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The Road to TensorFlow – Part 9: TensorBoard

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We’ve spent some time developing a Neural Network model for predicting the stock market. TensorFlow has produced a fairly black box implementation that is trained by historical data and then can output predictions for tomorrow’s prices.

But what confidence do we have that this model is really doing what we want? Last time we discussed some of the meta-parameters that configure the model. How do we know these are vaguely correct? How do we know if the weights we are training are converging? If we want to step through the model, how do we do that?

TensorFlow comes with a tool called TensorBoard which you can use to get some insight into what is happening. You can’t easily just print variables since they are all internal to the TensorFlow engine and only have values when required as a session is running. There is also the problem with how to visualize the variables. The weights matrix is very large and is constantly changing as you train it, you certainly don’t want to print this out repeatedly, let alone try to read through it.

To use TensorBoard you instrument your program. You tell it what you want to track and assign useful names to those items. This data is then written to log files as your model runs. You then run the TensorBoard program to process these log files and view the results in your Web Browser.

Something Went Wrong

Due to household logistics I moved my TensorFlow work over to my MacBook Air from running in an Ubuntu VM image on our Windows 10 laptop. Installing Python 3, TensorFlow and the various other libraries I’m using was quite simple and straight forward. Just install Python from and then use pip3 to install any other libraries. That all worked fine. But when I started running the program from last time, I was getting NaN results quite often. I wondered if TensorFlow wasn’t working right on my Mac? Anyway I went to debug the program and that led me to TensorBoard. As it turns out there was quite a bad bug in the program presented last time due to un-initialized variables.

You tend to get complacent programming in Python about un-initialized variables (and array subscript range errors) because usually Python will raise and exception if you try to use a variable that hasn’t been initialized. The problem is NumPy which is a library written in C for efficiency. When you create a NumPy array, it is returned to Python, telling Python its good to go. But since its managed by C code you don’t get the usual Python error checking. So when I changed the program to add the volumes to the price changes, I had a bug that left some of the data arrays uninitialized. I suspect on the Windows 10 laptop that these were initialized to zero, but that all depends on which exact C runtime is being used. On the Mac these values were just random memory and that immediately led to program errors.

Adding the TensorBoard initialization showed the problem was originating with the data and then it was fairly straight forward to zero in on the problem and fix it.

As a result, for this article, I’m just going to overwrite the Python file from last time with a newer one ( which is posted here. This version includes TensorBoard instrumentation and a couple of other improvements that I’ll talk about next time.


First we’ll start with some of the things that TensorBoard shows you. If you read an overview of TensorFlow it’s a bit confusing about what are Tensors and what flows. If you’ve looked at the program so far, it shows quite a few algebraic matrix equations, but where are the Tensors? What TensorFlow does is break these equations down into nodes where each node is a function execution and the data flows along the edges. This is a fairly common way to evaluate algebraic expressions and not unique to TensorFlow. TensorFlow then supports executing these on GPUs and in distributed environments as well as providing all the node types you need to create Neural Networks. TensorBoard gives you a way to visualize these graphs. The names of the nodes are from the program instrumentation.


When the program was instrumented it grouped things together. Here is an expansion of the trainingmodel box where you can see the operations that make up our model.


This gives us some confidence that we have constructed our TensorFlow graph correctly, but doesn’t show any data.

We can track various statistics of all our TensorFlow variables over time. This graph is showing a track of the means of the various weight and bias matrixes.


TensorBoard also lets us look at the distribution of the matrix values over time.


TensorBoard also lets us look at histograms of the data and how those histograms evolve over time.


You can see how the layer 1 weights start as their seeded normal distribution of random numbers and then progress to their new values as training progresses. If you look at all these graphs you can see that the values are still progressing when training stops. This is because TensorBoard instrumentation really slows down processing, so I shortened the training steps while using TensorBoard. I could let it run much longer over night to ensure that I am providing sufficient training for all the values to settle down.

Program Instrumentation

Rather than include all the code here, check out the Google Drive for the Python source file. But quickly we added a function to get all the statistics on a variable:

def variable_summaries(var, name):
  """Attach a lot of summaries to a Tensor."""
  with tf.name_scope('summaries'):
   mean = tf.reduce_mean(var)
   tf.scalar_summary('mean/' + name, mean)
  with tf.name_scope('stddev'):
   stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
   tf.scalar_summary('stddev/' + name, stddev)
  tf.scalar_summary('max/' + name, tf.reduce_max(var))
  tf.scalar_summary('min/' + name, tf.reduce_min(var))
  tf.histogram_summary(name, var)

We define names in the various section and indicate the data we want to collect:

with tf.name_scope('Layer1'):
    with tf.name_scope('weights'):
        layer1_weights = tf.Variable(tf.truncated_normal(
            [NHistData * num_stocks * 2, num_hidden], stddev=0.1))
        variable_summaries(layer1_weights, 'Layer1' + '/weights')
     with tf.name_scope('biases'):
         layer1_biases = tf.Variable(tf.zeros([num_hidden]))
         variable_summaries(layer1_biases, 'Layer1' + '/biases')

Before the call to initialize_all_variables we need to call:

merged = tf.merge_all_summaries()
test_writer = tf.train.SummaryWriter('/tmp/tf/test',
    session.graph )

And then during training:

summary, _, l, predictions =
    [merged, optimizer, loss, train_prediction], feed_dict=feed_dict)
test_writer.add_summary(summary, i)


TensorBoard is quite a good tool to give you insight into what is going on in your model. Whether the program is correctly doing what you think and whether there is any sanity to the data. It also lets you tune the various parameters to ensure you are getting best results.

Written by smist08

October 1, 2016 at 4:49 pm

One Response

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  1. Really great examples and insight, thanks Stephen. This blog is excellent.

    Aslan Kanzas

    October 3, 2016 at 12:10 am

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