在Python中打印張量張量永遠掛起

Question

我想在一個簡單的程序中打印一個python張量。該程序使用張量流讀取器從文件讀取虹膜數據集。如果我取消註釋該程序的最後一行，它會永久掛起。目標是打印sepal_length，sepal_width等。我需要做什麼來打印sepal_length張量？

import tensorflow as tf 

def read_csv(batch_size, file_name, record_defaults): 
    filename_queue = tf.train.string_input_producer(["iris.data"]) 

    reader = tf.TextLineReader(skip_header_lines=1) 
    key, value = reader.read(filename_queue) 

    # decode_csv will convert a Tensor from type string (the text line) in 
    # a tuple of tensor columns with the specified defaults, which also 
    # sets the data type for each column 
    decoded = tf.decode_csv(value, record_defaults=record_defaults) 

    # batch actually reads the file and loads "batch_size" rows in a single tensor 
    return tf.train.shuffle_batch(decoded, 
            batch_size=batch_size, 
            capacity=batch_size * 50, 
            min_after_dequeue=batch_size) 

with tf.Session() as sess: 
    tf.global_variables_initializer().run() 

    sepal_length, sepal_width, petal_length, petal_width, label =\ 
     read_csv(100, "iris.data", [[0.0], [0.0], [0.0], [0.0], [""]]) 

    print("sepal_length:\n", sepal_length) 

    # uncommenting the following line makes the program hang forever 
    # print(sess.run(sepal_length)) 

這裏是iris.data輸入文件：

SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 
5.1,3.5,1.4,0.2,Iris-setosa 
4.9,3.0,1.4,0.2,Iris-setosa 
4.7,3.2,1.3,0.2,Iris-setosa 
4.6,3.1,1.5,0.2,Iris-setosa 
5.0,3.6,1.4,0.2,Iris-setosa 
5.4,3.9,1.7,0.4,Iris-setosa 
4.6,3.4,1.4,0.3,Iris-setosa 
5.0,3.4,1.5,0.2,Iris-setosa 
4.4,2.9,1.4,0.2,Iris-setosa 
4.9,3.1,1.5,0.1,Iris-setosa 
5.4,3.7,1.5,0.2,Iris-setosa 
4.8,3.4,1.6,0.2,Iris-setosa 
4.8,3.0,1.4,0.1,Iris-setosa 
4.3,3.0,1.1,0.1,Iris-setosa 
5.8,4.0,1.2,0.2,Iris-setosa 
5.7,4.4,1.5,0.4,Iris-setosa 
5.4,3.9,1.3,0.4,Iris-setosa 
5.1,3.5,1.4,0.3,Iris-setosa 
5.7,3.8,1.7,0.3,Iris-setosa 
5.1,3.8,1.5,0.3,Iris-setosa 
5.4,3.4,1.7,0.2,Iris-setosa 
5.1,3.7,1.5,0.4,Iris-setosa 
4.6,3.6,1.0,0.2,Iris-setosa 
5.1,3.3,1.7,0.5,Iris-setosa 
4.8,3.4,1.9,0.2,Iris-setosa 
5.0,3.0,1.6,0.2,Iris-setosa 
5.0,3.4,1.6,0.4,Iris-setosa 
5.2,3.5,1.5,0.2,Iris-setosa 
5.2,3.4,1.4,0.2,Iris-setosa 
4.7,3.2,1.6,0.2,Iris-setosa 
4.8,3.1,1.6,0.2,Iris-setosa 
5.4,3.4,1.5,0.4,Iris-setosa 
5.2,4.1,1.5,0.1,Iris-setosa 
5.5,4.2,1.4,0.2,Iris-setosa 
4.9,3.1,1.5,0.1,Iris-setosa 
5.0,3.2,1.2,0.2,Iris-setosa 
5.5,3.5,1.3,0.2,Iris-setosa 
4.9,3.1,1.5,0.1,Iris-setosa 
4.4,3.0,1.3,0.2,Iris-setosa 
5.1,3.4,1.5,0.2,Iris-setosa 
5.0,3.5,1.3,0.3,Iris-setosa 
4.5,2.3,1.3,0.3,Iris-setosa 
4.4,3.2,1.3,0.2,Iris-setosa 
5.0,3.5,1.6,0.6,Iris-setosa 
5.1,3.8,1.9,0.4,Iris-setosa 
4.8,3.0,1.4,0.3,Iris-setosa 
5.1,3.8,1.6,0.2,Iris-setosa 
4.6,3.2,1.4,0.2,Iris-setosa 
5.3,3.7,1.5,0.2,Iris-setosa 
5.0,3.3,1.4,0.2,Iris-setosa 
7.0,3.2,4.7,1.4,Iris-versicolor 
6.4,3.2,4.5,1.5,Iris-versicolor 
6.9,3.1,4.9,1.5,Iris-versicolor 
5.5,2.3,4.0,1.3,Iris-versicolor 
6.5,2.8,4.6,1.5,Iris-versicolor 
5.7,2.8,4.5,1.3,Iris-versicolor 
6.3,3.3,4.7,1.6,Iris-versicolor 
4.9,2.4,3.3,1.0,Iris-versicolor 
6.6,2.9,4.6,1.3,Iris-versicolor 
5.2,2.7,3.9,1.4,Iris-versicolor 
5.0,2.0,3.5,1.0,Iris-versicolor 
5.9,3.0,4.2,1.5,Iris-versicolor 
6.0,2.2,4.0,1.0,Iris-versicolor 
6.1,2.9,4.7,1.4,Iris-versicolor 
5.6,2.9,3.6,1.3,Iris-versicolor 
6.7,3.1,4.4,1.4,Iris-versicolor 
5.6,3.0,4.5,1.5,Iris-versicolor 
5.8,2.7,4.1,1.0,Iris-versicolor 
6.2,2.2,4.5,1.5,Iris-versicolor 
5.6,2.5,3.9,1.1,Iris-versicolor 
5.9,3.2,4.8,1.8,Iris-versicolor 
6.1,2.8,4.0,1.3,Iris-versicolor 
6.3,2.5,4.9,1.5,Iris-versicolor 
6.1,2.8,4.7,1.2,Iris-versicolor 
6.4,2.9,4.3,1.3,Iris-versicolor 
6.6,3.0,4.4,1.4,Iris-versicolor 
6.8,2.8,4.8,1.4,Iris-versicolor 
6.7,3.0,5.0,1.7,Iris-versicolor 
6.0,2.9,4.5,1.5,Iris-versicolor 
5.7,2.6,3.5,1.0,Iris-versicolor 
5.5,2.4,3.8,1.1,Iris-versicolor 
5.5,2.4,3.7,1.0,Iris-versicolor 
5.8,2.7,3.9,1.2,Iris-versicolor 
6.0,2.7,5.1,1.6,Iris-versicolor 
5.4,3.0,4.5,1.5,Iris-versicolor 
6.0,3.4,4.5,1.6,Iris-versicolor 
6.7,3.1,4.7,1.5,Iris-versicolor 
6.3,2.3,4.4,1.3,Iris-versicolor 
5.6,3.0,4.1,1.3,Iris-versicolor 
5.5,2.5,4.0,1.3,Iris-versicolor 
5.5,2.6,4.4,1.2,Iris-versicolor 
6.1,3.0,4.6,1.4,Iris-versicolor 
5.8,2.6,4.0,1.2,Iris-versicolor 
5.0,2.3,3.3,1.0,Iris-versicolor 
5.6,2.7,4.2,1.3,Iris-versicolor 
5.7,3.0,4.2,1.2,Iris-versicolor 
5.7,2.9,4.2,1.3,Iris-versicolor 
6.2,2.9,4.3,1.3,Iris-versicolor 
5.1,2.5,3.0,1.1,Iris-versicolor 
5.7,2.8,4.1,1.3,Iris-versicolor 
6.3,3.3,6.0,2.5,Iris-virginica 
5.8,2.7,5.1,1.9,Iris-virginica 
7.1,3.0,5.9,2.1,Iris-virginica 
6.3,2.9,5.6,1.8,Iris-virginica 
6.5,3.0,5.8,2.2,Iris-virginica 
7.6,3.0,6.6,2.1,Iris-virginica 
4.9,2.5,4.5,1.7,Iris-virginica 
7.3,2.9,6.3,1.8,Iris-virginica 
6.7,2.5,5.8,1.8,Iris-virginica 
7.2,3.6,6.1,2.5,Iris-virginica 
6.5,3.2,5.1,2.0,Iris-virginica 
6.4,2.7,5.3,1.9,Iris-virginica 
6.8,3.0,5.5,2.1,Iris-virginica 
5.7,2.5,5.0,2.0,Iris-virginica 
5.8,2.8,5.1,2.4,Iris-virginica 
6.4,3.2,5.3,2.3,Iris-virginica 
6.5,3.0,5.5,1.8,Iris-virginica 
7.7,3.8,6.7,2.2,Iris-virginica 
7.7,2.6,6.9,2.3,Iris-virginica 
6.0,2.2,5.0,1.5,Iris-virginica 
6.9,3.2,5.7,2.3,Iris-virginica 
5.6,2.8,4.9,2.0,Iris-virginica 
7.7,2.8,6.7,2.0,Iris-virginica 
6.3,2.7,4.9,1.8,Iris-virginica 
6.7,3.3,5.7,2.1,Iris-virginica 
7.2,3.2,6.0,1.8,Iris-virginica 
6.2,2.8,4.8,1.8,Iris-virginica 
6.1,3.0,4.9,1.8,Iris-virginica 
6.4,2.8,5.6,2.1,Iris-virginica 
7.2,3.0,5.8,1.6,Iris-virginica 
7.4,2.8,6.1,1.9,Iris-virginica 
7.9,3.8,6.4,2.0,Iris-virginica 
6.4,2.8,5.6,2.2,Iris-virginica 
6.3,2.8,5.1,1.5,Iris-virginica 
6.1,2.6,5.6,1.4,Iris-virginica 
7.7,3.0,6.1,2.3,Iris-virginica 
6.3,3.4,5.6,2.4,Iris-virginica 
6.4,3.1,5.5,1.8,Iris-virginica 
6.0,3.0,4.8,1.8,Iris-virginica 
6.9,3.1,5.4,2.1,Iris-virginica 
6.7,3.1,5.6,2.4,Iris-virginica 
6.9,3.1,5.1,2.3,Iris-virginica 
5.8,2.7,5.1,1.9,Iris-virginica 
6.8,3.2,5.9,2.3,Iris-virginica 
6.7,3.3,5.7,2.5,Iris-virginica 
6.7,3.0,5.2,2.3,Iris-virginica 
6.3,2.5,5.0,1.9,Iris-virginica 
6.5,3.0,5.2,2.0,Iris-virginica 
6.2,3.4,5.4,2.3,Iris-virginica 
5.9,3.0,5.1,1.8,Iris-virginica 

Answer 1

我想通了，這裏的工作程序：

import tensorflow as tf 

def read_csv(batch_size, file_name, record_defaults): 
    filename_queue = tf.train.string_input_producer(["iris.data"]) 

    reader = tf.TextLineReader(skip_header_lines=1) 
    key, value = reader.read(filename_queue) 

    # decode_csv will convert a Tensor from type string (the text line) in 
    # a tuple of tensor columns with the specified defaults, which also 
    # sets the data type for each column 
    decoded = tf.decode_csv(value, record_defaults=record_defaults) 

    # batch actually reads the file and loads "batch_size" rows in a single tensor 
    return tf.train.shuffle_batch(decoded, 
            batch_size=batch_size, 
            capacity=batch_size * 50, 
            min_after_dequeue=batch_size) 

with tf.Session() as sess: 
    tf.global_variables_initializer().run() 

    sepal_length, sepal_width, petal_length, petal_width, label =\ 
     read_csv(100, "iris.data", [[0.0], [0.0], [0.0], [0.0], [""]]) 

    # adding these 2 lines fixed the hang forever problem 
    coord = tf.train.Coordinator() 
    threads = tf.train.start_queue_runners(sess=sess, coord=coord) 

    print("sepal_length:\n", sepal_length) 

    # uncommenting the following line used to make the program hang forever 
    print(sess.run(sepal_length))