为什么tensorflow不更新网络的权重

我正在尝试使用tensorflow，当我开始学习我的网络时，我发现当我运行优化时，Weights没有更新。我不明白为什么tensorflow不更新网络的权重。 这是我在执行任务时使用的代码：

import tensorflow as tf
import numpy as np

def importDataset(path,nsample):
  #--------------------------------------------------------------IMPORT DATASET---------------------------------------------------------------------------------------------------
  filename_queue = tf.train.string_input_producer([path],shuffle=True)

  reader = tf.TextLineReader()
  key, value = reader.read(filename_queue)

  # Default values, in case of empty columns. Also specifies the type of the
  ## decoded result.
  record_defaults = [[1.0], [1.0], [1.0], [1.0],[1.0],[1.0],[1.0],[1.0],[1.0],[1.0],[1.0],[1.0],[1.0],[1.0]]
  col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11, col12, col13, col14 = tf.decode_csv(value, record_defaults=record_defaults)
  features = tf.stack([col2, col3, col4, col5, col6, col7, col8, col9, col10, col11, col12, col13, col14])


  with tf.Session() as sess:
    # Start populating the filename queue.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    training_data= np.matrix([])
    training_y= np.matrix([])
    for i in range(nsample):
      # Retrieve a single instance:
      example, label = sess.run([features, col1])
      #creo un vettore con tre zeri che rappresenta le mie ipotetiche tre classi
      temp=np.zeros(3)
      #devo assegnare nella posizione corrispettiva del vettore la classe che rappresenta
      #es. in posizione 2 ci sara' uno se l'esempio appartiene alla classe 2
      temp[int(label)-1] = 1.0
      if i==0:
        training_data=np.vstack([example])
        training_y=np.vstack([temp])
      else:
        training_data=np.vstack([training_data,example])
        training_y=np.vstack([training_y,temp])

    coord.request_stop()
    coord.join(threads)
    #print(len(training_data[:,1]))
    #print(len(training_y[:,1]))
    #print(training_y)
    return training_data,training_y

def splitDataset(nsample, testPerc, path):
    example,example_y= importDataset(path,nsample)
    #convert training_data and training_y in a list
    example_list   = example.tolist()
    example_y_list = example_y.tolist()
    training_data  = list()
    training_y     = list()
    percent = int((nsample*testPerc)/100)
    #begin uniform extraction from data.

    for i in range(percent):
      index = np.random.randint(0,len(example_list))
      training_data.append(example_list[index])
      training_y.append(example_y_list[index])
      example_list.remove(example_list[index])
      example_y_list.remove(example_y_list[index])

    training_data = np.matrix(training_data)
    training_y    = np.matrix(training_y)
    test_data     = np.matrix(example_list)
    test_y        = np.matrix(example_y_list) 

    #print(len(training_data[:,1]))
    #print(len(training_y[:,1]))
    #print(len(test_data[:,1]))
    #print(len(test_y[:,1]))
    return training_data,training_y,test_data,test_y


#---------------------------------------------INIZIO DEFINIZIONE MODELLO--------------------------------------

x = tf.placeholder(tf.float32, [None,13])
y = tf.placeholder(tf.float32, [None,3])
hidden_Layer1 ={'weights':tf.Variable(tf.truncated_normal([13,3],stddev=0.001)), 'biases':tf.Variable(tf.truncated_normal([3],stddev=0.001))}
hidden_Layer2 ={'weights':tf.Variable(tf.truncated_normal([3,3],stddev=0.001)), 'biases':tf.Variable(tf.truncated_normal([3],stddev=0.001))}
hidden_Layer3 ={'weights':tf.Variable(tf.truncated_normal([3,3],stddev=0.001)), 'biases':tf.Variable(tf.truncated_normal([3],stddev=0.001))}
output_Layer  ={'weights':tf.Variable(tf.truncated_normal([3,3],stddev=0.001)), 'biases':tf.Variable(tf.truncated_normal([3],stddev=0.001))}

#output layer #1
output_Layer1 = tf.add( tf.matmul(x,hidden_Layer1['weights']),hidden_Layer1['biases'])

output_Layer1 = tf.nn.sigmoid(output_Layer1)

#output layer #2
output_Layer2 = tf.add(tf.matmul(output_Layer1,hidden_Layer2['weights']),hidden_Layer2['biases'])
output_Layer2 = tf.nn.sigmoid(output_Layer2)


#output layer #3
output_Layer3 = tf.add(tf.matmul(output_Layer2,hidden_Layer3['weights']),hidden_Layer3['biases'])
output_Layer3 = tf.nn.sigmoid(output_Layer3)

#output layer #output
output_Layer_Output = tf.nn.sigmoid(tf.add(tf.matmul(output_Layer3,output_Layer['weights']),output_Layer['biases']))




#--------------------------------------------FINE DEFINIZIONE MODELLO-------------------------------------------

#--------------------------------------------TRAINING DEL MODELLO-----------------------------------------------
error = tf.nn.l2_loss(output_Layer_Output-y, name="squared_error_cost")

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(error)





sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

#
training_data,training_label,test_data,test_label = splitDataset(178,70,"datasetvino.csv")


for _ in range(100):
    print(sess.run([error,train_step,hidden_Layer1['weights']],feed_dict={x:training_data, y:training_label}))


correct_class = tf.equal(tf.argmax(output_Layer_Output,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_class, tf.float32))

#print(sess.run([accuracy], feed_dict={x:test_data,y:test_label}))

---

您应该围绕您的参数、图层、过滤器大小、过滤器数量和批次大小来实现您的目标性能，您应该注意以下几点：

1-您使用非常低的标准偏差初始化权重，尝试通过0.1 StdDev初始化权重

'weights'：tf.变量（tf.截断_normal（[13,3]，stddev=0.1））

而且，你的偏见太小了，不需要那样初始化，试试看

“偏差”：tf.Variable（tf.constant（0.1，shape=[3]））

2-您的学习率

0.5

太高，请将其降至

0.001

---

使用这些参数，可以在这里随意发布结果，我希望它能有所帮助。

我修改了一点代码，因为我用iris数据集更改了数据集。我用你的提示修改了我的代码，我打印了错误，在迭代过程中错误没有减少，我不知道为什么，也许我应该尝试添加更多的隐藏层

    import tensorflow as tf
import numpy as np

def importDataset(path,nsample):
  #--------------------------------------------------------------IMPORT DATASET---------------------------------------------------------------------------------------------------
  filename_queue = tf.train.string_input_producer([path],shuffle=True)

  reader = tf.TextLineReader()
  key, value = reader.read(filename_queue)

  # Default values, in case of empty columns. Also specifies the type of the
  ## decoded result.
  record_defaults = [[1.0], [1.0], [1.0], [1.0],[1.0]]
  col1, col2, col3, col4, col5 = tf.decode_csv(value, record_defaults=record_defaults)
  features = tf.stack([col1, col2, col3, col4])


  with tf.Session() as sess:
    # Start populating the filename queue.
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    training_data= np.matrix([])
    training_y= np.matrix([])
    for i in range(nsample):
      # Retrieve a single instance:
      example, label = sess.run([features, col5])
      #creo un vettore con tre zeri che rappresenta le mie ipotetiche tre classi

      temp=np.zeros(3)
      #devo assegnare nella posizione corrispettiva del vettore la classe che rappresenta
      #es. in posizione 2 ci sara' uno se l'esempio appartiene alla classe 2
      temp[int(label)] = 1.0
      if i==0:
        training_data=np.vstack([example])
        training_y=np.vstack([temp])
      else:
        training_data=np.vstack([training_data,example])
        training_y=np.vstack([training_y,temp])

    coord.request_stop()
    coord.join(threads)
    #print(len(training_data[:,1]))
    #print(len(training_y[:,1]))

    return training_data,training_y

def splitDataset(nsample, testPerc, path):
    example,example_y= importDataset(path,nsample)
    #convert training_data and training_y in a list
    example_list   = example.tolist()
    example_y_list = example_y.tolist()
    training_data  = list()
    training_y     = list()
    percent = int((nsample*testPerc)/100)
    #begin uniform extraction from data.

    for i in range(percent):
      index = np.random.randint(0,len(example_list))
      training_data.append(example_list[index])
      training_y.append(example_y_list[index])
      example_list.remove(example_list[index])
      example_y_list.remove(example_y_list[index])

    training_data = np.matrix(training_data)
    training_y    = np.matrix(training_y)
    test_data     = np.matrix(example_list)
    test_y        = np.matrix(example_y_list) 

    #print(len(training_data[:,1]))
    #print(len(training_y[:,1]))
    #print(len(test_data[:,1]))
    #print(len(test_y[:,1]))
   # print(training_y[0])
   # print(type(training_data))
    return training_data,training_y,test_data,test_y


#---------------------------------------------INIZIO DEFINIZIONE MODELLO--------------------------------------

x = tf.placeholder(tf.float32, [None,4])
y = tf.placeholder(tf.float32, [None,3])
hidden_Layer1 ={'weights':tf.Variable(tf.truncated_normal([4,10],stddev=0.1)), 'biases':tf.Variable(tf.constant(0.1,shape=[10]))}
hidden_Layer2 ={'weights':tf.Variable(tf.truncated_normal([10,10],stddev=0.1)), 'biases':tf.Variable(tf.constant(0.1,shape=[10]))}
hidden_Layer3 ={'weights':tf.Variable(tf.truncated_normal([10,20],stddev=0.1)), 'biases':tf.Variable(tf.constant(0.1,shape=[20]))}
output_Layer  ={'weights':tf.Variable(tf.truncated_normal([20,3],stddev=0.1)), 'biases':tf.Variable(tf.constant(0.1,shape=[3]))}

#output layer #1
output_Layer1 = tf.add( tf.matmul(x,hidden_Layer1['weights']),hidden_Layer1['biases'])

output_Layer1 = tf.nn.sigmoid(output_Layer1)

#output layer #2
output_Layer2 = tf.add(tf.matmul(output_Layer1,hidden_Layer2['weights']),hidden_Layer2['biases'])
output_Layer2 = tf.nn.sigmoid(output_Layer2)


#output layer #3
output_Layer3 = tf.add(tf.matmul(output_Layer2,hidden_Layer3['weights']),hidden_Layer3['biases'])
output_Layer3 = tf.nn.sigmoid(output_Layer3)

#output layer #output
output_Layer_nosig = tf.add(tf.matmul(output_Layer3,output_Layer['weights']),output_Layer['biases'])
output_Layer_Output = tf.nn.sigmoid(output_Layer_nosig)



#--------------------------------------------FINE DEFINIZIONE MODELLO-------------------------------------------

#--------------------------------------------TRAINING DEL MODELLO-----------------------------------------------
error = tf.nn.l2_loss(output_Layer_Output-y, name="squared_error_cost")
#error=tf.nn.softmax_cross_entropy_with_logits(logits=output_Layer_nosig,labels=y)
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(error)





sess = tf.InteractiveSession()
tf.global_variables_initializer().run()


training_data,training_label,test_data,test_label = splitDataset(100,90,"iris1.csv")


for _ in range(2000):
    print(sess.run(error,feed_dict={x:training_data, y:training_label}))


correct_class = tf.equal(tf.argmax(output_Layer_Output,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_class, tf.float32))

print(sess.run([accuracy], feed_dict={x:test_data,y:test_label}))

---

很抱歉，我忘了在代码的这一部分插入train_步骤：

print(sess.run(error,feed_dict={x:training_data, y:training_label}))

现在，我试图设置超参数以获得良好的分类。谢谢你的回答