问题
I am using python 3.6 installed on mac os. I have text file that store name of image and the class number of every single image on.
#label.txt:
img0001.jpg 1
img0002.jpg 3
img0003.jpg 5
img0004.jpg 10
img0005.jpg 6
img0006.jpg 8
img0007.jpg 10
.....
I want to give them to my neural network in tensorflow as label of input data and give the image to network in same time like this
xs = tf.placeholder(tf.float32,[None,#size of my photo])
ys = tf.placeholder(tf.float32,[None,#size of my label if it is an array])
I cannot find any related documentation. could some one tell me what should i do for this pleased ?
回答1:
Assuming that you wanted to know, how to feed image and its respective label into neural network.
There are two things:
- Reading the images and converting those in numpy array.
- Feeding the same and its corresponding label into network.
As said by Thomas Pinetz , once you calculated names and labels. Create one hot encoding of labels.
from PIL import Image
number_of_batches = len(names)/ batch_size
for i in range(number_of_batches):
batch_x = names[i*batch_size:i*batch_size+batch_size]
batch_y = labels[i*batch_size:i*batch_size+batch_size]
batch_image_data = np.empty([batch_size, image_height, image_width, image_depth], dtype=np.int)
for ix in range(len(batch_x)):
f = batch_x[ix]
batch_image_data[ix] = np.array(Image.open(data_dir+f))
sess.run(train_op, feed_dict={xs:batch_image_data, ys:batch_y})
回答2:
You can use streight forward python i/o utilities (https://docs.python.org/3/tutorial/inputoutput.html) like:
names = []
labels = []
with open('label.txt', 'r') as f:
for line in f.readlines():
tokens = line.split(' ')
names.append(tokens[0])
labels.append(int(tokens[1]))
Then you can use the names array to load in the images and the labels as your y array.
来源:https://stackoverflow.com/questions/41612057/how-to-add-label-to-image-data-set-for-classification