Declaring and Initializing Variable Tensors[edit | edit source]
Variable tensors are used when the values require updating within a session. It is the type of tensor that would be used for the weights matrix when creating neural networks, since these values will be updated as the model is being trained.
Declaring a variable tensor can be done using the
tf.get_variable()function. It is recommended to use
tf.get_variable, as it offers more flexibility eg:
# Declare a 2 by 3 tensor populated by ones a = tf.Variable(tf.ones([2,3], dtype=tf.float32)) a = tf.get_variable('a', shape=[2, 3], initializer=tf.constant_initializer(1))
Something to note is that declaring a variable tensor does not automatically initialize the values. The values need to be intialized explicitly when starting a session using one of the following:
The following example shows the full process of declaring and initializing a variable tensor.
# Build a graph graph = tf.Graph() with graph.as_default(): a = tf.get_variable('a', shape=[2,3], initializer=tf.constant_initializer(1), dtype=tf.float32)) # Create a variable tensor # Create a session, and run the graph with tf.Session(graph=graph) as session: tf.global_variables_initializer().run() # Initialize values of all variable tensors output_a = session.run(a) # Return the value of the variable tensor print(output_a) # Print this value
Which prints out the following:
[[ 1. 1. 1.] [ 1. 1. 1.]]
Fetch the value of a TensorFlow variable or a Tensor[edit | edit source]
Sometimes we need to fetch and print the value of a TensorFlow variable to guarantee our program is correct.
For example, if we have the following program:
import tensorflow as tf import numpy as np a = tf.Variable(tf.random_normal([2,3])) # declare a tensorflow variable b = tf.random_normal([2,2]) #declare a tensorflow tensor init = tf.initialize_all_variables()
if we want to get the value of a or b, the following procedures can be used:
with tf.Session() as sess: sess.run(init) a_value = sess.run(a) b_value = sess.run(b) print a_value print b_value
with tf.Session() as sess: sess.run(init) a_value = a.eval() b_value = b.eval() print a_value print b_value
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