TensorFlow Variable vs get_variable: Key Differences Explained

• Introduction
• Step-by-Step Guide
• Code Example
• Additional Notes
• Summary
• Conclusion
• References

In TensorFlow, understanding the distinction between tf.Variable and tf.get_variable is crucial for effective variable management, especially in complex models. While both deal with creating variables, they offer different levels of control and are suited for distinct scenarios.

Let's break down the difference between tf.Variable and tf.get_variable in TensorFlow.

tf.Variable

• Think of it as directly creating a variable in Python.

  my_var = tf.Variable(1.0, name="my_var") 

• Each time you call tf.Variable, it creates a new variable, even if you use the same name.

tf.get_variable

• This is TensorFlow's way of managing variables, especially for weight sharing.

  my_var = tf.get_variable("my_var", [1], initializer=tf.zeros_initializer())

• If a variable with the given name ("my_var" here) exists within the current scope, it will reuse it.
• If it doesn't exist, it creates a new one.

Why is tf.get_variable useful?

• Weight Sharing: Imagine building a neural network where you want two layers to use the same weights. tf.get_variable makes this easy.
• Variable Scopes: TensorFlow uses scopes to organize variables. tf.get_variable interacts with these scopes to prevent accidental name collisions.

Key Points

• tf.get_variable is generally preferred for most use cases, especially in complex models.
• If you need absolute control over variable creation (like in very specific situations), you might use tf.Variable.

Example

import tensorflow as tf

with tf.compat.v1.variable_scope("my_scope"):
  v1 = tf.get_variable("my_var", [1], initializer=tf.zeros_initializer()) 
  v2 = tf.get_variable("my_var", [1], initializer=tf.zeros_initializer()) 
  # v2 reuses the variable created by v1

print(v1 is v2)  # This will print True 

This code demonstrates the difference between creating variables in TensorFlow using tf.Variable and tf.get_variable. It shows that tf.Variable creates new variables each time, while tf.get_variable allows for variable sharing within a scope. The code provides examples of both approaches and highlights the benefits of using tf.get_variable for weight sharing in neural networks.

import tensorflow as tf

# Using tf.Variable - Creates new variables each time
var1 = tf.Variable(2.0, name="my_var")
var2 = tf.Variable(3.0, name="my_var") 

print("var1:", var1.numpy())  # Output: 2.0
print("var2:", var2.numpy())  # Output: 3.0
print(var1 is var2)           # Output: False (Different variables)

# Using tf.get_variable within a scope - Enables variable sharing
with tf.compat.v1.variable_scope("my_scope"):
  get_var1 = tf.get_variable("shared_var", [1], initializer=tf.ones_initializer())
  get_var2 = tf.get_variable("shared_var", [1], initializer=tf.ones_initializer()) 

print("get_var1:", get_var1.numpy())  # Output: [1.]
print("get_var2:", get_var2.numpy())  # Output: [1.]
print(get_var1 is get_var2)         # Output: True (Same variable)

# Demonstrating weight sharing in a simple neural network layer
def simple_layer(x, scope_name):
  with tf.compat.v1.variable_scope(scope_name, reuse=tf.compat.v1.AUTO_REUSE):
    w = tf.get_variable("weights", [x.shape[1], 10], initializer=tf.random_normal_initializer())
    b = tf.get_variable("bias", [10], initializer=tf.zeros_initializer())
    return tf.matmul(x, w) + b

input_data = tf.ones((1, 5))
output1 = simple_layer(input_data, "layer1")
output2 = simple_layer(input_data, "layer1")  # Reuses weights from "layer1"

print("Output 1:", output1.numpy())
print("Output 2:", output2.numpy()) 

Explanation:

1. tf.Variable Example: We create var1 and var2 with the same name. They are different variables in memory, as shown by the is comparison.

2. tf.get_variable Example: Inside the my_scope, get_var1 and get_var2 refer to the same underlying variable. This is because tf.get_variable reuses an existing variable with the same name within the scope.

3. Weight Sharing Example: The simple_layer function demonstrates weight sharing. Both calls to simple_layer with the same scope name ("layer1") will use the same "weights" and "bias" variables.

Key Takeaways:

• Use tf.get_variable for managing variables, especially when you need weight sharing or are working within variable scopes.
• tf.Variable is used less often and might be suitable for very specific situations where you need direct control over variable creation.

Here are some extra points to clarify the differences and use cases:

Behavior:

• tf.Variable: Always creates a new variable in the current scope. If a variable with the same name already exists, it will raise an error (unless you're specifically using tf.compat.v1.Variable and set reuse=True).
• tf.get_variable: Prioritizes reuse. It will try to find an existing variable with the given name in the current scope. Only if it doesn't find one will it create a new variable.

Variable Scope Interaction:

• tf.Variable: Less integrated with variable scopes. While you can technically use it within a scope, it doesn't inherently leverage the scope's naming and reuse mechanisms as effectively as tf.get_variable.
• tf.get_variable: Designed to work seamlessly with variable scopes. It uses the scope to determine the full variable name (e.g., "scope_name/my_var") and to manage variable reuse.

TensorFlow 2.x Considerations:

• In TensorFlow 2.x and later, the recommended way to manage variables is through Keras layers and models. These abstractions handle variable creation and reuse automatically.
• tf.Variable is still available in TensorFlow 2.x, but you might use it less frequently.
• tf.get_variable is less common in TensorFlow 2.x, as Keras layers often handle variable management for you.

When to Choose What:

• tf.get_variable:
  • Weight sharing: Essential for situations where multiple parts of your model need to use the same variables (e.g., convolutional filters in CNNs).
  • Organized models: Helps structure your code by working within variable scopes, preventing name collisions and making it easier to manage variables.
• tf.Variable:
  • Fine-grained control: When you need very specific control over variable creation and don't need the features of tf.get_variable.
  • Legacy code: You might encounter it in older TensorFlow 1.x codebases.

Additional Tips:

• Variable Naming: Use descriptive names for your variables to improve code readability and maintainability.
• Variable Initialization: Always initialize your variables using appropriate initializers (e.g., tf.zeros_initializer(), tf.random_normal_initializer()) to ensure proper model training.
• Variable Scopes: Use variable scopes liberally to organize your variables and prevent naming conflicts, especially in complex models.

tf.Variable and tf.get_variable are fundamental for variable creation in TensorFlow, but they serve different purposes. tf.Variable provides explicit control, creating a new variable whenever called. Conversely, tf.get_variable enables variable sharing by reusing existing variables with the same name within a scope. This distinction is crucial for tasks like weight sharing in neural networks. While tf.Variable offers granular control, tf.get_variable is generally preferred for its ability to manage variables efficiently, especially in complex models. Understanding these differences is key to writing clean, organized, and efficient TensorFlow code.

• Difference between Variable and get_variable in TensorFlow ... | A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
• tensorflow - Am I using tf.get_variable() correctly? - Stack Overflow | Mar 16, 2017 ... It seems that you cannot change what already exists in a variable scope, thus only when you restart the kernel, you can change a variable ...
• Tensorflow get_variable into PyTorch - nlp - PyTorch Forums | Hi all, I am trying to convert this tensorflow code into pytorch. For example, I converted the below tensorflow code tf.get_variable("char_embeddings", [len(data.char_dict), data.char_embedding_size]), char_index) # [num_sentences, max_sentence_length, max_word_length, emb] into class CharEmbeddings(nn.Module): def init(self, config, data): .... self.embeddings = nn.init.xavier_uniform_(torch.empty(len(data.char_dict), data.char_embedding_size)) def fo...
• Different outcomes when using tf.Variable() and tf.get_variable ... | Oct 16, 2017 ... I'm trying to get familiar with TensorFlow framework from this site by playing around with Linear Regression (LR). The source code for LR can be ...
• tf.compat.v1.get_variable | TensorFlow v2.16.1 | Gets an existing variable with these parameters or create a new one.
• TensorFlow - Sharing Variables | get_variable() is used to get or create a variable instead of a direct call to tf.Variable . It uses an initializer instead of passing the value directly, as in ...
• What is the equivalent of tf.get_variable() in Pytorch? - PyTorch ... | I come from tensorflow and I know that tf.get_variable() (https://www.tensorflow.org/programmers_guide/variable_scope) is very useful because then I don’t have to have giant list of things were I define variables and instead they are included in the graph as I create more variables. i.e. I avoid: D,H_l1 = 2,1 w_init = torch.zeros(H_l1,D).type(dtype) W_l1 = Variable(w_init, requires_grad=True) D,H_l2 = 2,1 w_init = torch.zeros(H_l2,D).type(dtype) W_l2 = Variable(w_init, requires_grad=True) wha...
• get_variable style tf.Variable initialization · Issue #30366 · tensorflow ... | Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_t...
• Tensorflow, The Confusing Parts (1) | This post is the first of a series; click here for the next post. Click here to skip the intro and dive right in! Introduction What is this? Who are you? I’m Jacob, a Google AI Resident. When I started the residency program in the summer of 2017, I had...