mirror of
https://github.com/clearml/clearml
synced 2025-01-31 17:17:00 +00:00
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# TRAINS - Example of tensorboard with tensorflow (without any actual training)
|
|
#
|
|
import tensorflow as tf
|
|
import numpy as np
|
|
import cv2
|
|
from time import sleep
|
|
#import tensorflow.compat.v1 as tf
|
|
#tf.disable_v2_behavior()
|
|
|
|
from trains import Task
|
|
task = Task.init(project_name='examples', task_name='tensorboard toy example')
|
|
|
|
|
|
k = tf.placeholder(tf.float32)
|
|
|
|
# Make a normal distribution, with a shifting mean
|
|
mean_moving_normal = tf.random_normal(shape=[1000], mean=(5*k), stddev=1)
|
|
# Record that distribution into a histogram summary
|
|
tf.summary.histogram("normal/moving_mean", mean_moving_normal)
|
|
tf.summary.scalar("normal/value", mean_moving_normal[-1])
|
|
|
|
# Make a normal distribution with shrinking variance
|
|
variance_shrinking_normal = tf.random_normal(shape=[1000], mean=0, stddev=1-(k))
|
|
# Record that distribution too
|
|
tf.summary.histogram("normal/shrinking_variance", variance_shrinking_normal)
|
|
tf.summary.scalar("normal/variance_shrinking_normal", variance_shrinking_normal[-1])
|
|
|
|
# Let's combine both of those distributions into one dataset
|
|
normal_combined = tf.concat([mean_moving_normal, variance_shrinking_normal], 0)
|
|
# We add another histogram summary to record the combined distribution
|
|
tf.summary.histogram("normal/bimodal", normal_combined)
|
|
tf.summary.scalar("normal/normal_combined", normal_combined[0])
|
|
|
|
# Add a gamma distribution
|
|
gamma = tf.random_gamma(shape=[1000], alpha=k)
|
|
tf.summary.histogram("gamma", gamma)
|
|
|
|
# And a poisson distribution
|
|
poisson = tf.random_poisson(shape=[1000], lam=k)
|
|
tf.summary.histogram("poisson", poisson)
|
|
|
|
# And a uniform distribution
|
|
uniform = tf.random_uniform(shape=[1000], maxval=k*10)
|
|
tf.summary.histogram("uniform", uniform)
|
|
|
|
# Finally, combine everything together!
|
|
all_distributions = [mean_moving_normal, variance_shrinking_normal, gamma, poisson, uniform]
|
|
all_combined = tf.concat(all_distributions, 0)
|
|
tf.summary.histogram("all_combined", all_combined)
|
|
|
|
# convert to 4d [batch, col, row, RGB-channels]
|
|
image = cv2.imread('./samples/picasso.jpg')
|
|
image = image[:, :, 0][np.newaxis, :, :, np.newaxis]
|
|
# image = image[np.newaxis, :, :, :] # test greyscale image
|
|
|
|
# un-comment to add image reporting
|
|
tf.summary.image("test", image, max_outputs=10)
|
|
|
|
# Setup a session and summary writer
|
|
summaries = tf.summary.merge_all()
|
|
sess = tf.Session()
|
|
|
|
logger = task.get_logger()
|
|
|
|
# Use original FileWriter for comparison , run:
|
|
# % tensorboard --logdir=/tmp/histogram_example
|
|
writer = tf.summary.FileWriter("/tmp/histogram_example")
|
|
|
|
# Setup a loop and write the summaries to disk
|
|
N = 40
|
|
for step in range(N):
|
|
k_val = step/float(N)
|
|
summ = sess.run(summaries, feed_dict={k: k_val})
|
|
writer.add_summary(summ, global_step=step)
|
|
|
|
print('Done!')
|