Tensorflow 在循环中多次运行会话 [英] Tensorflow running session multiple times in a loop

问题描述

我正在尝试一个简单的 Tensorflow 代码来多次计算两个矩阵的乘积.我的代码如下:

I'm trying out a simple Tensorflow code to compute the product of two matrices multiple times. My code is as follows:

import numpy as np
import tensorflow as tf

times = 10
alpha = 2
beta = 3

graph = tf.Graph()

with graph.as_default():
    A = tf.placeholder(tf.float32)
    B = tf.placeholder(tf.float32)
    C = tf.placeholder(tf.float32)
    alpha = tf.constant(2.0, shape=[1, 1])
    beta = tf.constant(3.0, shape=[1, 1])
    D = alpha*tf.matmul(A, B) + beta*C          

with tf.Session(graph=graph) as session:
    tf.initialize_all_variables().run()
    for time in xrange(1, 2):
        N = 10**time
        a = tf.constant(np.random.random((N, N)))
        b = tf.constant(np.random.random((N, N)))
        c = tf.constant(np.random.random((N, N)))

        for num in xrange(1, 3):
            print num
            session.run(D, feed_dict={A:a.eval(), B:b.eval(), C:c.eval()})      
            c = D

在 for 循环中运行 session.run() 时:

Upon running session.run() in the for loop:

for num in xrange(1, 3):
    print num
    session.run(D, feed_dict={A:a.eval(), B:b.eval(), C:c.eval()})      
    c = D

我收到以下错误:

我在 Tensorflow 网站上查看了 MNIST 的示例代码，但它们在 for 循环中以类似的方式运行session.run()".我正在寻找有关为什么我的代码中的session.run()"在 for 循环中不起作用的任何见解.

I looked at the sample code for MNIST on the Tensorflow website but they run 'session.run()' in a similar manner in a for loop. I'm looking for any insight on why 'session.run()' in my code does not work inside a for loop.

谢谢.

推荐答案

with tf.Session(graph=graph) as session:
    tf.initialize_all_variables().run()
    for time in xrange(1, 2):
        N = 10**time
        a = np.random.random((N, N))
        b = np.random.random((N, N))
        c = np.random.random((N, N))

        for num in xrange(1, 3):
            print num
            c = session.run(D, feed_dict={A:a, B:b, C:c})

您可以直接输入 numpy 数组，Session.run(D, ...) 返回 D's 评估.

You can feed numpy array directly and Session.run(D, ...) returns D's evaluation.

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