When working with the default global graph, is it possible to remove nodes after they\'ve been added, or alternatively to reset the default graph to empty? When working with
Tensorflow 2.0 Compatible Answer: In Tensorflow Version >= 2.0
, the Command to Reset Entire Default Graph, when run in Graph Mode is tf.compat.v1.reset_default_graph
.
NOTE: The default graph is a property of the current thread. This function applies only to the current thread. Calling this function while a tf.compat.v1.Session
or tf.compat.v1.InteractiveSession
is active will result in undefined behavior. Using any previously created tf.Operation
or tf.Tensor
objects after calling this function will result in undefined behavior.
Raises: AssertionError: If this function is called within a nested graph.
Not sure if I faced the very same problem, but
tf.keras.backend.clear_session()
at the beginning of the cell in which the model (Keras, in my case) was constructed and trained helped to "cut the clutter" so only the current graph remains in the TensorBoard visualization after repeated runs of the same cell.
Environment: TensorFlow 2.0 (tensorflow-gpu==2.0.0b1
) in Colab with built-in TensorBoard (using the %load_ext tensorboard
trick).
By default, a session is constructed around the default graph. To avoid leaving dead nodes in the session, you need to either control the default graph or use an explicit graph.
To clear the default graph, you can use the tf.reset_default_graph function.
tf.reset_default_graph()
sess = tf.InteractiveSession()
You can also construct explicitly a graph and avoid using the default one. If you use a normal Session
, you will need to fully create the graph before constructing the session. For InteractiveSession
, you can just declare the graph and use it as a context to declare further changes:
g = tf.Graph()
sess = tf.InteractiveSession(graph=g)
with g.asdefault():
# Put variable declaration and other tf operation
# in the graph context
....
b = tf.matmul(A, x)
....
sess.run([b], ...)
EDIT: For recent versions of tensorflow
(1.0+), the correct function is g.as_default.
Update 11/2/2016
tf.reset_default_graph()
Old stuff
There's reset_default_graph
, but not part of public API (I think it should be, does someone wants to file an issue on GitHub?)
My work-around to reset things is this:
from tensorflow.python.framework import ops
ops.reset_default_graph()
sess = tf.InteractiveSession()
IPython / Jupyter notebook cells keep state between runs of a cell.
Create a custom graph:
def main():
# Define your model
data = tf.placeholder(...)
model = ...
with tf.Graph().as_default():
main()
Once ran, the graph is cleaned up.