作者：手机用户2602916141 | 来源：互联网 | 2023-09-09 18:19
InTensorFlowv2,belowcodecancauseGraphDefreconciliationerror.
In TensorFlow v2, below code can cause GraphDef reconciliation error.
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| py
.function
def foo(x):
return x ** 2
with writer.as_default():
tf.summary.trace_on()
foo(1)
foo(2)
tf.summary.trace_export("foo") |
Depending on the argument,
(really, auto-graph) creates ops that are unique within GraphDef but is not globally unique. In the example above, two GraphDefs (on from
and another from
) will be written out and they can collide badly in names and content.
In such case, instead of showing wrong graph content, TensorBoard throws an error.
该提问来源于开源项目：tensorflow/tensorboard
I had the same issue. Tensorboard needs unique names to be given to the graph variables (I don't why and I hope this issue will be fixed). In your case this piece of code should fix it:
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| import tensorflow as tf
.function
def foo(x):
return x ** 2
writer&＃061;tf.summary.create_file_writer(&＃039;logs\\&＃039;)
with writer.as_default():
tf.summary.trace_on()
foo(tf.Variable(1, name&＃061;&＃039;foo1&＃039;)) # define a unique name for the variable
foo(tf.Variable(2, name&＃061;&＃039;foo2&＃039;))
tf.summary.trace_export("foo", step&＃061;0) |
This issue also exists when overriding tf.Module. Then, self.name_scope (or tf.name_scope) can be used when defining the module variables (wrapping the other operations or not). Here is an example of a custom Dense layer:
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| import tensorflow as tf
import numpy as np
class Dense(tf.Module):
# Fully-connected layer.
def __init__(self, out_fmaps, name&＃061;None):
super().__init__(name&＃061;name)
self.is_built &＃061; False
self.out_fmaps &＃061; out_fmaps
def __call__(self, x):
if not self.is_built:
with self.name_scope: # Creates the variable under name_scope
he_init &＃061; np.sqrt(2/x.shape[-1])
init_val &＃061; tf.random.normal([x.shape[-1], self.out_fmaps])*he_init
self.w &＃061; tf.Variable(init_val, name&＃061;&＃039;dense&＃039;)
self.is_built &＃061; True
return tf.matmul(x, self.w) |
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