Question

我编写了一个多层神经网络但我在将维度输入其中时遇到错误。我收到了价值错误。

以下是代码：

import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing


# In[207]:

df =pd.read_csv("train_data.csv")


# In[252]:

target = df["target"]
feat=df.drop(['target','connection_id'],axis=1)
target[189]


# In[209]:

len(feature.columns)



# In[210]:

logs_path="Server_attack"


# In[211]:

#Hyperparameters
batch_size=100
learning_rate=0.5
training_epochs=10


# In[244]:

X=tf.placeholder(tf.float32,[None,41])
Y_=tf.placeholder(tf.float32,[None,3])
lr=tf.placeholder(tf.float32)


# In[245]:

#5Layer Neural Network
L=200
M=100
N=60
O=30


# In[257]:

#Weights and Biases
W1=tf.Variable(tf.truncated_normal([41,L],stddev=0.1))
B1=tf.Variable(tf.ones([L]))
W2=tf.Variable(tf.truncated_normal([L,M],stddev=0.1))
B2=tf.Variable(tf.ones([M]))
W3=tf.Variable(tf.truncated_normal([M,N],stddev=0.1))
B3=tf.Variable(tf.ones([N]))
W4=tf.Variable(tf.truncated_normal([N,O],stddev=0.1))
B4=tf.Variable(tf.ones([O]))
W5=tf.Variable(tf.truncated_normal([O,3],stddev=0.1))
B5=tf.Variable(tf.ones([3]))               



# In[247]:

Y1=tf.nn.relu(tf.matmul(X,W1)+B1)
Y2=tf.nn.relu(tf.matmul(Y1,W2)+B2)
Y3=tf.nn.relu(tf.matmul(Y2,W3)+B3)
Y4=tf.nn.relu(tf.matmul(Y3,W4)+B4)
Ylogits=tf.nn.relu(tf.matmul(Y4,W5)+B5)
Y=tf.nn.softmax(Ylogits)


# In[216]:

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy)


# In[217]:

correct_prediction=tf.equal(tf.argmax(Y,1),tf.argmax(Y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))


# In[218]:

train_step=tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)


# In[219]:

#TensorBoard Parameters
tf.summary.scalar("cost",cross_entropy)
tf.summary.scalar("accuracy",accuracy)
summary_op=tf.summary.merge_all()


# In[220]:

init = tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)


# In[253]:

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())
    for epoch in range(training_epochs):
        batch_count=int(len(feature)/batch_size)
        for i in range(batch_count):


            batch_x,batch_y=feature.iloc[i, :].values.tolist(),target[i]

            _,summary = sess.run([train_step,summary_op],
                                 {X:batch_x,Y:batch_y,learning_rate:0.001}
                                )

我收到以下错误：

ValueError: Cannot feed value of shape (41,) for Tensor 'Placeholder_24:0', which has shape '(?, 41)'

我想重塑一下。

Answer 1

您的数据格式与定义为

的占位符不兼容

X=tf.placeholder(tf.float32,[None,41])

重新格式化您在培训/评估期间提供的数据可能更容易。我不知道你导入它的位置，但你需要重塑或交换轴，使其具有格式（索引，41）而不是（41，索引）

Answer 2

你是对的，你只需要重塑你的输入值，以使它们与占位符的形状兼容。

您的占位符的形状(?,41)表示任何批量大小，包含41个值。相反，您的输入的形状为41。

很明显，缺少批量维度。只需在输入中添加1维，就可以了：

batch_x = np.expand_dims(np.array(feature.iloc[i, :].values.tolist()), axis=0)

请注意，您可能还需要为batch_y变量添加1维。（出于同样的原因）

神经网络馈电时的数值误差

2 个答案: