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TensorBoard: TF Dev Summit Tutorial
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# Copyright 2017 Google, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import tensorflow as tf
import urllib
LOGDIR = '/tmp/mnist_tutorial/'
GIST_URL = 'https://gist.githubusercontent.com/dandelionmane/4f02ab8f1451e276fea1f165a20336f1/raw/dfb8ee95b010480d56a73f324aca480b3820c180'
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
### Get a sprite and labels file for the embedding projector ###
urllib.urlretrieve(GIST_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
urllib.urlretrieve(GIST_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')
def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.nn.relu(tf.matmul(input, w) + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
if use_two_conv:
conv1 = conv_layer(x_image, 1, 32, "conv1")
conv_out = conv_layer(conv1, 32, 64, "conv2")
else:
conv1 = conv_layer(x_image, 1, 64, "conv")
conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
if use_two_fc:
fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, "fc1")
embedding_input = fc1
embedding_size = 1024
logits = fc_layer(fc1, 1024, 10, "fc2")
else:
embedding_input = flattened
embedding_size = 7*7*64
logits = fc_layer(flattened, 7*7*64, 10, "fc")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(2001):
batch = mnist.train.next_batch(100)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
conv_param = "conv=2" if use_two_conv else "conv=1"
fc_param = "fc=2" if use_two_fc else "fc=1"
return "lr_%.0E,%s,%s" % (learning_rate, conv_param, fc_param)
def main():
# You can try adding some more learning rates
for learning_rate in [1E-4]:
# Include "False" as a value to try different model architectures
for use_two_fc in [True]:
for use_two_conv in [True]:
# Construct a hyperparameter string for each one (example: "lr_1E-3,fc=2,conv=2)
hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
print('Starting run for %s' % hparam)
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)
if __name__ == '__main__':
main()
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@jborlinic

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commented Feb 24, 2017

I'd just like to say that this example has been one of the best examples/tutorials of Tensorflow I've come across in the past few months.
Thank you :)

@mamcgrath

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commented Feb 28, 2017

Thanks for tutorial, the new tools look great.

@kickoffqi

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commented Mar 3, 2017

just watch your video on Youtube. Thank you for our sharing.
Best example to show the power of TensorBoard. Can wait to se TensorBoard debugging.

@kickoffqi

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commented Mar 3, 2017

I found the png file is empty and pdf is broken after I cloned this to my local Git.

@ubergarm

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commented Mar 5, 2017

The png is empty for me too, even if I download the whole shebang as a .zip.

I ported some of this visualization instrumentation code into a nice general RNN MNIST example:
https://github.com/ubergarm/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py

Great talk/video @dandelionmane

@Queequeg92

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commented Mar 7, 2017

Great tutorial! Would you like to share the tools to produce metadata(TSV file, sprite image)?

@leejaymin

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commented Mar 14, 2017

At first, I'd like to thank you for nice talk.
However, the file is broken and the code occurs "socket time-out error".
Does anyone know it ?

@sebaschaal

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commented Mar 15, 2017

The socket timeout error happened, since the website http://yann.lecun.com/exdb/mnist/, where the MNIST data set is taken from, was down (at least the download links were).
Try again today.

@mamcgrath

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commented Mar 15, 2017

Looks like Gist messes up the binary files when code changes are made. If you are looking for a copy of the sprite png try https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial

@sebaschaal

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commented Mar 15, 2017

I have one more questions. Isn't this implementation using the RELU also in the output layer before the softmax?
I think that is screwing up the training?

@rafalfirlejczyk

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commented Mar 15, 2017

Great tutorial and show! Thanks.

When running the mnist.py with python3.5 I get the failure:

File "mnist.py", line 25, in
urllib.urlretrieve(GIST_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
AttributeError: module 'urllib' has no attribute 'urlretrieve'

@rafalfirlejczyk

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commented Mar 15, 2017

Problem solved.

  1. I downloaded the code from the other source mentioned already above:
    https://github.com/mamcgrath/TensorBoard-TF-Dev-Summit-Tutorial

  2. I corrected the cuda installation as described here:
    tensorflow/tensorflow#5968

I got the nice Tensorboard graphs and scalars running:
tensorboard --logdir /tmp/mnist_tutorial

@ghost

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commented May 7, 2017

so nobody solve the problem the pic sprite_1024.png is broken and we can't load the data in the first step....

@arunkumarwa

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commented May 20, 2017

embeddings visualizer is not working (the rest seem to be working fine). I got the file from the other location (one location has an empty file) mentioned in the thread above (~ 32kb in size?). But Tensorboard gets stuck "Fetching sprite image.."

@arunkumarwa

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commented May 20, 2017

Actually the sprite_1024.png from the location that @rafalfirlejczyk mentioned up above works. Thanks @rafalfirlejczyk !

I can see PCA and T-SNE views of the 1024 data points / labels. It would be very convenient if the code itself just generates the tsv and png files when it is writing out the tensor variables. Perhaps it does and I am just not seeing it? (I am new to this).

@xiaoxinyi

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commented May 31, 2017

Save sprite_1024.png.

import numpy as np
import scipy.misc as misc

sprite_images = mnist.test.images[:1024]

x = None
res = None
for i in range(32):
    x = None
    for j in range(32):
        img = sprite_images[i*32 + j,:].reshape((28, 28))
        x = np.concatenate((x, img), axis=1) if x is not None else img
    res = np.concatenate((res, x), axis=0) if res is  not None else  x

misc.toimage(256 - res, channel_axis=0).save('sprite_1024.png')
@bajorekp

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commented Jun 11, 2017

Last fc layer should be without tf.relu function, because later we use softmax.

@decentralion

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Owner Author

commented Jun 11, 2017

I've moved the tutorial (and added a few fixes) to a GitHub repository:
https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial

@GoingMyWay

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commented Jun 30, 2017

Great job, after learning how to use tensorboard, I can easily to know the performance of the algorithm via web browser.

@arnaldog12

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commented Jul 16, 2017

The slides file are broken for me too

@shekhovt

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commented Sep 20, 2017

Hi,

With this version of code I am getting very poor training results, not at all like in the video,
image

I have no idea why. It is in the default settings 2 conv, 2 fc, learning rate 1e-4 Adam. Different runs may land in very different training accuracy but more often a poor accuracy and never close to 1.

Ok, after reading the other comments, the problem is clear:
it is the ReLu + softmax activation on the output. The moved tutorial repository does not have this problem. Maybe you should take this one down.

@Steven0706

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commented Oct 21, 2017

This is an amazing TensorBoard example! Love it!

@bluesammer

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commented Oct 26, 2017

Beautiful relatable example for humans to comprehend the power of tensorboard. Switching to my own data use cases will be cool.

@cnzero

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commented Nov 14, 2017

@shekhovt Yes, the same problem to me. At this time, one dropout layer between two fully-connected neural network would make results better. Have a try.

@psvrao

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commented Jan 14, 2018

embedding visualisation is not working for me. I can see both label and sprite image files, but tensorboard is unable to load them, it just says loading forever... I have downloaded the files from https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial
labels file does not have a header in the first line, it simply has label(digit) in each row. Could that be a problem?
I am able to see all other graphs without any issue... Any help appreciated

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