tensorboard with numpy array - numpy

Question

Can someone give a example on how to use tensorboard visualize numpy array value?
There is a related question here, I don't really get it.
Tensorboard logging non-tensor (numpy) information (AUC)
For example,
If I have
for i in range(100):
foo = np.random.rand(3,2)
How can I keep tracking the distribution of foo using tensorboard for 100 iterations? Can someone give a code example?
Thanks.

Answer 1

For simple values (scalar), you can use this recipe
summary_writer = tf.train.SummaryWriter(FLAGS.logdir)
summary = tf.Summary()
summary.value.add(tag=tagname, simple_value=value)
summary_writer.add_summary(summary, global_step)
summary_writer.flush()
As far as using array, perhaps you can add 6 values in a sequence, ie
for value in foo:
summary.value.add(tag=tagname, simple_value=value)

Answer 2

Another (simplest) way is just using placeholders. First, you can make a placeholder for your numpy array shape.
# Some place holders for summary
summary_reward = tf.placeholder(tf.float32, shape=(), name="reward")
tf.summary.scalar("reward", summary_reward)
Then, just call session.run the merged summary with the feed_dict.
# Summary
summ = tf.summary.merge_all()
...
s = sess.run(summ, feed_dict={summary_reward: reward})
writer.add_summary(s, i)

Answer 3

if you install this package via pip install tensorboard-pytorch it becomes as straightforward as it can get:
import numpy as np
from tensorboardX import SummaryWriter
writer = SummaryWriter()
for i in range(50):
writer.add_histogram("moving_gauss", np.random.normal(i, i, 1000), i, bins="auto")
writer.close()
Will generate the corresponding histogram data in the runs directory:

Answer 4

Found a way to work around, create a variable and assign the value of numpy array to the variable, use tensorboard to track the variable
mysummary_writer = tf.train.SummaryWriter("./tmp/test/")
a = tf.Variable(tf.zeros([3,2]), name="a")
sum1 = tf.histogram_summary("nparray1", a)
summary_op = tf.merge_all_summaries()
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for ii in range(10):
foo = np.random.rand(3, 2)
assign_op = a.assign(foo)
summary, _ = sess.run([summary_op, assign_op])
mysummary_writer.add_summary(tf.Summary.FromString(summary), global_step=ii)
mysummary_writer.flush()

Answer 5

sess = tf.Session()
writer = tf.summary.FileWriter('tensorboard_test')
var = tf.Variable(0.0,trainable=False,name='loss')
sess.run(var.initializer)
summary_op = tf.summary.scalar('scalar1',var)
for value in array:
sess.run(var.assign(value))
summary = sess.run(summary_op)
writer.add_summary(summary,i)
It works, but slow.

Answer 6

You could define a function like this (taken from gyglim's gist):
def add_histogram(writer, tag, values, step, bins=1000):
"""
Logs the histogram of a list/vector of values.
From: https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
"""
# Create histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill fields of histogram proto
hist = tf.HistogramProto()
hist.min = float(np.min(values))
hist.max = float(np.max(values))
hist.num = int(np.prod(values.shape))
hist.sum = float(np.sum(values))
hist.sum_squares = float(np.sum(values ** 2))
# Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
# See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
# Therefore we drop the start of the first bin
bin_edges = bin_edges[1:]
# Add bin edges and counts
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
# Create and write Summary
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
writer.add_summary(summary, step)
And then add to the summary writer like this:
add_histogram(summary_writer, "Histogram_Name", your_numpy_array, step)

Answer 7

You can plot the vector with matplotlib, convert the plot to numpy array along the lines of
https://stackoverflow.com/a/35362787/10873169, and then add it to Tensorboard as image
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter("runs/example")
for step in range(1, 10):
# Time-dependent vector we want to plot
example_vector = np.sin(np.arange(100) / step)
# Plot it in matplotlib first. Default DPI doesn't look good in Tensorboard
fig = Figure(figsize=(5, 2), dpi=200)
canvas = FigureCanvasAgg(fig)
fig.gca().plot(example_vector)
canvas.draw()
# Get the image as a string of bytes
image_as_string = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
# Need to reshape to (height, width, channels)
target_shape = canvas.get_width_height()[::-1] + (3,)
reshaped_image = image_as_string.reshape(target_shape)
# Write to Tensorboard logs
writer.add_image("example_vector", reshaped_image,
dataformats="HWC", global_step=step)
writer.close()