恢复保存的 TensorFlow 模型以在测试集上进行评估

Question

我在恢复TF 模型和exporting graphs 上的Google 文档页面上看到了一些posts，但我认为我遗漏了一些东西。

我使用 Gist 中的代码将模型与 defines 模型的 utils 文件一起保存

现在我想恢复它并在以前看不见的测试数据中运行如下：

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    total_loss = 0
    sess = tf.get_default_session()
    acc_steps = len(X_data) // BATCH_SIZE
    for i in range(acc_steps):
        batch_x, batch_y = next_batch(X_val, Y_val, BATCH_SIZE)

        loss, accuracy = sess.run([loss_value, acc], feed_dict={
                images_placeholder: batch_x,
                labels_placeholder: batch_y,
                keep_prob: 0.5
                })
        total_accuracy += (accuracy * len(batch_x))
        total_loss += (loss * len(batch_x))
    return (total_accuracy / num_examples, total_loss / num_examples)

## re-execute the code that defines the model

# Image Tensor
images_placeholder = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name='x')

gray = tf.image.rgb_to_grayscale(images_placeholder, name='gray')

gray /= 255.

# Label Tensor
labels_placeholder = tf.placeholder(tf.float32, shape=(None, 43), name='y')

# dropout Tensor
keep_prob = tf.placeholder(tf.float32, name='drop')

# construct model
logits = inference(gray, keep_prob)

# calculate loss
loss_value = loss(logits, labels_placeholder)

# training
train_op = training(loss_value, 0.001)

# accuracy
acc = accuracy(logits, labels_placeholder)

with tf.Session() as sess:
    loader = tf.train.import_meta_graph('gtsd.meta')
    loader.restore(sess, tf.train.latest_checkpoint('./'))
    sess.run(tf.initialize_all_variables())   
    test_accuracy = evaluate(X_test, y_test)
    print("Test Accuracy = {:.3f}".format(test_accuracy[0]))

我得到的测试准确率只有 3%。但是，如果我在训练模型后不关闭笔记本并立即运行测试代码，我将获得 95% 的准确度。

这让我相信我没有正确加载模型？

Answer 1

问题出在这两行：

loader.restore(sess, tf.train.latest_checkpoint('./'))
sess.run(tf.initialize_all_variables())   

第一行从检查点加载保存的模型。第二行重新初始化模型中的所有变量（例如权重矩阵、卷积滤波器和偏置向量），通常为随机数，并覆盖加载值。

解决方案很简单：删除第二行 (sess.run(tf.initialize_all_variables()))，然后评估将从检查点加载的训练值继续进行。

---

PS。此更改很有可能会给您一个关于“未初始化变量”的错误。在这种情况下，您应该执行sess.run(tf.initialize_all_variables()) 以在执行loader.restore(sess, tf.train.latest_checkpoint('./')) 之前 初始化任何未保存在检查点中的变量。

Answer 2

我遇到了类似的问题，对我来说这很有效：

with tf.Session() as sess:
    saver=tf.train.Saver(tf.all_variables())
    saver=tf.train.import_meta_graph('model.meta')
    saver.restore(sess,"model")

    test_accuracy = evaluate(X_test, y_test)

Answer 3

here 找到的答案最终工作如下：

save_path = saver.save(sess, '/home/ubuntu/gtsd-12-23-16.chkpt')
print("Model saved in file: %s" % save_path)
## later re-run code that creates the model
# Image Tensor
images_placeholder = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name='x')

gray = tf.image.rgb_to_grayscale(images_placeholder, name='gray')

gray /= 255.

# Label Tensor
labels_placeholder = tf.placeholder(tf.float32, shape=(None, 43), name='y')

# dropout Tensor
keep_prob = tf.placeholder(tf.float32, name='drop')

# construct model
logits = inference(gray, keep_prob)

# calculate loss
loss_value = loss(logits, labels_placeholder)

# training
train_op = training(loss_value, 0.001)

# accuracy
acc = accuracy(logits, labels_placeholder)

saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, '/home/ubuntu/gtsd-12-23-16.chkpt')
        print("Model restored.")
        test_accuracy = evaluate(X_test, y_test)
        print("Test Accuracy = {:.3f}".format(test_accuracy[0]*100))