Tensorarray initialization

Question

I am writing an example program to learn distributed tensorflow on 4 nodes. My program consists of assigning values to a simple numpy array. Then I convert them into tensorflow arrays to do a basic arithmetic addition operation. I store the outputs in another tensorflowarray. Finally convert this array into tensor to print it in the output.

import tensorflow as tf
import numpy as np
import datetime

u1 = tf.TensorArray(dtype=tf.float64,size=101)
u2 = tf.TensorArray(dtype=tf.float64,size=101)
u3 = tf.TensorArray(dtype=tf.float64,size=101)
u4 = tf.TensorArray(dtype=tf.float64,size=101)
u5 = tf.Variable(np.zeros(101))
u = np.zeros(101)
a = tf.Variable(0, dtype=tf.float64)
L = 100.0

x = tf.constant(2, dtype=tf.float64)

t1 = datetime.datetime.now()
with tf.device("/job:local/task:3"):
    for i in range(2):
        for j in range(101):
            # u[j] = (np.sin(2*np.pi*j/L))
            u[j] = j
            u1 = u1.write(j,u[j])
            u2 = u2.write(j,u[j])
            u3 = u3.write(j,u1.read(j)+u2.read(j))

        u4 = u3.pack()
        assign = tf.assign(u5, u3(100))


model = tf.initialize_all_variables()

with tf.Session("grpc://localhost:2222") as sess:
    sess.run(model)
    print sess.run(u4)
    print sess.run(assign)
    # print (u4.eval())
t2 = datetime.datetime.now()

print('\n')
print "Multi node computation time: " + str(t2-t1)

I want to overwrite the tensorarrays u1,u2,u3,u4. The above written program gives the following error:-

InvalidArgumentError (see above for traceback): TensorArray TensorArray_1_21: Could not write to TensorArray index 0 because it has already been read.

Is there anyway to initialize the used tensorarrays after the first for loop? Please help.

Allen Lavoie · Accepted Answer · 2016-10-31 19:52:28Z

2

TensorArrays are a "write-once" data structure: after they've been read from (e.g. with pack()) they can't be written to. They're mainly useful in combination with tf.while_loop (see the TensorArray documentation).

It looks like TensorArrays aren't a great fit here. You could use Python lists of Tensors, which will avoid the TensorArray errors, although this will lead to a large graph when looping over many values. If that becomes an issue, you can switch the Python for loops over to a while_loop or one of the higher-level looping constructs. It's not clear exactly what you're trying to do, but you might look at scan() in particular if you want to save intermediate values.

answered Oct 31, 2016 at 19:52

Allen Lavoie

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