是的，我搜索过SO，Reddit，GitHub，Google Plus等等。我在Windows 10 64位上使用TensorFlow运行Python 3。我的目标是阅读一堆图像并为其分配标签以进行培训。
我试图将我的标签列表转换为可用的＆＃34;对象＆＃34;为sess.run(train_step, feed_dict={imgs:batchX,lbls:batchY})。我的图像导入正常，因为在此之前我调用函数来创建批处理（下面的代码）。在函数中，我可以成功创建numpy数组图像。但是，我不知道从哪里开始分配我的标签。
我的labels.txt文件格式为
data/cats/cat (1) copy.png,1
data/cats/cat (2) copy.png,1
data/cats/cat (3) copy.png,1
and so on for about 300 lines
data/cats/cat (x) copy.png是文件，1是Class（在本例中是Cat）。该文件被读入一个名为labels_list的常规数组（或列表？），每行都是数组中的新元素。当我打印labels_list时，会显示
['data/cats/cat (1) copy.png,1' 'data/cats/cat (2) copy.png,1'
'data/cats/cat (3) copy.png,1' 'data/cats/cat (4) copy.png,1'
'data/cats/cat (5) copy.png,1' 'data/cats/cat (6) copy.png,1'
(alot more lines of this)
'data/cats/cat (295) copy.png,1' 'data/cats/cat (296) copy.png,1'
'data/cats/cat (297) copy.png,1' 'data/cats/cat (298) copy.png,1']
我不知道如何为我的train_step制作一个可用的numpy数组（下面的代码）。我试过谷歌搜索，但大多数解决方案只使用带整数的标签列表，但我需要使用文件的路径。
任何帮助表示感谢，谢谢：）
代码:(和我的GitHub github.com/supamonkey2000/jm-uofa）
import tensorflow as tf
import numpy as np
import os
import sys
import cv2
content = [] # Where images are stored
labels_list = [] # Stores the image labels, still not 100% working
########## File opening function
with open("data/cats/files.txt") as ff:
for line in ff:
line = line.rstrip()
content.append(line)
#################################
########## Labels opening function
with open("data/cats/labels.txt") as fff:
for linee in fff:
linee = linee.rstrip()
labels_list.append(linee)
labels_list = np.array(labels_list)
###############################
############ Function used to create batches for training
def create_batches(batch_size):
images1 = [] # Array to hold images within the function
for img1 in content: # Read the images from content[] in a loop
thedata = cv2.imread(img1) # Load the image
thedata = thedata.flatten() # Convert the image to a usable numpy array
images1.append(thedata) # Append the image to the images1 array
images1 = np.array(images1) # Convert images1[] to numpy array
print(labels_list) # Debugging purposes
while(True):
for i in range(0,298,10):
yield(images1[i:i+batch_size],labels_list[i:i+batch_size])
#########################################################
imgs = tf.placeholder(dtype=tf.float32,shape=[None,786432]) # Images placeholder
lbls = tf.placeholder(dtype=tf.float32,shape=[None,10]) # Labels placeholder
W = tf.Variable(tf.zeros([786432,10])) # Weights
b = tf.Variable(tf.zeros([10])) # Biases
y_ = tf.nn.softmax(tf.matmul(imgs,W) + b) # Something complicated
cross_entropy = tf.reduce_mean(-tf.reduce_sum(lbls * tf.log(y_),reduction_indices=[1])) # Cool spacey sounding thing that does cool stuff
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy) # When this is called use the GDO to train the model
sess = tf.InteractiveSession() # Setup the session
tf.global_variables_initializer().run() # Initialize the variables
############################## Training steps for teaching the model
for i in range(10000): # Run for 10,000 steps
for (batchX,batchY) in create_batches(10): # Call a batch to be used
sess.run(train_step, feed_dict={imgs:batchX, lbls: batchY}) # Train the model with the batch (THIS IS GIVING ME TONS OF ISSUES)
###################################################################
correct_prediction = tf.equal(tf.argmax(y_,1),tf.argmax(lbls,1)) # Find out if the program tested properly (I think?)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # Find the accuracy of the model
print(sess.run(accuracy, feed_dict={imgs:content, lbls:labels_list})) # Print the accuracy of the model !!! imgs:content may be incorrect, must look into it
答案 0 :(得分：1)
据我所知，你有2个档案 - data / cats / files.txt其中包含文件的URL - data / cats / labels.txt其中还包含文件的URL和相应的标签。
我建议只使用标签文件，因为网址和标签在这里链接。
现在继续贴标签。
当您阅读labels.txt文件时，已经创建了输出标签。我在代码中添加了一些注释。
import re
import numpy
import cv2
label_map = [] # the map that holds the link between the label and the one_hot_encoded label
file_info = [] # holds all your file locations and the label of the file
#read all the lines. A line should look like this: mycatimage.png,1
with open('labels.txt') as f:
rows = [re.split(",", line.rstrip("\n")) for line in f]
for row in rows:
file_info.append(row)
label_column = [line[1] for line in rows] # line[1] is based on your 'data/cats/cat (1) copy.png,1' example where the label is the second index
unique_labels = list(set(label_column)) # set gives unique values
# now the onehot encoding of the labels which basically means everything 0 except 1
for label in unique_labels:
output_values = np.zeros(len(unique_labels), dtype=np.int)
output_values [unique_labels.index(label)] = 1
label_map.append({'name': label , 'value': output_values })
# Write the found labels to the label file if you want for later use. We will use the label_map variable for now
with open("mylabelfile.txt", 'w+') as lf:
for label in label_map:
lf.write(label['name'] + ',' + ','.join(str(x) for x in label['value']) + "\n") # writes --> Christina,0,0,1,0\n
现在进入批处理功能：）
def get_one_hot_encoded_array_for_label(label):
for existing in label_map:
if existing['name'] == label:
return existing['value']
def create_batches(batch_size):
images1 = [] # Array to hold images within the function
labels_list = []
for img1 in file_info: # Read the images from file_info[] in a loop
image_location = img1[0]
image_label = img1[1]
thedata = cv2.imread(image_location ) # Load the image
thedata = thedata.flatten() # Convert the image to a usable numpy array
images1.append(thedata) # Append the image to the images1 array
outputvalues = get_one_hot_encoded_array_for_label(image_label )
labels_list.append(outputvalues) # where we fill the labels list with the one hot encoded values.
images1 = np.array(images1) # Convert images1[] to numpy array
labels_list = np.array(labels_list)
print(labels_list) # Debugging purposes
while(True):
for i in range(0,298,10):
yield(images1[i:i+batch_size],labels_list[i:i+batch_size])
这应该提供一个热门编码值batchY。基于我自己的网络写了这个，但没有用图像数据测试它。你能确认它是否有效或告诉它在哪里断裂？如果有什么不清楚请问：）