Feeling like totally a graphical aspect within Tensorflow after reading its basic usage:

  • it uses graph to represent computation
  • nodes equal to operations (ops)
  • edges mean tensors, we see a tensor as a multi-dimension array (datas, a numpy ndarray)
  • need to launch the graph in a session
  • variables, constant, fetches, and feeds
  • a bit unfamiliar with interactive usage, use tf.InteractiveSession() to avoid keeping one variable holding the session.

Basic Usage

The easiest way to evaluate the actual value of a Tensor object is to pass it to the Session.run() method, or call Tensor.eval() when you have a default session (i.e. in a with tf.Session(): block, or see below).

sess = tf.InteractiveSession()

W = tf.Variable(numpy.random.randn(), name="weight")

# print tensor
W.eval() # or sess.run(W)

When running tensorflow with the interactive python, we can execute Tensor.eval() or Operation.run(op) to avoid keeping a variable holding a session

# Enter an interactive TensorFlow Session.
import tensorflow as tf
sess = tf.InteractiveSession()

x = tf.Variable([1.0, 2.0])
a = tf.constant([3.0, 3.0])

# Initialize 'x' using the run() method of its initializer op.

# Add an op to subtract 'a' from 'x'.  Run it and print the result
sub = tf.sub(x, a)
print(sub.eval())   ##### or print(sess.run(sub))
# ==> [-2. -1.]

# Close the Session when we're done.

Difference between Session.run() and Tensor.run()


Variables maintain state across executions of the graph, and must be initialized by the following 2 ways: #1. running an init Op:

state = tf.Variable(0, name=counter)
init_op = tf.initialize_all_variables()

#2. using the run() method of its initializer op:

x = tf.Variable([1.0,2.0])
x.initializer.run()    # x.initializer.run(session=sess)


To fetch multiple tensors:

intermed = tf.add(b, c)
mul = tf.mul(a, intermed)
result = sess.run( [mul, intermed] )  #### fetch 2 tensors


Feed is for patching a tensor into any operation in the graph

a = tf.placeholder(tf.int32)
one = tf.constant(1)
add = tf.add(a, one)

sess.run( [add], feed_dict={ a:[5] } )

A placeholder() operation will generate an error if you do not supply a feed for it.

Variables: Creation, Initialization, Saving, and Loading

Variables Saving and Restoring:

a = tf.Variable(..., name="v1")
b = tf.Variable(..., name="v2")
saver = tf.train.Saver()
save_path = saver.save(sess, /Users/elsalin/model.ckpt")

This generates checkpoint files: checkpoint, model.ckpt, and model.ckpt.meta

In the last part of model.ckpt.meta, we can see the saved variable names as “v1” and “v2”. Each one of them is saved under the name that was passed when the variable was created.

Restoring variables from a file don’t have to initialize them beforehand.

c = tf.Variable(..., name="v1")
d = tf.Variable(..., name="v2”)
saver = tf.train.Saver()
saver.restore(sess, "/Users/elsalin/model.ckpt)

save\restroe a subset of variables:

v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2”)
saver = tf.train.Saver({"my_v2": v2})

Tensorflow Mechanics 101

tf.app.run() —> a wrapper handles flag parsing and then dispatches to main(). see at https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/platform/default/_app.py

tf.app.flags —> a wrapper around python-gflag. Rather than an application having to define all flags in or near main(), each python module defines flags that are useful to it.

Here is the flow of how it works:

#1. prepare datas

#2. build graph

  • inference(): data model
  • loss(): cross entropy (the way that how you define a good model)
  • training()

#3. train model

  • run in session
  • train loop(): feed the graph(use feed_dict to input batches of datas), check status(print “loss” value), visualize status(TensorBoard), save checkpoint

#4. evaluate model

  • eval graph
  • eval output

TensorBoard: Visualizing Learning

Serialize data:

#1. create graph use tf.name_scope to clean up the graph representation

with tf.name_scope(Wx_b):
    y = tf.nn.softmax(tf.matmul(x, W) + b)

#2. annotate nodes with “summary operations”

tf.histogram_summary('weights', W)
# or
tf.scalar_summary('accuracy', accuracy)
tf.scalar_summary('cross entropy', cross_entropy)

#3. merge all summaries

merged = tf.merge_all_summaries()
# this returns a ‘string’ containing the serialized ‘summary’ protocol buffer.

#4. write to disk ( default out to /tmp/mnist_logs)

writer = tf.train.SummaryWriter(FLAGS.summaries_dir, sess.graph)

#5. every N steps, run

summary_str = sess.run(merged, feed_dict=feed)
writer.add_summary(summary_str, i)

Launch Tensorboard:

#1. run the source mnist_with_summaries.py under /examples/tutorials/mnist

#2. run tensorboard --logdir=path/to/log-directory

#3. see at localhost:6006


Tensorflow Basic Usage
Variables: Creation, Initialization, Saving, and Loading
Tensorflow Mechanics 101
TensorBoard: Visualizing Learning
TensorFlow Tutorial given by Dr. Chung-Cheng Chiu at Google Brain on Dec. 29, 2015
How to print the value of a Tensor object in TensorFlow?