I have been using the introductory example of matrix multiplication in TensorFlow.
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
produ
Please note that tf.Print()
will change the tensor name.
If the tensor you seek to print is a placeholder, feeding data to it will fail as the original name will not be found during feeding.
For example:
import tensorflow as tf
tens = tf.placeholder(tf.float32,[None,2],name="placeholder")
print(eval("tens"))
tens = tf.Print(tens,[tens, tf.shape(tens)],summarize=10,message="tens:")
print(eval("tens"))
res = tens + tens
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(sess.run(res))
Output is:
python test.py
Tensor("placeholder:0", shape=(?, 2), dtype=float32)
Tensor("Print:0", shape=(?, 2), dtype=float32)
Traceback (most recent call last):
[...]
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'placeholder' with dtype float