馈入神经网络时出现值错误 [英] Value Error while feeding in Neural Network

问题描述

我已经编写了一个多层神经网络，但是在向其输入尺寸时遇到错误.我遇到了价值错误.

I have programmed a multi-layer neural network but I'm getting an error while feeding my dimension into it. I'm getting a Value Error.

这是代码:

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}
                                )

我遇到以下错误:

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

我想我需要重塑.

推荐答案

您是对的，您只需要重塑输入值即可使其与占位符的形状兼容.

You're right, you just have to reshape your input values in order to make them compatible with the placeholder's shape.

您的占位符的形状为(?,41)，表示任何批量大小，具有41个值.您输入的形状为41.

Your placeholder has shape (?,41) that means any batch size, with 41 values. Your input is, instead, with a shape of 41.

很明显，批次尺寸缺失.只需在输入中添加1维，就可以了:

It's clear that the batch dimension is missing. Just add a 1 dimension to your input and you'll be fine:

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

请注意，您可能还必须在batch_y变量中添加1维. (出于上述相同的原因)

Note that probably you have to add a 1 dimension to your batch_y variable too. (for the same reason described above)

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