Question

我有一个大的张量（几百兆字节）已经出局。

small_queue = tf.FIFOQueue(10, [tf.float64, tf.float64, tf.float64])
big_queue = tf.FIFOQueue(10, [tf.float64])

....

small1, small2, small3 = small_queue.dequeue()
large = big_queue.dequeue()
result = process(small1, small2, small3, large)

...

with tf.Session() as S:
    R = S.run(result)

我希望在large的后续调用中重复使用S.run变量，但我不确定如何处理现有的tensorflow variable sharing范例。 tf.get_variable需要初始化程序，因此这是错误的方法，但说明了我尝试做的事情：

with tf.variable_scope("cache"):
    large = tf.get_variable("large", initializer=big_queue.dequeue())

编辑1 ：以下是一个更完整的示例 - 我想在result1 result2和get_expr() >

import time
import numpy as np
import threading

import tensorflow as tf

capacity=10
data_shape1 = [10, 3000, 128, 4]
data_shape2 = [20, 500, 100]
dtype1 = np.float64
dtype2 = np.int32

data_input1 = tf.placeholder(dtype1, shape=data_shape1)
data_input2 = tf.placeholder(dtype2, shape=data_shape2)

queue = tf.FIFOQueue(capacity=capacity,
    dtypes=[dtype1, dtype2],
    shapes=[data_shape1, data_shape2],
    name="FIFOQueue")

def load_and_enqueue(session):
    enqueue_op = queue.enqueue([data_input1, data_input2])

    for i in xrange(1, capacity+1):
        # Just feed random stuff to the queue
        random_data1 = np.full(data_shape1, i, dtype=dtype1)
        random_data2 = np.full(data_shape2, i, dtype=dtype2)

        # Feed example to Tensorflow placeholder
        feed_dict = { data_input1: random_data1,
            data_input2: random_data2 }

        print ("Enqueueing {i} with shape {s} "
            "and size {b} MB").format(
                i=i,
                s=random_data1.shape,
                b=random_data1.nbytes/(1024**2))

        # Push all the training examples to the queue
        session.run(enqueue_op, feed_dict=feed_dict)


def get_expr():
    result1, result2 = queue.dequeue()

    # Would like to cache result1, result2
    # at this point

    return result1

with tf.Session() as S:
    # Start enqueue thread
    t = threading.Thread(target=load_and_enqueue, args=(S,))
    t.setDaemon(True)
    t.start()
    # Wait for all data to be enqueued
    t.join()

    expr1 = get_expr()
    expr2 = get_expr()

    S.run(tf.initialize_all_variables())
    print S.run(expr1).ravel()[0]
    print S.run(expr2).ravel()[0]

Answer 1

我不确定我理解你的问题。

如果您只想获得Tensor变量，则tf.get_variable之后需要scope.reuse_variables()：

with tf.scope('my_scope') as scope:
    scope.reuse_variables()
    large_var = tf.get_variable('my_large_var')
    #do something

如果您需要评估多个变量，我认为您需要sess.run([var1, var2])（我不确定，但在cifair10示例中有字符串_, loss_value = sess.run([train_op, loss])）。

在Tensorflow中重用大的出列变量

1 个答案: