閱讀tensorflow腳本的時候武氓，如何打印每一步數據是一個讓人頭疼的問題茎用。
在此記錄一下打印tf.train.slice_input_producer()輸出的步驟。
關鍵步驟在tf.session()里虹茶，在傳入路徑后要初始化coordinator, 啟動queue撮奏。這點和普通Variable只要run（）一下是不一樣的。
import tensorflow as tf
truth_filenames_np = ['dir/%d.jpg' % j for j in range(66)]
truth_filenames_tf = tf.convert_to_tensor(truth_filenames_np)
# get the labels
labels = [f.rsplit("/", 1)[1] for f in truth_filenames_np]
labels_tf = tf.convert_to_tensor(labels)
# My list is also already shuffled, so I set shuffle=False
truth_image_name, truth_label = tf.train.slice_input_producer(
[truth_filenames_tf, labels_tf], shuffle=False)
# # Another key step, where I batch them together
# truth_images_batch, truth_label_batch = tf.train.batch(
# [truth_image_name, truth_label], batch_size=11)
epochs = 7
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(epochs):
print("Epoch ", i)
X_truth_batch = truth_image_name.eval()
X_label_batch = truth_label.eval()
# Here I display all the images in this batch, and then I check
# which file numbers they actually are.
# BUT, the images that are displayed don't correspond with what is
# printed by X_label_batch!
print(X_truth_batch)
print(X_label_batch)
coord.request_stop()
coord.join(threads)
輸出如下：
Epoch 0
b'dir/0.jpg'
b'1.jpg'
Epoch 1
b'dir/2.jpg'
b'3.jpg'
Epoch 2
b'dir/4.jpg'
b'5.jpg'
Epoch 3
b'dir/6.jpg'
b'7.jpg'
Epoch 4
b'dir/8.jpg'
b'9.jpg'
Epoch 5
b'dir/10.jpg'
b'11.jpg'
Epoch 6
b'dir/12.jpg'
b'13.jpg'
