Question

我正在尝试一本书的简单例子，其中我有892行的火车数据样本，这是通常的泰坦生存教科书示例。我定义：

def read_csv(batch_size, file_path, record_defaults):

    filename_queue = tf.train.string_input_producer([file_path])
    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)
def inputs():
    passenger_id, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked = \
        read_csv(BATCH_SIZE, file_path, record_defaults)

    # convert categorical data
    is_first_class = tf.to_float(tf.equal(pclass, [1]))
    is_second_class = tf.to_float(tf.equal(pclass, [2]))
    is_third_class = tf.to_float(tf.equal(pclass, [3]))

    gender = tf.to_float(tf.equal(sex, ["female"]))

    # Finally we pack all the features in a single matrix;
    # We then transpose to have a matrix with one example per row and one feature per column.
    features = tf.transpose(tf.pack([is_first_class, is_second_class, is_third_class, gender, age]))


    print 'shape of features', features.get_shape()
    return features, survived

现在我尝试做：

graph = tf.Graph()

with tf.Session(graph=graph) as sess:

    W = tf.Variable(tf.zeros([5, 1]), name="weights")
    b = tf.Variable(0., name="bias")
    tf.global_variables_initializer().run()
    print 'tf was run'
    X,Y = inputs()
    print 'inputs!'
    sess.run(Y)

我看到了

'tf was run!'
'inputs!'

但是run部分永远挂起（或至少是一个非常长的结果）。我在Jupyter上使用2.7内核和tf版0.12

运行此操作

我错过了什么？

Answer 1

在

行

return tf.train.shuffle_batch(decoded,
                              batch_size=batch_size,
                              capacity=batch_size * 50,
                              min_after_dequeue=batch_size)

您定义从队列中提取值和创建批次的操作。

如果你查看方法的完整签名，你会发现有一个参数引用了许多线程。

tf.train.shuffle_batch(
tensors,
batch_size,
capacity,
min_after_dequeue,
num_threads=1,
seed=None, enqueue_many=False, shapes=None,
allow_smaller_final_batch=False, shared_name=None, name=None)

我指出了这一点，因为您定义的操作是从某些线程执行的。线程必须启动和停止，此功能不会为您执行此操作。关于线程处理，这个函数唯一做的就是将num_thread添加到队列中。

实际上，要启动和停止需要在会话中定义一个唤醒队列中线程的操作的线程：

graph = tf.Graph()

with tf.Session(graph=graph) as sess:

    W = tf.Variable(tf.zeros([5, 1]), name="weights")
    b = tf.Variable(0., name="bias")
    tf.global_variables_initializer().run()
    print 'tf was run'
    X,Y = inputs()
    # define a coordinator to start and stop the threads
    coord = tf.train.Coordinator()
    # wake up the threads
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    print 'inputs!'
    sess.run(Y) #execute operation
    # When done, ask the threads to stop.
    coord.request_stop()
    # Wait for threads to finish.
    coord.join(threads)

tensorflow运行永远需要小数据

1 个答案: