Tensorflow 真实示例

本文介绍了Tensorflow 真实示例的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我已经根据这个

解决方案

它不可能是您的问题或错误的根源，但我认为所有出现的 row[:-2] 都应替换为 row[:-1]，如果你想获取除一个之外的所有索引(Python 不包括在 row[begin:end] 等范围内给出的索引 end)

你应该:

y_ = tf.placeholder(tf.float32,[None,numberOFClasses])...sess.run(train_step,feed_dict={x:batch_xs[i],y_:np.reshape(batch_ys[i],(batchSize, numberOFClasses))})...打印(sess.run(accuracy,feed_dict={x:batch_txs[i],y_:np.reshape(batch_tys[i],(batchSize, numberOFClasses))}))

无论如何，您应该明确地使用 batch_size != numberOFClasses，因为它会抛出一个错误，您可以使用它来了解代码中的错误.如果不这样做，您将丢失异常消息，但错误仍然存在，隐藏(您的网络仍然无法了解您想要的内容).当您看到导致问题的错误时，请尝试了解原因(查看形状是什么以及应该是什么)

I've a wrote a code based on this Tensorflow example . the issue that I'm having is that accuracy that I get doesn't make any sense ( it' either 1 or 0 ) so my question is what I'm missing here?

import tensorflow as tf
import  numpy as np
import  csv
import os


#defining  batch fuuntion

def batch(iterable, n=1):
    l = len(iterable)
    for ndx in range(0, l, n):
        yield iterable[ndx:min(ndx + n, l)]


os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
Training_File = 'Training.csv'
Test_File     = 'Test.csv'
numberOFClasses = 19
batchSize = 19

# read training data
filePointer  = open(Training_File, 'r', newline='')
reader = csv.reader(filePointer)
Training_Data   = []
Training_Labels = []
row = next(reader)
len(row)
#### Getting Training_Data and labels
for  row in reader:
    Training_Data.append(row[:-2])
    Training_Labels.append(row[-1])
# close TrainingFile  and  getting Data and labels from Test
len(Training_Data)
filePointer.close();

filePointer =open(Test_File, 'r', newline='')
reader  =   csv.reader(filePointer)
Test_Data = []
Test_Labels=[]
row = next(reader)

for row in reader:
    Test_Data.append(row[:-2])
    Test_Labels.append(row[-1])
len(Test_Labels)
filePointer.close()
len(Training_Data[0])



x = tf.placeholder('float',[None,len(row[:-2])])
w = tf.Variable(tf.zeros([len(row[:-2]),numberOFClasses]))
b = tf.Variable(tf.zeros([numberOFClasses]))
model = tf.add(tf.matmul(x,w),b)
y_ = tf.placeholder(tf.float32,[None,numberOFClasses])
y =  tf.nn.softmax(model)

cross_entropy= -tf.reduce_sum(y_*tf.log(y),reduction_indices=[1])
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
index =0
batch_xs = []
batch_ys = []
batch_txs= []
batch_tys= []
# Training processing


for i in batch(Training_Data,batchSize):
    batch_xs.append(i)
for i in batch(Training_Labels,batchSize):
    batch_ys.append(i)

for i in batch(Test_Data,batchSize):
    batch_txs.append(i)
for i in batch(Test_Labels,batchSize):
    batch_tys.append(i)


#print(np.reshape(batch_ys[len],(1,batchSize)))
for i in range(len(batch_xs) -1 ):
    sess.run(train_step,feed_dict={x:batch_xs[i],y_:np.reshape(batch_ys[i],(1,batchSize))})



correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
for i in range(len(batch_txs) -1):
    print(sess.run(accuracy,feed_dict={x:batch_txs[i],y_:np.reshape(batch_tys[i],(1,batchSize))}))

UPDATEI've changed the size of the batches:

.............................................
numberOFClasses = 19

batchSize = 19 * 3
....................................
for i in range(int(len(batch_xs)/batchSize) ):
    print(sess.run(train_step,feed_dict={x:batch_xs[i],y_:np.reshape(batch_ys[i],(batchSize,numberOFClasses))}))



correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
for i in range(len(batch_txs) -1):
    print(sess.run(accuracy,feed_dict={x:batch_txs[i],y_:np.reshape(batch_tys[i],(1,batchSize))}))

the result still the same, So I just don't get what I'm missing here

2ndUpdate

Running this part of the code : for j in range(len(batch_xs)-1): print(sess.run(train_step,feed_dict={x:batch_xs[j],y_:np.reshape(batch_ys[j],(numberOFClasses,3))}))

delivers a huge error message but I guess this part is relevant :

InvalidArgumentError (see above for traceback): Incompatible shapes: [19,3] vs. [57,19]
 [[Node: mul = Mul[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_1_0, Log)]]

So since my batch size is tree times the number of classes, I should get 57 predictions-> Y_ .

Shaping the feeding of Y_ [57,1]

for j in range(len(batch_xs)-1): print(sess.run(train_step,feed_dict={x:batch_xs[j],y_:np.reshape(batch_ys[j],(batchSize,1))}))

the print delivers None as return value but no error which is (I guess) ok.

But running the accuracy part delivers 1 and 0 as mentioned in the beginning.

the Test and Train Data and labels are 100% correct !

here is part of the end of CSV file :

解决方案

It can't be the source of your problem or errors, but I think all occurrences of row[:-2] should be replaced by row[:-1], if you want to take all indices but one (Python excludes the index end given in a range like row[begin:end])

You should have:

y_ = tf.placeholder(tf.float32,[None,numberOFClasses])
...
sess.run(train_step,feed_dict={x:batch_xs[i],y_:np.reshape(batch_ys[i],(batchSize, numberOFClasses ))})
...
print(sess.run(accuracy,feed_dict={x:batch_txs[i],y_:np.reshape(batch_tys[i],(batchSize, numberOFClasses ))}))

Anyway, you should definetly use batch_size != numberOFClasses, because it throws an error that you can use to understand what is wrong in your code. If you don't, you lose the exception message but the error is still there, hidden (you network still does not learn what you want). When you get the error look which reshapecauses a problem, and try to understand why (look what the shapes are and should be)

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