tensorflow中saver.restore(sess,model_v2)

这是一个用于在 TensorFlow 中恢复模型参数的操作。其中，"saver" 是一个 TensorFlow 自带的对象，用于保存和恢复 TensorFlow 模型的参数。在这个操作中，"sess" 是指 TensorFlow 的会话对象，而 "model_v2" 是指模型参数所在的路径。通过这个操作，我们可以将之前训练好的模型参数重新加载到我们的模型中，以便进行预测或继续训练。

相关问题

import time import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow.examples.tutorials.mnist import input_data import mnist_inference import mnist_train tf.compat.v1.reset_default_graph() EVAL_INTERVAL_SECS = 10 def evaluate(mnist): with tf.Graph().as_default() as g: #定义输入与输出的格式 x = tf.compat.v1.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input') y_ = tf.compat.v1.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input') validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels} #直接调用封装好的函数来计算前向传播的结果 y = mnist_inference.inference(x, None) #计算正确率 correcgt_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correcgt_prediction, tf.float32)) #通过变量重命名的方式加载模型 variable_averages = tf.train.ExponentialMovingAverage(0.99) variable_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variable_to_restore) #每隔10秒调用一次计算正确率的过程以检测训练过程中正确率的变化 while True: with tf.compat.v1.Session() as sess: ckpt = tf.train.get_checkpoint_state(minist_train.MODEL_SAVE_PATH) if ckpt and ckpt.model_checkpoint_path: #load the model saver.restore(sess, ckpt.model_checkpoint_path) global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] accuracy_score = sess.run(accuracy, feed_dict=validate_feed) print("After %s training steps, validation accuracy = %g" % (global_step, accuracy_score)) else: print('No checkpoint file found') return time.sleep(EVAL_INTERVAL_SECS) def main(argv=None): mnist = input_data.read_data_sets(r"D:\Anaconda123\Lib\site-packages\tensorboard\mnist", one_hot=True) evaluate(mnist) if __name__ == '__main__': tf.compat.v1.app.run()对代码进行改进

以下是对代码的改进建议：

1. 在代码开头添加注释，简要说明代码功能和使用方法。

2. 将导入模块的语句放在代码开头。

3. 将模型保存路径和评估时间间隔定义为常量，并使用有意义的变量名。

4. 将计算正确率和加载模型的过程封装为函数。

5. 在主函数中调用评估函数。

改进后的代码如下：

# 该代码实现了使用已训练好的模型对 MNIST 数据集进行评估

import time
import tensorflow.compat.v1 as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
import mnist_train

# 定义常量
MODEL_SAVE_PATH = 'model/'
EVAL_INTERVAL_SECS = 10

def evaluate(mnist):
    """
    计算模型在验证集上的正确率
    """
    with tf.Graph().as_default() as g:
        # 定义输入和输出格式
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')

        # 直接调用封装好的函数计算前向传播结果
        y = mnist_inference.inference(x, None)

        # 计算正确率
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        # 加载模型
        variable_averages = tf.train.ExponentialMovingAverage(mnist_train.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        # 在验证集上计算正确率
        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                saver.restore(sess, ckpt.model_checkpoint_path)
                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                accuracy_score = sess.run(accuracy, feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})
                print("After %s training steps, validation accuracy = %g" % (global_step, accuracy_score))
            else:
                print('No checkpoint file found')

def main(argv=None):
    # 读取数据集
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

    # 每隔一定时间评估模型在验证集上的正确率
    while True:
        evaluate(mnist)
        time.sleep(EVAL_INTERVAL_SECS)

if __name__ == '__main__':
    tf.app.run()

以下代码有什么错误，怎么修改： import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from PIL import Image import matplotlib.pyplot as plt import input_data import model import numpy as np import xlsxwriter num_threads = 4 def evaluate_one_image(): workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') 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, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: print("从指定路径中加载模型...") 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('模型加载成功, 训练的步数为： %s' % global_step) else: print('模型加载失败，checkpoint文件没找到！') prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() def evaluate_images(test_img): coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start() coord.request_stop() coord.join(threads) if __name__ == '__main__': test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') evaluate_images(test_img)

在代码中，我注意到了几个错误：

1. 在导入`input_data`和`model`模块时，代码中没有找到这两个模块的导入语句。
2. `evaluate_one_image`函数中的`image_array`参数未定义。
3. 在`evaluate_images`函数中，`tf.train.Coordinator()`和`tf.train.start_queue_runners()`函数之间缺少了一个空行。
4. 在调用`evaluate_one_image`函数时，传递的参数应该是`(image_array, index)`而不是`(image_array, index)`。

以下是修改后的代码：

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from PIL import Image
import matplotlib.pyplot as plt
import input_data
import model
import numpy as np
import xlsxwriter

num_threads = 4

def evaluate_one_image(image_array, index):
    workbook = xlsxwriter.Workbook('formatting.xlsx')
    worksheet = workbook.add_worksheet('My Worksheet')
    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, 208, 208, 3])
        logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES)
        logit = tf.nn.softmax(logit)
        x = tf.placeholder(tf.float32, shape=[208, 208, 3])
        logs_train_dir = 'log/'
        saver = tf.train.Saver()
        with tf.Session() as sess:
            print("从指定路径中加载模型...")
            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('模型加载成功, 训练的步数为： %s' % global_step)
            else:
                print('模型加载失败，checkpoint文件没找到！')
            prediction = sess.run(logit, feed_dict={x: image_array})
            max_index = np.argmax(prediction)
    workbook.close() 

def evaluate_images(test_img):
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for index, img in enumerate(test_img):
        image = Image.open(img)
        image = image.resize([208, 208])
        image_array = np.array(image)
        tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start()

    coord.request_stop()
    coord.join(threads)

if __name__ == '__main__':
    test_dir = 'data/test/'
    import glob
    import xlwt
    test_img = glob.glob(test_dir + '*.jpg')
    evaluate_images(test_img)

请注意，我只修复了我在代码中发现的错误，并没有对其他部分进行修改。如果还有其他错误或问题，请提供更多信息，以便我能够更好地帮助你。