Printing Values of TensorFlow Tensors: A Comprehensive Guide
In TensorFlow, a Tensor object represents a multidimensional array of data. To access the actual values stored within a Tensor, you'll need to evaluate it within a Session.
Session.run() Method
The most straightforward approach is to use the Session.run() method to evaluate the Tensor and retrieve its value:
import tensorflow as tf sess = tf.Session() matrix1 = tf.constant([[3., 3.]]) matrix2 = tf.constant([[2.],[2.]]) product = tf.matmul(matrix1, matrix2) print(sess.run(product))
This will print the value of the Tensor as a NumPy array.
Tensor.eval() Method
You can also use the Tensor.eval() method to evaluate a Tensor within the default Session:
with tf.Session():
print(product.eval())
Interactive Session
For a more convenient approach, you can use the tf.InteractiveSession to open a default Session for your entire program:
import tensorflow as tf tf.InteractiveSession() matrix1 = tf.constant([[3., 3.]]) matrix2 = tf.constant([[2.],[2.]]) product = tf.matmul(matrix1, matrix2) print(product.eval())
Notes
- For efficiency, TensorFlow separates the definition of computations (building the dataflow graph) from the execution (evaluating the graph and producing values).
- The tf.print() operator can also be used to print the value of a Tensor, but this requires manual execution with Session.run().
- The tf.get_static_value() function can be used to get the constant value of a Tensor if it can be calculated efficiently.
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