TensorFlow：shuffle_batch沒有顯示任何錯誤，但沒有完成

Question

我試圖使用shuffle.batch批處理從.csv文件加載的訓練數據。但是，當我運行代碼時，它似乎不起作用。它沒有顯示任何錯誤，但沒有完成。

那麼，你能告訴我我的代碼有什麼問題嗎？

此外，什麼是適合的容量值和min_after_dequeue？

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編譯：

1. 隊列Q成批量數據集將是排隊
2. 的操作中退出Q並獲得了一批
3. 一個QueueRunner入隊Q

（見here瞭解詳細信息）

所以你不需要調用Shuffle_batch在每次迭代，但只有一個你的循環之前的時間。之後你必須撥打tf.train.start_queue_runners()。所以，你的代碼的末尾應該是這樣的：

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})) 

能力的合適的含義和min_after_dequeue取決於你的可用內存和I/O吞吐量。容量限制了記錄數據集的地點。它們可能會影響計算時間，但不會影響最終結果（有關更多詳細信息，請參閱here）。