，防止內存不夠用
for chunk in r.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
print("finish: ", filename)
# 解壓文件
tarfile.open(filepath, 'r:gz').extractall(inception_pretrain_model_dir)
# 模型結構存放文件
log_dir = 'inception_log'
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# classify_image_graph_def.pb爲google訓練好的模型
inception_graph_def_file = os.path.join(inception_pretrain_model_dir, 'classify_image_graph_def.pb')
with tf.Session() as sess:
# 創建一個圖來存放google訓練好的模型
with tf.gfile.FastGFile(inception_graph_def_file, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
# 保存圖的結構
print('save sess.graph in ',log_dir)
writer = tf.summary.FileWriter(log_dir, sess.graph)
writer.close()命令行輸入
tensorboard --logdir=/home/hadoop/PycharmProject/tensorflow/inception_log打開tensorboard 。
可以看到模型結構，除了Inception module group 值得研究，還有數據的輸入節點（operation）值得注意，我們就是通過
predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})feed進圖片的。後面的節點經過維度的擴張將圖片數據轉換成 Inception V3 中規定的格式。
四、保存和載入模型
第一種加載方式：
#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size=100
n_batch=mnist.train.num_examples//batch_size
x=tf.placeholder(tf.float32,[None,784], name='input_x')
y=tf.placeholder(tf.float32,[None,10], name='input_y')
W = tf.Variable(tf.zeros([784,10]), name='w1')
b = tf.Variable(tf.zeros([10]), name='b1')
prediction = tf.nn.softmax(tf.matmul(x,W)+b,name='op')
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
init = tf.global_variables_initializer()
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
saver = tf.train.Saver()
tf.add_to_collection('pred_network', prediction)
with tf.Session() as sess:
sess.run(init)
for epoch in range(11):
for batch in range(n_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
saver.save(sess, 'net/my_net.ckpt')#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
batch_size=100
n_batch=mnist.train.num_examples//batch_size
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W)+b)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
init = tf.global_variables_initializer()
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
saver=tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
print('參數全部初始化爲零')
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))
print('加載已訓練的模型')
saver.restore(sess,'net/my_net.ckpt')
#print(mnist.test.images.shape[0])
print(sess.run(accuracy,feed_dict={x: mnist.test.images, y: mnist.test.labels}))第二種加載方式：
上面代碼都一樣，就是上下文管理器中修改一處：
with tf.Session() as sess:
sess.run(init)
for epoch in range(11):
for batch in range(n_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
saver.save(sess, './my-model/my_model', global_step=n_batch*epoch)
#coding=utf-8
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
with tf.Session() as sess:
new_saver = tf.train.import_meta_graph('my-model/my_model-5500.meta')
new_saver.restore(sess, 'my-model/my_model-5500')
# tf.get_collection() 返回一個list. 但是這裏只要第一個參數即可
predict = tf.get_collection('pred_network')[0]
graph = tf.get_default_graph()
# 因爲y中有placeholder，所以sess.run(y)的時候還需要用實際待預測的樣本以及相應的參數來填充這些placeholder，
# 而這些需要通過graph的get_operation_by_name方法來獲取。
input_x = graph.get_operation_by_name('input_x').outputs[0]
#keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]
# 使用X進行預測
X = mnist.test.images[0]
X = X.reshape((1,784))
print(sess.run(predict, feed_dict={input_x: X}))