One way to analyze these is in terms of memory and time complexity (which depends on how you want to access the graph). Storing nodes as objects with pointers to one another The memory complexity for this approach is O(n) because you have as many objects as you have nodes. The number of pointers (to … Read more
You can use this simple script to do that. But you must specify the names of the output nodes. import tensorflow as tf meta_path=”model.ckpt-22480.meta” # Your .meta file output_node_names = [‘output:0’] # Output nodes with tf.Session() as sess: # Restore the graph saver = tf.train.import_meta_graph(meta_path) # Load weights saver.restore(sess,tf.train.latest_checkpoint(‘path/of/your/.meta/file’)) # Freeze the graph frozen_graph_def = … Read more
This following works for me (gnuplot 4.4) plot “./file.dat” u 1:2:3 with lines palette Hope this helps. When I ran your code gnuplot couldn’t pass the “rgb” part. For an example of using the variable tag see the similar question: GNUPLOT: dot plot with data depending dot size with the useful examples found here: http://gnuplot.sourceforge.net/demo/pointsize.html … Read more
There’s no API for pinning ops to a particular core at present, though this would make a good feature request. You could approximate this functionality by creating multiple CPU devices, each with a single-threaded threadpool, but this isn’t guaranteed to maintain the locality of a core-pinning solution: with tf.device(“/cpu:4”): # … with tf.device(“/cpu:7”): # … … Read more
Simple bar graph: set boxwidth 0.5 set style fill solid plot “data.dat” using 1:3:xtic(2) with boxes data.dat: 0 label 100 1 label2 450 2 “bar label” 75 If you want to style your bars differently, you can do something like: set style line 1 lc rgb “red” set style line 2 lc rgb “blue” set … Read more
First of all, for some historical context, “in-graph replication” is the first approach that we tried in TensorFlow, and it did not achieve the performance that many users required, so the more complicated “between-graph” approach is the current recommended way to perform distributed training. Higher-level libraries such as tf.learn use the “between-graph” approach for distributed … Read more
For an undirected graph the standard approach is to look for a so called cycle base : a set of simple cycles from which one can generate through combinations all other cycles. These are not necessarily all simple cycles in the graph. Consider for example the following graph: A / \ B —– C \ … Read more