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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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Thanks for tutorial, the new tools look great. |
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just watch your video on Youtube. Thank you for our sharing. |
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I found the png file is empty and pdf is broken after I cloned this to my local Git. |
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The I ported some of this visualization instrumentation code into a nice general RNN MNIST example: Great talk/video @dandelionmane |
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Great tutorial! Would you like to share the tools to produce metadata(TSV file, sprite image)? |
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At first, I'd like to thank you for nice talk. |
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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). |
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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 |
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I have one more questions. Isn't this implementation using the RELU also in the output layer before the softmax? |
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Great tutorial and show! Thanks. When running the mnist.py with python3.5 I get the failure: File "mnist.py", line 25, in |
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Problem solved.
I got the nice Tensorboard graphs and scalars running: |
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so nobody solve the problem the pic sprite_1024.png is broken and we can't load the data in the first step.... |
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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.." |
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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). |
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Save 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') |
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Last fc layer should be without tf.relu function, because later we use softmax. |
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I've moved the tutorial (and added a few fixes) to a GitHub repository: |
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Great job, after learning how to use tensorboard, I can easily to know the performance of the algorithm via web browser. |
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The slides file are broken for me too |
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This is an amazing TensorBoard example! Love it! |
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Beautiful relatable example for humans to comprehend the power of tensorboard. Switching to my own data use cases will be cool. |
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@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. |
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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 |
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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 :)