tensorflow學習筆記——驗證碼識別（四）模型測試

 這裏對之前訓練好的模型進行測試，代碼如下：

import os
import tensorflow as tf 
from PIL import Image
from nets import nets_factory
import numpy as np
import matplotlib.pyplot as plt  

# 不同字符數量
CHAR_SET_LEN = 10
# 圖片高度
IMAGE_HEIGHT = 60 
# 圖片寬度
IMAGE_WIDTH = 160  
# 批次
BATCH_SIZE = 1
# tfrecord文件存放路徑
TFRECORD_FILE = "D:/Tensorflow/captcha/test.tfrecords"

# placeholder
x = tf.placeholder(tf.float32, [None, 224, 224])  

# 從tfrecord讀出數據
def read_and_decode(filename):
    # 根據文件名生成一個隊列
    filename_queue = tf.train.string_input_producer([filename])
    reader = tf.TFRecordReader()
    # 返回文件名和文件
    _, serialized_example = reader.read(filename_queue)   
    features = tf.parse_single_example(serialized_example,
                                       features={
                                           'image' : tf.FixedLenFeature([], tf.string),
                                           'label0': tf.FixedLenFeature([], tf.int64),
                                           'label1': tf.FixedLenFeature([], tf.int64),
                                           'label2': tf.FixedLenFeature([], tf.int64),
                                           'label3': tf.FixedLenFeature([], tf.int64),
                                       })
    # 獲取圖片數據
    image = tf.decode_raw(features['image'], tf.uint8)
    # 沒有經過預處理的灰度圖
    image_raw = tf.reshape(image, [224, 224])
    # tf.train.shuffle_batch必須確定shape
    image = tf.reshape(image, [224, 224])
    # 圖片預處理
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    # 獲取label
    label0 = tf.cast(features['label0'], tf.int32)
    label1 = tf.cast(features['label1'], tf.int32)
    label2 = tf.cast(features['label2'], tf.int32)
    label3 = tf.cast(features['label3'], tf.int32)

    return image, image_raw, label0, label1, label2, label3

# 獲取圖片數據和標籤
image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE)

#使用shuffle_batch可以隨機打亂
image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(
        [image, image_raw, label0, label1, label2, label3], batch_size = BATCH_SIZE,
        capacity = 50000, min_after_dequeue=10000, num_threads=1)

#定義網絡結構
train_network_fn = nets_factory.get_network_fn(
    'alexnet_v2',
    num_classes=CHAR_SET_LEN,
    weight_decay=0.0005,
    is_training=False)

with tf.Session() as sess:
    # inputs: a tensor of size [batch_size, height, width, channels]
    X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])
    # 數據輸入網絡得到輸出值
    logits0,logits1,logits2,logits3,end_points = train_network_fn(X)
    
    # 預測值
    predict0 = tf.reshape(logits0, [-1, CHAR_SET_LEN])  
    predict0 = tf.argmax(predict0, 1)  

    predict1 = tf.reshape(logits1, [-1, CHAR_SET_LEN])  
    predict1 = tf.argmax(predict1, 1)  

    predict2 = tf.reshape(logits2, [-1, CHAR_SET_LEN])  
    predict2 = tf.argmax(predict2, 1)  

    predict3 = tf.reshape(logits3, [-1, CHAR_SET_LEN])  
    predict3 = tf.argmax(predict3, 1)  

    # 初始化
    sess.run(tf.global_variables_initializer())
    # 載入訓練好的模型
    saver = tf.train.Saver()
    saver.restore(sess,'./captcha/models/crack_captcha.model-6000')

    # 創建一個協調器，管理線程
    coord = tf.train.Coordinator()
    # 啓動QueueRunner, 此時文件名隊列已經進隊
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    for i in range(10):
        # 獲取一個批次的數據和標籤
        b_image, b_image_raw, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, 
                                                                    image_raw_batch, 
                                                                    label_batch0, 
                                                                    label_batch1, 
                                                                    label_batch2, 
                                                                    label_batch3])
        # 顯示圖片
        img=Image.fromarray(b_image_raw[0],'L')
        plt.imshow(img)
        plt.axis('off')
        plt.show()
        # 打印標籤
        print('label:',b_label0, b_label1 ,b_label2 ,b_label3)
        # 預測
        label0,label1,label2,label3 = sess.run([predict0,predict1,predict2,predict3], feed_dict={x: b_image})
        # 打印預測值
        print('predict:',label0,label1,label2,label3) 
                
    # 通知其他線程關閉
    coord.request_stop()
    # 其他所有線程關閉之後，這一函數才能返回
    coord.join(threads)

效果如下：