TensorFlow 1 version | View source on GitHub |
Enables / disables eager execution of tf.function
s.
tf.config.experimental_run_functions_eagerly(
run_eagerly
)
Calling tf.config.experimental_run_functions_eagerly(True)
will make all
invocations of tf.function
run eagerly instead of running as a traced graph
function.
This can be useful for debugging or profiling. For example, let's say you
implemented a simple iterative sqrt function, and you want to collect the
intermediate values and plot the convergence. Appending the values to a list
in @tf.function
normally wouldn't work since it will just record the Tensors
being traced, not the values. Instead, you can do the following.
ys = []
@tf.function
def sqrt(x):
y = x / 2
d = y
for _ in range(10):
d /= 2
if y * y < x:
y += d
else:
y -= d
ys.append(y.numpy())
return y
tf.config.experimental_run_functions_eagerly(True)
sqrt(tf.constant(2.))
<tf.Tensor: shape=(), dtype=float32, numpy=1.4150391>
ys
[1.5, 1.25, 1.375, 1.4375, 1.40625, 1.421875, 1.4140625, 1.4179688, 1.4160156,
1.4150391]
tf.config.experimental_run_functions_eagerly(False)
Calling tf.config.experimental_run_functions_eagerly(False)
will undo this
behavior.
Args | |
---|---|
run_eagerly
|
Boolean. Whether to run functions eagerly. |