我正在学习教程并可以浏览代码，训练神经网络并评估其准确性。

但我不知道如何在新的单个输入(字符串)上使用经过训练的模型来预测其标签。

你能建议如何做到这一点吗？

教程:

https://medium.freecodecamp.org/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274

session 代码:

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(len(newsgroups_train.data)/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_x,batch_y = get_batch(newsgroups_train,i,batch_size)
            # Run optimization op (backprop) and cost op (to get loss value)
            c,_ = sess.run([loss,optimizer], feed_dict={input_tensor: batch_x,output_tensor:batch_y})
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "loss=", \
                "{:.9f}".format(avg_cost))
    print("Optimization Finished!")

    # Test model
    correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(output_tensor, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    total_test_data = len(newsgroups_test.target)
    batch_x_test,batch_y_test = get_batch(newsgroups_test,0,total_test_data)
    print("Accuracy:", accuracy.eval({input_tensor: batch_x_test, output_tensor: batch_y_test}))

我对 Python 有一些经验，但对 Tensorflow 基本没有经验。

最佳答案

Tensorflow 使用声明式编程风格。您需要声明您希望它做什么，然后才调用它的run 或eval 函数。

1) 如果您想对您的模型进行一些交互式修改，您需要打开Session 处理程序。将第一行替换为:

# Launch the graph
sess = tf.Session()
with sess.as_default():
    .......

原代码关闭session，不能再继续使用训练好的模型。 不要忘记在不需要时调用 sess.close() 来释放分配给 TF 的资源。

2) 现在您必须将要分类的文本转换为数值张量表示。在原始代码中，它是通过 get_batch() 完成的。遵循相同的模式。

3) 声明结果。您的模型与变量 prediction 相关联。

4) 调用 TF。 所以最终的代码看起来像:

texts = ['''By '8 grey level images' you mean 8 items of 1bit images?
It does work(!), but it doesn't work if you have more than 1bit
in your screen and if the screen intensity is non-linear.''',

'''Wanted: Shareware graphics display program for DOS.
Distribution: usa\nOrganization: University of Notre Dame, Notre Dame
Lines: 16 I need a graphics display program that can take as a parameter the name of
the file to be displayed, then just display that image and then quit.
All of the other graphics display programs come up with a menu first or some other silliness.
This program is going to be run from within another program.  '''       
        ]
# convert texts to tensors
batch = []
for text in texts:
    vector = np.zeros(total_words,dtype=float)
    for word in text.split(' '):
        if word in word2index:
            vector[word2index[word.lower()]] += 1
    batch.append(vector)

x_in = np.array(batch)

# declare new Graph node variable
category = tf.argmax(prediction,1) # choose by maximum score

# run TF
with sess.as_default():
    print("scores:", prediction.eval({input_tensor: x_in}))
    print('class:', category.eval({input_tensor: x_in}))


Out[]:
scores: [[-785.557    -781.1719    105.238686]
         [ 554.584    -532.36383   263.20908 ]]
class: [2 0] 

关于python - 在 Tensorflow 中使用多层感知器模型预测文本标签，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/50535247/