### Variables

# Rank 0 tensor (scalar)
fruit = tf.Variable("Orange", tf.string)
quantity = tf.Variable(2, tf.int16)
price = tf.Variable(3.23, tf.float32)

# Rank 1 tensor
strings = tf.Variable(["Fruit", "orange"], tf.string)
prices  = tf.Variable([3.23, 4.02], tf.float64)

# Rank 2 tensor
answers = tf.Variable([[False, True],[False, False]], tf.bool)


When you train a model, we use variables to store training parameters like weight and bias, hyper parameters like learning rate, or state information like global step.

However, the best way to create a variable is using tf.get_variable. It allows deep net to share parameters.

Initialize variables:

import tensorflow as tf
import numpy as np

v1 =  tf.get_variable("v1", [5, 5, 3])  # A tensor with shape (5, 5, 3) filled with random values
v2 =  tf.get_variable("v2", [5, 5, 3], dtype=tf.int32, trainable=True)

v3 = tf.get_variable("v3", [3, 2], initializer=tf.zeros_initializer) # Set to 0
v4 = tf.get_variable("v4", [3, 2], initializer=tf.ones_initializer)  # Set to 1

v5 = tf.get_variable("v5", initializer=tf.constant(2))    # scalar: 2. float32.
v6 = tf.get_variable("v6", initializer=tf.constant([2]))  # [2]
v7 = tf.get_variable("v7", initializer=tf.constant([[2, 3], [4, 5]]))  # [[2, 3], [4, 5]]

v8 = tf.get_variable("v8", initializer=tf.constant(0.1, shape=[3, 2]))

# [[ 1.  2.], [ 3.  4.], [ 5.  6.]]
v9 = tf.get_variable("v3", [3, 2], initializer=tf.constant_initializer([1, 2, 3, 4, 5, 6]))

# [[ 1.  2.], [ 2.  2.], [ 2.  2.]]
v10 = tf.get_variable("v4", [3, 2], initializer=tf.constant_initializer([1, 2]))


Note: when we use tf.constant in tf.get_variable, we do not need to specify the tensor shape unless we want to change the shape of the Tensor from the constant data. By default, variable is of type float32. tf.get_variable assumes the variable is trainable.

Randomized the value of variables:

import tensorflow as tf
import numpy as np

W = tf.get_variable("W", [784, 256], initializer=tf.truncated_normal_initializer(stddev=np.sqrt(2.0 / 784)))
Z = tf.get_variable("z", [4, 5], initializer=tf.random_uniform_initializer(-1, 1))


The following program:

• Define variables and the initializers.
• Create op to update variable.
• Explicitly initialize the variables. (Always required)
• Retrieve a variable value.
import tensorflow as tf

### Using variables
# Define variables and its initializer
weights = tf.get_variable("W", [784, 256], initializer=tf.truncated_normal_initializer(stddev=np.sqrt(2.0 / 784)))
biases = tf.get_variable("z", [256], initializer=tf.zeros_initializer)

counter = tf.get_variable("counter", initializer=tf.constant(0))

# Add an Op to increment a counter
increment = tf.assign(counter , counter + 1)

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
# Execute the init_op to initialize all variables
sess.run(init_op)

# Retrieve the value of a variable
b = sess.run(biases)
print(b)


### Save a Checkpoint

Variables can be saved to a disk during training. It can be reloaded to continue the training or to make inferences.

# Create some variables
v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)

# Create the op
inc_v1 = v1.assign(v1+1)
dec_v2 = v2.assign(v2-1)
init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

with tf.Session() as sess:
sess.run(init_op)
inc_v1.op.run()
dec_v2.op.run()

# Save the variables to disk.
save_path = saver.save(sess, "/tmp/model.ckpt")


### Restore a checkpoint

# Create some variables.
# We do not need to provide initializer or init_op if it is restored from a checkpoint.
v1 = tf.get_variable("v1", shape=[3])
v2 = tf.get_variable("v2", shape=[5])

saver = tf.train.Saver()

with tf.Session() as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")

# Check the values of the variables
print("v1 : %s" % v1.eval())
print("v2 : %s" % v2.eval())


To save a subset of variables only.

v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)
v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)

# Save only v2
saver = tf.train.Saver({"v2": v2})

with tf.Session() as sess:
# Initialize v1 since the saver will not.
v1.initializer.run()
saver.restore(sess, "/tmp/model.ckpt")


### Load a model and saving checkpoints regularly

This is the sample code in loading the model at the beginning and saves it occasionally during training.

import tensorflow as tf
import os

session.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
saver.restore(session, os.path.join(checkpoint_dir, ckpt_name))
return True
else:
return False

def save(session, saver, checkpoint_dir, step):
dir = os.path.join(checkpoint_dir, "model")
saver.save(session, dir, global_step=step)

with tf.Session() as session:
saver = tf.train.Saver()
...
...
for i in range(10000):
...
if (i % 1000 == 0):
save(session, saver, "./checkpoint", i)


### Trainable/Non-trainable parameters

In transfer learning, we may load a model from a checkpoint but freeze some of the layers during training by setting “trainable=False”.

freezed_W = tf.get_variable('CNN_W!', [5, 5, 3, 32], trainable=False,
initializer=tf.truncated_normal_initializer(stddev=0.02))
...


In some problems, we may have multiple deep nets to be trained together. To have two different optimizers with different cost functions for different trainable parameters.

import tensorflow as tf

def scope_variables(name):
with tf.variable_scope(name):
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=tf.get_variable_scope().name)

# Model parameters for the discriminator network
with tf.variable_scope("discriminator"):
v1 = tf.get_variable("v1", [3], initializer=tf.zeros_initializer)
...

# Model parameters for the generator network
with tf.variable_scope("generator"):
v2 = tf.get_variable("v2", [2], initializer=tf.zeros_initializer)
...

# Get all the trainable parameters for the discriminator
discriminator_variables = scope_variables("discriminator")

# Get all the trainable parameters for the generator
generator_variables = scope_variables("generator")

# 2 optimizers each for different networks
train_discriminator = discriminator_optimizer.minimize(d_loss,
var_list=discriminator_variables)
train_generator = generator_optimizer.minimize(g_loss,
var_list=generator_variables)


### Scoping

We can use scoping such that we can create 2 different layers that have their own parameters from the same method. For example, cnn1 and cnn2 have their own $$w$$ and $$b$$.

import tensorflow as tf

def conv2d(input, output_dim, filter_h=5, filter_w=5, stride_h=2, stride_w=2, stddev=0.02):
w = tf.get_variable('w', [filter_h, filter_w, input.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input, w, strides=[1, stride_h, stride_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))

return conv

input1 = tf.random_normal([1,10,10,32])
input2 = tf.random_normal([1,20,20,32])

with tf.variable_scope("conv1"):
cnn1 = conv2d(input1, 16)

with tf.variable_scope("conv2"):
cnn1 = conv2d(input2, 16)


### Variable sharing

Before looking into variable sharing, we first describe how tf.Varaible works. tf.Variable always create a new variable even given the same name.

# tf.Variable always create new variable even given the same name.
v1 = tf.Variable(10, name="name1")
v2 = tf.Variable(10, name="name1")
assert(v1 is not v2)
print(v1.name)  # name1:0
print(v2.name)  # name1_1:0


If an operation named “name1” exist, the TensorFlow append “_1”, “_2” etc.. to the name to make it unique.

So calling the affine method twice below, we create 2 sets of W and b, i.e. 2 affine layers with their own set of W & b.

def affine(x, shape):
W = tf.Variable(tf.truncated_normal(shape))
b = tf.Variable(tf.zeros([shape[1]]))

model = tf.nn.relu(tf.matmul(x, W) + b)
return model

x = tf.placeholder(tf.float32, [None, 784])
with tf.variable_scope("n1"):
n1 = affine(x, [784, 500])

with tf.variable_scope("n1"):
n2 = affine(x, [784, 500])


Sometimes, in a complex model, we want to share a common layer or parameters. How can we have a affine method similar to the code above but share the same W & b.

def affine_reuseable(x, shape):
W = tf.get_variable("W", shape,
initializer=tf.random_normal_initializer())
b = tf.get_variable("b", [shape[1]],
initializer=tf.constant_initializer(0.0))
model = tf.nn.relu(tf.matmul(x, W) + b)
return model

nx = tf.placeholder(tf.float32, [None, 784])
with tf.variable_scope("n2"):
nn1 = affine_reuseable(x, [784, 500])

with tf.variable_scope("n2", reuse=True):
nn2 = affine_reuseable(x, [784, 500])


If a variable with the give “scope/name” exists, tf.get_variable returns the existing variable instead of creating one.

W = tf.get_variable("W", shape, initializer=tf.random_normal_initializer())


So for the second affine_reuseable call below, tf.get_variable reuses the W & b variables created before.

with tf.variable_scope("n2", reuse=True):
nn2 = affine_reuseable(x, [784, 500])


#### Reuse

However, TensorFlow wants the developer to be self-aware of whether the variable exists or not. Developers need to have the correct setting for the “reuse” flag before calling tf.get_variable. Both scenarios below will throw an exception when calling tf.get_variable:

• if the reuse flag is False or None (default) and the variable already exists.
• if the reuse flag is True and the variable does not exist.

Do NOT do this

with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
v1 = tf.get_variable("v")
# Raises ValueError("... v already exists ...").

with tf.variable_scope("foo", reuse=True):
v = tf.get_variable("v")
# Raises ValueError("... v does not exists ...").


Instead set the reuse flag probably.

with tf.variable_scope("foo"):
v = tf.get_variable("v2", [1]) # Create a new variable.

with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v2")  # reuse/share the variable "foo/v2".
assert v1 == v

with tf.variable_scope("foo") as scope:
v = tf.get_variable("v3", [1])
scope.reuse_variables()
v1 = tf.get_variable("v3")
assert v1 == v


We can reuse scope instead of supplying the scope name again:

with tf.variable_scope("model") as scope:
output1 = my_image_filter(input1)
with tf.variable_scope(scope, reuse=True):  # Can use scope instead of "model"
output2 = my_image_filter(input2)


#### Nested scope

with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"


### Caveat of variable sharing

Most developers are familiar with tf.name_scope and tf.Variables methods. However, these APIs are NOT for shared variables. For example, tf.get_variable below does not pick up the name scope created from tf.name_scope.

with tf.name_scope("foo1"):
v1 = tf.get_variable("v", [1])
v2 = tf.Variable(1, name="v2")

with tf.variable_scope("foo2"):
v3 = tf.get_variable("v", [1])
v4 = tf.Variable(1, name="v2")

print(v1.name)  # v:0 (Unexpected!)
print(v2.name)  # foo1/v2:0
print(v3.name)  # foo2/v:0
print(v4.name)  # foo2/v2:0


The best way to avoid nasty issues with shared variables are

• Do NOT use tf.name_scope and tf.Variables with shareable variables.
• Always use tf.variable_scope to define the scope of a shared variable.
• Use tf.get_varaible to create or retrieve a shared variable.
with tf.variable_scope("foo"):
v = tf.get_variable("v2", [1])    # Create a new variable

with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v2")        # Reuse a variable created before.


### Assignment

v = tf.get_variable("v", shape=(), initializer=tf.zeros_initializer())