神经网络中的输入值误差

Question

我已经编写了一个多层神经网络，但我得到了一个错误，而我的尺寸输入到它。我得到一个值错误。你知道吗

代码如下：

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}
                                )

我得到以下错误：

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

我想我需要重塑。你知道吗

Answer 1

你是对的，你只需要重新塑造你的输入值，使它们与占位符的形状兼容。你知道吗

占位符的形状(?,41)表示任何批大小，有41个值。相反，您的输入的形状是41。你知道吗

很明显，缺少批处理维度。只需在输入中添加一个一维，您就可以：

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

请注意，可能还需要向batch_y变量添加1维。（与上述原因相同）

Answer 2

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/评估期间输入的数据可能更容易。我看不出您在哪里导入它，但是您需要重塑或交换轴，以便它具有格式（index，41）而不是（41，index）