利用tensorflow训练自己的图片数据（5）——测试训练网络

一.说明

上一篇博客中，我们已经将建立好的网络模型训练好了，并将训练的网络参数保存在相应的文件中；下面我们就开始测试网络，验证网络的训练效果；本次测试为随机的单图片测试，即随机的从训练集或测试集中读取一张图片，送入到神经网络中进行识别，打印识别率及识别的图像。

二. 编程实现

#=============================================================================
from PIL import Image
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import model
from input_data import get_files

#=======================================================================
#获取一张图片
def get_one_image(train):
#输入参数：train,训练图片的路径
#返回参数：image，从训练图片中随机抽取一张图片
n = len(train)
ind = np.random.randint(0, n)
img_dir = train[ind]   #随机选择测试的图片

img = Image.open(img_dir)
plt.imshow(img)
imag = img.resize([64, 64])  #由于图片在预处理阶段以及resize，因此该命令可略
image = np.array(imag)
return image

#--------------------------------------------------------------------
#测试图片
def evaluate_one_image(image_array):
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 4

image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 64, 64, 3])

logit = model.inference(image, BATCH_SIZE, N_CLASSES)

logit = tf.nn.softmax(logit)

x = tf.placeholder(tf.float32, shape=[64, 64, 3])

# you need to change the directories to yours.
logs_train_dir = 'E:/Re_train/image_data/inputdata/'

saver = tf.train.Saver()

with tf.Session() as sess:

print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(logs_train_dir)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')

prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
if max_index==0:
print('This is a husky with possibility %.6f' %prediction[:, 0])
elif max_index==1:
print('This is a jiwawa with possibility %.6f' %prediction[:, 1])
elif max_index==2:
print('This is a poodle with possibility %.6f' %prediction[:, 2])
else:
print('This is a qiutian with possibility %.6f' %prediction[:, 3])

#------------------------------------------------------------------------

if __name__ == '__main__':

train_dir = 'E:/Re_train/image_data/inputdata'
train, train_label, val, val_label = get_files(train_dir, 0.3)
img = get_one_image(val)  #通过改变参数train or val，进而验证训练集或测试集
evaluate_one_image(img)
#===========================================================================

其中，checkpoint文件会记录保存信息，通过它可以定位最新保存的模型。

注：可参考博客http://blog.csdn.net/jyshee/article/details/52563444

三. 测试结果

下面是训练200次时，随机从测试集和训练集中读取一张图片，分别测试验证集和训练集的识别效果