我刚开始学习TensorFlow和神经网络，我正在尝试开发一个能够预测某个属性值的神经网络（这是Kaggle.com上的一个入门比赛）。我知道使用神经网络可能不是解决回归问题的最佳模型，但我决定尝试一下。

当使用单层神经网络（没有隐藏层，这可能是一个线性回归）时，模型实际上预测的值与实际值非常接近。然而，当我添加一个隐藏层后，所有预测的值在20个输入张量的一批中都是相同的：

   ('real', array([[ 181000.],       [ 128900.],       [ 161500.],       [ 180500.],       [ 181000.],       [ 183900.],       [ 122000.],       [ 378500.],       [ 381000.],       [ 144000.],       [ 260000.],       [ 185750.],       [ 137000.],       [ 177000.],       [ 139000.],       [ 137000.],       [ 162000.],       [ 197900.],       [ 237000.],       [  68400.]]))('prediction ', array([[ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687],       [ 4995.10597687]]))

更新：我注意到预测的值仅反映了输出层的偏置，而隐藏层和输出层的权重没有变化，并且始终为零。

为了进一步检查哪里出了问题，我生成了模型的图表（一次使用隐藏层，另一次不使用隐藏层），以比较这两个图表，看看是否缺少某些东西，不幸的是，它们对我来说看起来都是正确的，但我仍然不明白为什么在没有隐藏层时模型能工作，而使用隐藏层时却不能工作。

工作模型的图表（中间没有隐藏层）：

不工作模型的图表（带有一个隐藏层和一个输出层）

我的完整代码如下：

# coding: utf-8import tensorflow as tf import numpy as np def loadDataFromCSV(fileName , numberOfFields , numberOfOutputFields , numberOfRecords):    XsArray = np.ndarray([numberOfRecords ,(numberOfFields-numberOfOutputFields)] , dtype=np.float64)    YsArray = np.ndarray([numberOfRecords ,numberOfOutputFields] , dtype=np.float64)    fileQueue = tf.train.string_input_producer(fileName)    defaultValues = [[0]]*numberOfFields    decodedLine = [[None]]*numberOfFields    reader  = tf.TextLineReader()    key , singleLine = reader.read(fileQueue)    decodedLine = tf.decode_csv(singleLine,record_defaults=defaultValues)    inputFeatures = decodedLine[0:numberOfFields-numberOfOutputFields]    outputFeatures =decodedLine[numberOfFields-numberOfOutputFields:numberOfFields]    with tf.Session() as session :         tf.global_variables_initializer().run()        coor = tf.train.Coordinator()        threads = tf.train.start_queue_runners(coord=coor)        for i in range(numberOfRecords) :            XsArray[i,:] ,YsArray[i,:]  = session.run([inputFeatures , outputFeatures])         coor.request_stop()        coor.join(threads)    return XsArray , YsArrayx , y =loadDataFromCSV(['/Users/mousaalsulaimi/Downloads/convertcsv.csv'] , 289 , 1, 1460)num_steps = 10000batch_size = 20 graph = tf.Graph()with graph.as_default() :    with tf.name_scope('input'):        inputProperties  = tf.placeholder(tf.float32 , shape=(batch_size ,287 ))    with tf.name_scope('realPropertyValue') :        outputValues = tf.placeholder(tf.float32,shape=(batch_size,1))    with tf.name_scope('weights'):        hidden1_w  = tf.Variable( tf.truncated_normal([287,1000],stddev=math.sqrt(3/(287+1000)) , dtype=tf.float32))    with tf.name_scope('baises'):        hidden1_b = tf.Variable( tf.zeros([1000] , dtype=tf.float32) )    with tf.name_scope('hidden_layer'):        hidden1 =tf.matmul(inputProperties,hidden1_w) + hidden1_b    #hidden1_relu = tf.nn.relu(hidden1)    #hidden1_dropout = tf.nn.dropout(hidden1_relu,.5)    with tf.name_scope('layer2_weights'):        output_w  = tf.Variable(tf.truncated_normal([1000,1],stddev=math.sqrt(3/(1000+1)) , dtype=tf.float32))    with tf.name_scope('layer2_baises'):        output_b = tf.Variable(tf.zeros([1] , dtype=tf.float32))    with tf.name_scope('layer_2_predictions'):        output =tf.matmul(hidden1,output_w) + output_b    with tf.name_scope('predictions'):        predictedValues = (output)    loss = tf.sqrt(tf.reduce_mean(tf.square(predictedValues-outputValues)))    loss_l2 = tf.nn.l2_loss(hidden1_w)    with tf.name_scope('minimization') :        minimum = tf.train.AdamOptimizer(.5).minimize(loss+.004*loss_l2)with tf.Session(graph=graph) as session:    tf.global_variables_initializer().run()    print("Initialized")    for step in range(num_steps):        # Pick an offset within the training data, which has been randomized.        # Note: we could use better randomization across epochs.        offset = (step * batch_size) % (y.shape[0] - batch_size)        # Generate a minibatch.        batch_data = x[offset:(offset + batch_size), 1:]        batch_labels = y[offset:(offset + batch_size), :]        print("real" , batch_labels)        # Prepare a dictionary telling the session where to feed the minibatch.        # The key of the dictionary is the placeholder node of the graph to be fed,        # and the value is the numpy array to feed to it.        feed_dict = {inputProperties : batch_data, outputValues : batch_labels}        _, l, predictions  , inp  = session.run([minimum, loss, predictedValues  ,inputProperties ], feed_dict=feed_dict)        print("prediction " , predictions)        print("loss : " , l)        print("----------")        print('+++++++++++')

我还上传了数据文件convertcsv.csv 这里，以防您想查看一下。

我非常感谢任何帮助我找出我做错的地方的建议。

谢谢您

回答：

好的，所以我终于明白问题所在了，正如预期的那样，是神经网络中的权重问题，我还进行了一些预处理以改善预测：