Tensorflow，train_step feed 不正确 [英] Tensorflow, train_step feed incorrect
本文介绍了Tensorflow，train_step feed 不正确的处理方法，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
我正在从 convnetjs 切换到 tensorflow，并且正在努力学习阅读图像和使用 tensorflow 训练 cnn 的基础知识.
I am switching from convnetjs to tensorflow and am tying to get the basics of reading images and training a cnn with tensorflow.
我在两个文件夹中有一堆 160*120*1 的图像:train/go 和 train/no，所以我使用两个类.
i have a bunch of images 160*120*1 in two folders: train/go and train/no so i use two classes.
不知何故，我可以了解 tf.train.slice_input_producer 和 sess.run(train_step.
somehow i can get my head around how the connection between a tf.train.slice_input_producer and the sess.run(train_step.
我的代码:
import tensorflow as tf
def read_my_list( minId, maxId ):
""" create list with train/no and train/go from 1 to maxid
max maxId = 50000
"""
filenames = []
labels = []
for num in range( minId, maxId ):
filenames.append( "/media/boss/tensor/train/go/" + str( num ) + ".jpg" )
labels.append( int( 1 ) )
filenames.append( "/media/boss/tensor/train/no/" + no_go_name( num ) + ".jpg" )
labels.append( int( 0 ) )
# return list with all filenames
return filenames, labels
def no_go_name( id ):
# create string where id = 5 becomes 00005
ret = str( id )
while ( len( ret ) < 5 ):
ret = "0" + ret;
return ret;
def read_images_from_disk(input_queue):
"""Consumes a single filename and label as a ' '-delimited string.
Args:
filename_and_label_tensor: A scalar string tensor.
Returns:
Two tensors: the decoded image, and the string label.
"""
label = input_queue[1]
print( "read file " )
file_contents = tf.read_file(input_queue[0])
example = tf.image.decode_jpeg(file_contents, channels=1)
# do i need to set shape??????????
example.set_shape([160, 120, 1])
print( "file read " )
return example, label
# some stuff to create a cnn etc
x = tf.placeholder(tf.float32, [None, 19200])
W = tf.Variable(tf.zeros([19200, 2]))
b = tf.Variable(tf.zeros([2]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 2])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
# get filelist and labels
image_list, label_list = read_my_list( 1, 10 )
# conver to tensors for input_queue
images = tf.convert_to_tensor(image_list, dtype=tf.string)
labels = tf.convert_to_tensor(label_list, dtype=tf.int32)
# Makes an input queue
input_queue = tf.train.slice_input_producer([images, labels],
num_epochs=10,
shuffle=True)
image, label = read_images_from_disk(input_queue)
for i in range(100):
print( i )
image_batch, label_batch = tf.train.batch([image, label],
batch_size=2)
#gives error see below
sess.run(train_step, feed_dict={x: image_batch, y_: label_batch})
# test accuracy, unsure if something is wrong here
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
batch_xs, batch_ys = tf.train.batch([image, label],
batch_size=10)
print(sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys}))
以下行给出错误:
sess.run(train_step, feed_dict={x: image_batch, y_: label_batch})
这是错误:
Traceback (most recent call last):
File "detectGoNo.py", line 95, in <module>
sess.run(train_step, feed_dict={x: image_batch, y_: label_batch})
File "/home/boss/anaconda2/envs/tensor2/lib/python2.7/site-
packages/tensorflow/python/client/session.py", line 340, in run
run_metadata_ptr)
File "/home/boss/anaconda2/envs/tensor2/lib/python2.7/site-
packages/tensorflow/python/client/session.py", line 545, in _run
raise TypeError('The value of a feed cannot be a tf.Tensor object. '
TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable
feed values include Python scalars, strings, lists, or numpy ndarrays.
2016 年 2 月 6 日更新
我得到了使用 nessuno、培训和验证解决方案所需的一切(代码如下)Mrry 表示管道更典型，不幸的是这个管道不起作用(下面的代码)没有给出错误，但计算出的成本保持不变，验证表明网络没有改善.
I got everything to work with the solution from nessuno, training and validation (Code below) Mrry indicated a pipeline is more typical unfortunately this one does not work ( code below ) no errors are given but calculated cost remains the same and validation shows me that the network is not improving.
我最好的猜测是我将标签提供给训练器的方式或我使用 one_hot 函数的方式有问题.
My best guess is that something is wrong with the way i feed the labels to the trainer or the way i use the one_hot function.
验证部分似乎正在工作，当我觉得标签 0 准确度始终变为 100%、标签 1 准确度为 0% 和 50/50 的图像为 50% 时.当然也可以反过来，但由于培训时成本没有变化，我认为培训时出了点问题
The validation part seems to be working, when i feel images with always the label 0 accuracy becomes 100%, labels 1 accuracy 0% and 50/50 it is 50%. of course it could be the other way around but since the cost is not changing while training i think something goes wrong while training
我知道，我现在使用的模型很简单，但对于调试来说已经足够好了，工作版本设法在 1500 张图像中达到 80% 的准确度.
i know, the model i use right now is to simple but for debugging it is good enough, the working verion manages to get to 80% accuracy within 1500 images.
label = tf.cast( label, tf.int64 )
label = tf.one_hot( label, 2, 0, 1 )
label = tf.cast( label, tf.float32 )
我的代码:(工作)
import tensorflow as tf
import numpy as np
import math
IMAGE_WIDTH = 160
IMAGE_HEIGHT = 120
IMAGE_DEPTH = 1
IMAGE_PIXELS = IMAGE_WIDTH * IMAGE_HEIGHT
NUM_CLASSES = 2
STEPS = 50000
STEP_PRINT = 100
STEP_VALIDATE = 100
LEARN_RATE = 0.0014
DECAY_RATE = 0.4
BATCH_SIZE = 5
def read_my_list( minId, maxId, folder ):
""" create list with train/no and train/go from 1 to maxid
max maxId = 50000
"""
filenames = []
labels = []
#labels = np.zeros( ( ( maxId - minId ) * 2, 2 ) )
for num in range( minId, maxId ):
filenames.append( "/media/boss/2C260F93260F5CE8/tensor/" + folder + "/go/" + str( num ) + ".jpg" )
#labels[ ( num - minId ) * 2 ][ 1 ] = 1
labels.append( int( 1 ) )
filenames.append( "/media/boss/2C260F93260F5CE8/tensor/" + folder + "/no/" + no_go_name( num ) + ".jpg" )
#labels[ ( ( num - minId ) * 2 ) + 1 ][ 0 ] = 1
labels.append( int( 0 ) )
# return list with all filenames
print( "label: " + str( len( labels ) ) )
print( "image: " + str( len( filenames ) ) )
return filenames, labels
def no_go_name( id ):
# create string where id = 5 becomes 00005
ret = str( id )
while ( len( ret ) < 5 ):
ret = "0" + ret;
return ret;
# Create model
def conv_net(x):
img_width = IMAGE_WIDTH
img_height = IMAGE_HEIGHT
img_depth = IMAGE_DEPTH
weights = tf.Variable( tf.random_normal( [ img_width * img_height * img_depth, NUM_CLASSES ] ) )
biases = tf.Variable( tf.random_normal( [ NUM_CLASSES ] ) )
# softmax layer
out = tf.add( tf.matmul( x, weights ), biases )
return out
def read_images_from_disk(input_queue):
"""Consumes a single filename and label as a ' '-delimited string.
Args:
filename_and_label_tensor: A scalar string tensor.
Returns:
Two tensors: the decoded image, and the string label.
"""
label = input_queue[1]
print( "read file " )
file_contents = tf.read_file(input_queue[0])
example = tf.image.decode_jpeg( file_contents, channels = 1 )
example = tf.reshape( example, [ IMAGE_PIXELS ] )
example.set_shape( [ IMAGE_PIXELS ] )
example = tf.cast( example, tf.float32 )
example = tf.cast( example, tf.float32 ) * ( 1. / 255 ) - 0.5
label = tf.cast( label, tf.int64 )
label = tf.one_hot( label, 2, 0, 1 )
label = tf.cast( label, tf.float32 )
print( "file read " )
return example, label
with tf.Session() as sess:
########################################
# get filelist and labels for training
image_list, label_list = read_my_list( 501, 50000, "train" )
# create queue for training
input_queue = tf.train.slice_input_producer( [ image_list, label_list ],
num_epochs = 100,
shuffle = True )
# read files for training
image, label = read_images_from_disk( input_queue )
# `image_batch` and `label_batch` represent the "next" batch
# read from the input queue.
image_batch, label_batch = tf.train.batch( [ image, label ], batch_size = BATCH_SIZE )
# input output placeholders
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES])
# create the network
y = conv_net( x )
# loss
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( y, y_) )
learning_rate = tf.placeholder(tf.float32, shape=[])
# train step
train_step = tf.train.AdamOptimizer( 1e-3 ).minimize( cost )
########################################
# get filelist and labels for validation
image_list_test, label_list_test = read_my_list( 1, 500, "validation" )
# create queue for validation
input_queue_test = tf.train.slice_input_producer( [ image_list_test, label_list_test ],
shuffle=True )
# read files for validation
image_test, label_test = read_images_from_disk( input_queue_test )
# `image_batch_test` and `label_batch_test` represent the "next" batch
# read from the input queue test.
image_batch_test, label_batch_test = tf.train.batch( [ image_test, label_test ], batch_size=200 )
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.initialize_all_variables()
sess.run(init)
# N.B. You must run this function before `sess.run(train_step)` to
# start the input pipeline.
#tf.train.start_queue_runners(sess)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(STEPS):
# No need to feed, because `x` and `y_` are already bound to
# the next input batch.
if i % STEP_PRINT == 0:
LEARN_RATE = LEARN_RATE * DECAY_RATE
print( str( i ) + " " + str( LEARN_RATE ) )
if i % STEP_VALIDATE == 0:
imgs, lbls = sess.run([image_batch_test, label_batch_test])
print(sess.run(accuracy, feed_dict={
x: imgs,
y_: lbls}))
imgs, lbls = sess.run([image_batch, label_batch])
sess.run(train_step, feed_dict={
x: imgs,
y_: lbls})
# ,learning_rate:LEARN_RATE})
imgs, lbls = sess.run([image_batch_test, label_batch_test])
print(sess.run(accuracy, feed_dict={
x: imgs,
y_: lbls}))
coord.request_stop()
coord.join(threads)
我的代码:(不工作)
with tf.Session() as sess:
########################################
# get filelist and labels for training
image_list, label_list = read_my_list( 501, 50000, "train" )
# create queue for training
input_queue = tf.train.slice_input_producer( [ image_list, label_list ],
num_epochs = 100,
shuffle = True )
# read files for training
image, label = read_images_from_disk( input_queue )
# `image_batch` and `label_batch` represent the "next" batch
# read from the input queue.
image_batch, label_batch = tf.train.batch( [ image, label ], batch_size = BATCH_SIZE )
x = image_batch
y_ = label_batch
# create the network
y = conv_net( x )
# loss
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( y, y_) )
# train step
train_step = tf.train.AdamOptimizer( 1e-3 ).minimize( cost )
########################################
# get filelist and labels for validation
image_list_test, label_list_test = read_my_list( 1, 500, "validation" )
# create queue for validation
input_queue_test = tf.train.slice_input_producer( [ image_list_test, label_list_test ],
shuffle=True )
# read files for validation
image_test, label_test = read_images_from_disk( input_queue_test )
# `image_batch_test` and `label_batch_test` represent the "next" batch
# read from the input queue test.
image_batch_test, label_batch_test = tf.train.batch( [ image_test, label_test ], batch_size=200 )
xval = image_batch_test
yval_ = label_batch_test
# network for validation
yval = conv_net( xval )
# validate network
correct_prediction = tf.equal( tf.argmax( yval, 1 ), tf.argmax( yval_, 1 ) )
# calculate accuracy
accuracy = tf.reduce_mean( tf.cast( correct_prediction, tf.float32 ) )
# init all variables
init = tf.initialize_all_variables()
sess.run( init )
# N.B. You must run this function before `sess.run(train_step)` to
# start the input pipeline.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners( coord = coord )
for i in range(STEPS):
# No need to feed, because `x` and `y_` are already bound to
# the next input batch.
if i % STEP_PRINT == 0:
print( i )
# validate accuracy
if i % STEP_VALIDATE == 0:
print( sess.run( accuracy ) )
# train one step
sess.run( train_step )
# validate accuracy
print( sess.run( accuracy ) )
coord.request_stop()
coord.join( threads )
2016 年 10 月 6 日更新
我花了一段时间才意识到训练管道和验证管道不具有相同的权重和偏差.
现在我训练、保存模型并在单独的脚本中加载模型，就像一个魅力.
UPDATE 10-06-2016
It took me a while to realize that a training pipeline and a validating pipeline do not share the same weights and biases.
right now i train, save the model and load the model in a separate script, works like a charm.
推荐答案
正如nessuno指出，tf.train.batch()—image_batch 和 label_batch—是 tf.Tensor 对象，因此您不能将它们用作喂到子图中.
As nessuno points out, the results of tf.train.batch()—image_batch and label_batch—are tf.Tensor objects, so you can't use these as a value to feed into a subgraph.
使用 tf.train.batch() 的典型方式是使用它来定义管道的输入(而不是使用 tf.placeholder()x 和 y_)，以便在 TensorFlow 图中处理批处理和预取.以下是根据需要执行批处理的程序第一部分的大致重组:
The typical way to use tf.train.batch() is to use it to define the inputs to a pipeline (rather than using tf.placeholder() for x and y_), so that batching and prefetching will be handled inside the TensorFlow graph. Here's an approximate restructuring of the first part of your program that performs batching as desired:
with tf.Session() as sess:
# get filelist and labels
image_list, label_list = read_my_list( 1, 10 )
input_queue = tf.train.slice_input_producer([image_list, label_list],
num_epochs=10,
shuffle=True)
image, label = read_images_from_disk(input_queue)
# `image_batch` and `label_batch` represent the "next" batch
# read from the input queue.
image_batch, label_batch = tf.train.batch([image, label], batch_size=2)
x = image_batch
y_ = label_batch
# Define your model in terms of `x` and `y_` here....
train_step = ...
# N.B. You must run this function after creating your graph.
init = tf.initialize_all_variables()
sess.run(init)
# N.B. You must run this function before `sess.run(train_step)` to
# start the input pipeline.
tf.train.start_queue_runners(sess)
for i in range(100):
# No need to feed, because `x` and `y_` are already bound to
# the next input batch.
sess.run(train_step)
这篇关于Tensorflow，train_step feed 不正确的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
