MaM*_*eak 5 queue image training-data tensorflow
我正在从convnetjs切换到tensorflow,并且正在努力获得读取图像和训练带有张量流的cnn的基础知识.
我在两个文件夹中有一堆160*120*1的图像:火车/去火车/没有所以我使用两个班级.
不知何故,我可以了解一下tf.train.slice_input_producer与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.
更新02-06-2016
我得到了一切与nessuno解决方案一起工作,培训和验证(下面的代码)Mrry表示管道更典型,不幸的是这个不起作用(下面的代码)没有给出错误,但计算成本保持不变,验证显示我网络没有改善.
我最好的猜测是,我将标签送入培训师的方式或我使用one_hot功能的方式出了问题.
验证部分似乎正在工作,当我感觉图像总是标签0精度变为100%,标签1精度0%和50/50它是50%.当然它可能是另一种方式,但由于成本在培训期间没有变化我认为在培训时出现问题
我知道,我现在使用的模型很简单但是对于调试它已经足够好了,工作版本可以在1500个图像中达到80%的准确度.
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 )
更新10-06-2016
我花了一段时间才意识到培训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.
image_batch并且label_batch是tf.Tensor对象.
背后的想法feed_dict是将图表外部的值传递到图表中.张量对象是图中的值.
因此,您必须在图形内部评估两个张量对象(以提取内容),然后使用计算值(现在是python值)提供图形.
所以,用eval值
imgs, lbls = image_batch.eval(), label_batch.eval()
或者(更好,因为它使用一个电话)
imgs, lbls = sess.run([image_batch, label_batch])
而不是提供图表,用返回的值替换占位符的内容:
sess.run(train_step, feed_dict={
x: imgs,
y_: lbls})
正如nessuno 指出的tf.train.batch()， -image_batch和-的结果label_batch是对象，因此您不能将它们用作输入子图tf.Tensor的值。
典型的使用方法tf.train.batch()是使用它来定义管道的输入（而不是使用tf.placeholder()forx和y_），以便批处理和预取将在 TensorFlow 图中处理。以下是程序第一部分的大致重构，该部分根据需要执行批处理：
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)