dtype: float32 shape: 1, 3 Tensor [[1, 2, 3], ] Tensor [[5, 8, 10], ]
That example is indeed not very illustrative. The important part is that
assign saves the given value to the variable within the session, so you can use it later in the next call to
run. See here:
import tensorflow as tf v1 = tf.get_variable("v1", shape = , initializer = tf.zeros_initializer) inc_v1 = v1.assign(v1 + 1) init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) sess.run(inc_v1) print(sess.run(v1)) #[1. 1. 1.] sess.run(inc_v1) print(sess.run(v1)) #[2. 2. 2.]
v1 saves the assigned value, so in further calls to
run it can be used. Compare now to:
import tensorflow as tf v1 = tf.get_variable("v1", shape = , initializer = tf.zeros_initializer) inc_v1 = v1 + 1 init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) sess.run(inc_v1) print(sess.run(v1)) #[0. 0. 0.] sess.run(inc_v1) print(sess.run(v1)) #[0. 0. 0.]
Last updated 2022-04-27 UTC.
This notebook discusses variable placement. If you want to see on what device your variables are placed, uncomment this line.
import tensorflow as tf # Uncomment to see where your variables get placed(see below) # tf.debugging.set_log_device_placement(True)
To create a variable, provide an initial value. The
tf.Variable will have the same
dtype as the initialization value.
my_tensor = tf.constant([ [1.0, 2.0], [3.0, 4.0] ]) my_variable = tf.Variable(my_tensor) # Variables can be all kinds of types, just like tensors bool_variable = tf.Variable([False, False, False, True]) complex_variable = tf.Variable([5 + 4 j, 6 + 1 j])
A variable looks and acts like a tensor, and, in fact, is a data structure backed by a
tf.Tensor. Like tensors, they have a
dtype and a shape, and can be exported to NumPy.
print("Shape: ", my_variable.shape) print("DType: ", my_variable.dtype) print("As NumPy: ", my_variable.numpy())
Most tensor operations work on variables as expected, although variables cannot be reshaped.
print("A variable:", my_variable) print("\nViewed as a tensor:", tf.convert_to_tensor(my_variable)) print("\nIndex of highest value:", tf.math.argmax(my_variable)) # This creates a new tensor; it does not reshape the variable. print("\nCopying and reshaping: ", tf.reshape(my_variable, [1, 4]))
A variable: <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy=array([[1., 2.], [3., 4.]], dtype=float32)> Viewed as a tensor: tf.Tensor( [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float32) Index of highest value: tf.Tensor([1 1], shape=(2,), dtype=int64) Copying and reshaping: tf.Tensor([[1. 2. 3. 4.]], shape=(1, 4), dtype=float32)
Now that you know how to create, initialize, and save a variable in TensorFlow in Python, I will tell you how to assign the values.,First of all, you shall know how to declare a variable in TensorFlow Python. While creating a variable you need to pass a Tensor as variable store these tensors as their initial value. Here is an example:,We use the variable_name.assign() function TensorFlow to assign a value to variable.,Today in this tutorial, we will learn about how to assign a value to a variable in TensorFlow with Python code example. Variables in a model are those that hold and update the parameters during the execution of the program/code.
First of all, you shall know how to declare a variable in TensorFlow Python. While creating a variable you need to pass a Tensor as variable store these tensors as their initial value.
Here is an example:
biases = tf.Variable(tf.zeros(), name = "biases")
Then the step is to initialize the variable. To do so we will use the tf.initialize_all_variables() function. This step is essential as it is important that all the variables are initialized and ready to use before other operations are executed.
Let’s see through an example:
biases = tf.Variable(tf.zeros(), name = "biases") # Initialize the variables. init_op = tf.initialize_all_variables() # Later, when launching the model with tf.Session() as session: # Run the init operation. session.run(init_op) # Can use the model now
The next step will be saving the variable you have created in order to make sure that you can use them in the future. An easy way to do so to use the tf.train.Saver() to create a saver.
v1 = tf.Variable(..., name = "v1") # Initialize the variables. init_op = tf.initialize_all_variables() # Save the variables. saver = tf.train.Saver()
So how do we do it in TensorFlow 2.0? Well, it is not that much of a difference for this, let’s see how:
import tensorflow as tf import numpy as np x = tf.Variable(0) init = tf.initialize_all_variables() session = tf.InteractiveSession() session.run(init) print(x.numpy()) x.assign(1) print(x.numpy())
This variable functions similar to a Python variable, where you can assign or change the value of the variable.,I hope, you have understood the procedure of Assigning a value to a TensorFlow variable.,Now we’ll define the tf.Variable class and assign the variable an initial value, and, we will also give it a name.,After assigning a new value to state, and storing it in a new variable named ‘update’. We’ll be now getting our tf.Variable into action.
Let’s start by importing TensorFlow in Python.
import tensorflow as tf
- Now we’ll define the
tf.Variableclass and assign the variable an initial value, and, we will also give it a name.
- Let’s name it as ‘number’.
state = tf.Variable(0, name = 'number') print(state.name)
state = tf.Variable(0,name='number') print(state.name)
Your Output will be: number = 0
Let’s run this code and see the output:
AttributeError Traceback (most recent call last) <ipython-input-2-2c272fa2e037> in <module> 4 one = tf.constant(1) 5 new_value= tf.add(state,one) ----> 6 update=tf.assign(state,new_value) AttributeError: module 'tensorflow' has no attribute 'assign'
Let’s Try it.
import tensorflow.compat.v1 as tf tf.disable_v2_behavior()