This has broken some tests.
https://travis-ci.org/malmaud/TensorFlow.jl/jobs/353723466#L1184

I've not looked too closely as to why,
Might the the tests are too fragile, might not.

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Merging #367 into master will increase coverage by 0.09%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #367      +/-   ##
==========================================
+ Coverage   64.73%   64.82%   +0.09%     
==========================================
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  Lines        2895     2900       +5     
==========================================
+ Hits         1874     1880       +6     
+ Misses       1021     1020       -1

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Thanks, I updated the failing test.

Since reduce without axis is implemented with multiple nodes I think that @tf Ysum1 = reduce_sum(Y) should create a scope that contains these nodes. Since the last of those nodes is a sum the output node is then named Ysum1/sum.

If an axis is given explicitly Ysum5 = reduce_sum(Y, axis=2) there's just one node, and it can then take the name Ysum5.

It bothers me that we have a function that takes a name argument and returns a Node.
and even if we provide the name argument directly, the returned node does not have that name.

I'm not sure what the resolution is.

This is definitely a breaking change -- it will break some of my code I am certain.

Ah, just because there is a node named reduce_sum/rank doesn't prevent us from creating a node named reduce_sum. I thought it did.

However before this PR the node was named reduce, is that inherited from python? I think reduce_sum (i.e reduce_$reduction)would be a better name.

Hmm,
Can you pop out some python graphs and compare?

Here are some graphs. tensorflow.py v1.0 and v1.8 were near identical.

TensorFlow.jl known rank:

tensorflow.py v1.8 known rank:

TensorFlow.jl unknown rank:

tensorflow.py v1.8 unknown rank:

Code for the above:

julia

using TensorFlow; tf = TensorFlow

v = tf.placeholder(Float32, shape=[2,3,5])

myop = tf.with_op_name("myop") do
    tf.reduce_sum(v) + tf.reduce_sum(v) + tf.reduce_sum(v, name="s")
end

myop_dim = tf.with_op_name("myop3") do
    tf.reduce_sum(v, axis=3) + tf.reduce_sum(v, axis=3) + tf.reduce_sum(v, axis=3, name="dims")
end

myop + myop_dim

sess = tf.Session()
fw = tf.summary.FileWriter("models/tensorflow.jl")

python

import tensorflow as tf

v = tf.placeholder(tf.float32, shape=[2,3,4])

with(tf.name_scope("myop")):
    myop = tf.reduce_sum(v) + tf.reduce_sum(v) + tf.reduce_sum(v, name='s')

with(tf.name_scope("myop2")):
    myop2 = tf.reduce_sum(v, 2) + tf.reduce_sum(v, 2) + tf.reduce_sum(v, 2, name="dims")

myop + myop2

sess = tf.Session()
fw = tf.summary.FileWriter('models/tensorflow.py')
fw.add_graph(sess.graph)

This is what the graph from TensorFlow.jl looks like before this PR (both the case with known rank and the case with unknown rank produces the same graph):

This looks good to me.
@malmaud can you give this a once over?

LGTM. Seems to be a a conflict now though.

@gustafsson Are you interested in rebasing this on the current TensorFlow.jl master?