TensorFlow：shuffle_batch 没有显示任何错误但没有完成

Question

我正在尝试使用shuffle.batch 对从 .csv 文件加载的训练数据进行批处理。但是，当我运行代码时，它似乎不起作用。它没有显示任何错误，但没有完成。

那么，你能告诉我我的代码有什么问题吗？

此外，容量和min_after_dequeue 的合适值是多少？

import tensorflow as tf
import numpy as np


test_label = []
in_label = []

iris_TRAINING = "iris_training.csv"
iris_TEST = "iris_test.csv"

# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(filename=iris_TRAINING, target_dtype=np.int, features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(filename=iris_TEST, target_dtype=np.int, features_dtype=np.float32)

x_train, x_test, y_train, y_test = training_set.data, test_set.data, training_set.target, test_set.target



for n in y_train:
    targets = np.zeros(3) 
    targets[int(n)] = 1 #  one-hot pixs[0] is label and then use that number as index of one-hot
    in_label.append(targets)  #store all of label (one-hot) 
training_label = np.asarray(in_label)

for i in y_test:    
    test_targets = np.zeros(3) 
    test_targets[int(i)] = 1  # one-hot pixs[0] is label and then use that number as index of one-hot
    test_label.append(test_targets) 
test_label = np.asarray(test_label)


x = tf.placeholder(tf.float32, [None,4])   #generate placeholder to store value of features for training

W = tf.Variable(tf.zeros([4, 3])) #weight
b = tf.Variable(tf.zeros([3]))  #bias

y = tf.matmul(x, W) + b

y_ = tf.placeholder(tf.float32, [None, 3])  #generate placeholder to store value of labels


cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)


sess = tf.InteractiveSession()
# Train
tf.initialize_all_variables().run()

for i in range(5):
    batch_xt, batch_yt = tf.train.shuffle_batch([x_train,training_label],batch_size=10,capacity=200,min_after_dequeue=10)
    sess.run(train_step, feed_dict={x: batch_xt.eval(), y_: batch_yt.eval()})  
    print(i)

# Test trained model
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


print(sess.run(accuracy, feed_dict={x: x_test, y_: test_label}))

Answer 1

Shuffle_batch 构建：

1. 队列Q您的数据集将被排入哪个批次
2. 出列 Q 并获得批次的操作
3. 一个 QueueRunner 将 Q 加入队列

（详情请参阅here）

因此，您不需要在每次迭代时调用 Shuffle_batch，而只需在循环之前调用一次。之后您必须致电tf.train.start_queue_runners()。所以你的代码的结尾应该是这样的：

sess = tf.InteractiveSession()
# Train
tf.initialize_all_variables().run()
batch_xt, batch_yt = tf.train.shuffle_batch([x_train,training_label],batch_size=10,capacity=200,min_after_dequeue=10)
tf.train.start_queue_runners()

for i in range(5):
    sess.run(train_step, feed_dict={x: batch_xt.eval(), y_: batch_yt.eval()})  
    print(i)

# Test trained model
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


print(sess.run(accuracy, feed_dict={x: x_test, y_: test_label}))

容量和 min_after_dequeue 的合适值取决于您的可用内存和 I/O 吞吐量。容量限制了数据集在内存中所占的位置。它们只会影响计算时间，但不会影响最终结果（有关详细信息，请参阅 here）。