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  • tensorflow:保存与读取网络结构,参数

    训练一个神经网络的目的是啥?不就是有朝一日让它有用武之地吗?可是,在别处使用训练好的网络,得先把网络的参数(就是那些variables)保存下来,怎么保存呢?其实,tensorflow已经给我们提供了很方便的API,来帮助我们实现训练参数的存储与读取,如果想了解详情,请看晦涩难懂的官方API,接下来我简单介绍一下我的理解。

    保存与读取数据全靠下面这个类实现:

    class tf.train.Saver

    当我们需要存储数据时,下面2条指令就够了

    saver = tf.train.Saver()
    save_path = saver.save(sess, model_path)
    解释一下,首先创建一个saver类,然后调用saver的save方法(函数),save需要传递两个参数,一个是你的训练session,另一个是文件存储路径,例如“/tmp/superNet.ckpt”,这个存储路径是可以包含文件名的。save方法会返回一个存储路径。当然,save方法还有别的参数可以传递,这里不再介绍。
    然后怎么读取数据呢?看下面
    saver = tf.train.Saver()
    load_path = saver.restore(sess, model_path)

    和存储数据神似啊!不再赘述。

    下面是重点!关于tf.train.Saver()使用的几点小心得!

    • 1、save方法在实现数据读取时,它仅仅读数据,关键是得有一些提前声明好的variables来接受这些数据,因此,当save读取数据到sess时,需要提前声明与数据匹配的variables,否则程序就报错了。
    • 2、save读取的数据不需要initialize。
    • 3、目前想到的就这么多,随时补充。

    为了对数据存储和读取有更直观的认识,我自己写了两个实验小程序,下面是第一个,训练网络并存储数据,用的MNIST数据集

    import tensorflow as tf
    import sys
    
    # load MNIST data
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets('data', one_hot=True)
    
    # 一些 hyper parameters
    activation = tf.nn.relu
    batch_size = 100
    iteration = 20000
    hidden1_units = 30
    # 注意!这里是存储路径!
    model_path = sys.path[0] + '/simple_mnist.ckpt'
    
    X = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])
    
    W_fc1 = tf.Variable(tf.truncated_normal([784, hidden1_units], stddev=0.2))
    b_fc1 = tf.Variable(tf.zeros([hidden1_units]))
    W_fc2 = tf.Variable(tf.truncated_normal([hidden1_units, 10], stddev=0.2))
    b_fc2 = tf.Variable(tf.zeros([10]))
    
    def inference(img):
        fc1 = activation(tf.nn.bias_add(tf.matmul(img, W_fc1), b_fc1))
        logits = tf.nn.bias_add(tf.matmul(fc1, W_fc2), b_fc2)
        return logits
    
    def loss(logits, labels):
        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, labels)
        loss = tf.reduce_mean(cross_entropy)
        return loss
    
    def evaluation(logits, labels):
        correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        return accuracy
    
    logits = inference(X)
    loss = loss(logits, y_)
    train_op = tf.train.AdamOptimizer(1e-4).minimize(loss)
    accuracy = evaluation(logits, y_)
    
    # 先实例化一个Saver()类
    saver = tf.train.Saver()
    init = tf.initialize_all_variables()
    
    with tf.Session() as sess:
        sess.run(init)
        for i in xrange(iteration):
            batch = mnist.train.next_batch(batch_size)
            if i%1000 == 0 and i:
                train_accuracy = sess.run(accuracy, feed_dict={X: batch[0], y_: batch[1]})
                print "step %d, train accuracy %g" %(i, train_accuracy)
            sess.run(train_op, feed_dict={X: batch[0], y_: batch[1]})
        print '[+] Test accuracy is %f' % sess.run(accuracy, feed_dict={X: mnist.test.images, y_: mnist.test.labels})
        # 存储训练好的variables
        save_path = saver.save(sess, model_path)
        print "[+] Model saved in file: %s" % save_path

    接下来是读取数据并做测试!

    import tensorflow as tf
    import sys
    
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets('data', one_hot=True)
    
    activation = tf.nn.relu
    hidden1_units = 30
    model_path = sys.path[0] + '/simple_mnist.ckpt'
    
    X = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])
    
    W_fc1 = tf.Variable(tf.truncated_normal([784, hidden1_units], stddev=0.2))
    b_fc1 = tf.Variable(tf.zeros([hidden1_units]))
    W_fc2 = tf.Variable(tf.truncated_normal([hidden1_units, 10], stddev=0.2))
    b_fc2 = tf.Variable(tf.zeros([10]))
    
    def inference(img):
        fc1 = activation(tf.nn.bias_add(tf.matmul(img, W_fc1), b_fc1))
        logits = tf.nn.bias_add(tf.matmul(fc1, W_fc2), b_fc2)
        return logits
    
    def evaluation(logits, labels):
        correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        return accuracy
    
    logits = inference(X)
    accuracy = evaluation(logits, y_)
    
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        # 读取之前训练好的数据
        load_path = saver.restore(sess, model_path)
        print "[+] Model restored from %s" % load_path
        print '[+] Test accuracy is %f' % sess.run(accuracy, feed_dict={X: mnist.test.images, y_: mnist.test.labels})

    转:https://www.jianshu.com/p/83fa3aa2d0e9






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  • 原文地址:https://www.cnblogs.com/fclbky/p/10239202.html
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