RuntimeError: Unable to create link (name already exists) Keras
Question:
When I save my model I get the following error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-40-853303da8647> in <module>()
7
8
----> 9 model.save(outdir+'model.h5')
10
11
5 frames
/usr/local/lib/python3.6/dist-packages/h5py/_hl/group.py in __setitem__(self, name, obj)
371
372 if isinstance(obj, HLObject):
--> 373 h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
374
375 elif isinstance(obj, SoftLink):
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/h5o.pyx in h5py.h5o.link()
RuntimeError: Unable to create link (name already exists)
This does not happen when I use built-in layers to build my model or others user defined layers. This error arises only when I use this particular user defined layer:
class MergeTwo(keras.layers.Layer):
def __init__(self, nout, **kwargs):
super(MergeTwo, self).__init__(**kwargs)
self.nout = nout
self.alpha = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True)
self.beta = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True)
def call(self, inputs):
A, B = inputs
result = keras.layers.add([self.alpha*A ,self.beta*B])
result = keras.activations.tanh(result)
return result
def get_config(self):
config = super(MergeTwo, self).get_config()
config['nout'] = self.nout
return config
I read the Docs but nothing worked, I cannot figure out why.
I am using Google Colab and Tensorflow version 2.2.0
Answers:
I think the problem is that both of your weight variables have internally the same name, which should not happen, you can give them names with the name parameter to add_weight:
self.alpha = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True, name="alpha")
self.beta = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True, name="beta")
This should workaround the problem.
I found another solution, although from a different scenario.
I was using Keras-tuner to do some hyper parameter tuning, and when building (E.G 4 models), the layers of the models would have the same name, for each of the models.
As i was testing depth of the network as a parameter, i would have multiple lstm,lstm_1, and dense layers in my group. An example of a single model is shown below for context.
Layer (type) Output Shape Param#
=================================================================
lstm (LSTM) (None, 12, 320) 536320
_________________________________________________________________
lstm_1 (LSTM) (None, 12, 64) 98560
_________________________________________________________________
dense (Dense) (None, 12, 1) 65
I found that changing the name of the layer using the name parameter, so something unique, meant that i wouldn’t get this error.
Example for illustration purposes:
unique_id = random.randint(1,99999999) # quite unlike
model.add(LSTM(64, name="my_layer_name_{}".format(unique_id)))
When adding the unique_id, onto the custom name, i ensured that each layer for each model in my keras-tuner group would have unique layers.
In your case, you have a single model, however, as the layers are custom, i’m not quite sure how i would name them (given the current format they have).
Can you check if this is the case ?
Addition to the accepted answer
You may encounter the exact same error even without custom defined weights or layers. In jupyter notebooks, after re-compiling a loaded checkpoint, it can happen, depending on the optimizer, that the optimizers weights has duplicate names. To get rid of the bad state without losing your trained model:
model.save("model.h5", include_optimizer=False)
# Restart kernel
model = load_model("model.h5")
model.compile(**args)
Leaving this here not as strict answer to the question but as a broader hint. Merely, because this thread was the first that popped up when i searched for the problem.
When I save my model I get the following error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-40-853303da8647> in <module>()
7
8
----> 9 model.save(outdir+'model.h5')
10
11
5 frames
/usr/local/lib/python3.6/dist-packages/h5py/_hl/group.py in __setitem__(self, name, obj)
371
372 if isinstance(obj, HLObject):
--> 373 h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
374
375 elif isinstance(obj, SoftLink):
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/h5o.pyx in h5py.h5o.link()
RuntimeError: Unable to create link (name already exists)
This does not happen when I use built-in layers to build my model or others user defined layers. This error arises only when I use this particular user defined layer:
class MergeTwo(keras.layers.Layer):
def __init__(self, nout, **kwargs):
super(MergeTwo, self).__init__(**kwargs)
self.nout = nout
self.alpha = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True)
self.beta = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True)
def call(self, inputs):
A, B = inputs
result = keras.layers.add([self.alpha*A ,self.beta*B])
result = keras.activations.tanh(result)
return result
def get_config(self):
config = super(MergeTwo, self).get_config()
config['nout'] = self.nout
return config
I read the Docs but nothing worked, I cannot figure out why.
I am using Google Colab and Tensorflow version 2.2.0
I think the problem is that both of your weight variables have internally the same name, which should not happen, you can give them names with the name parameter to add_weight:
self.alpha = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True, name="alpha")
self.beta = self.add_weight(shape=(self.nout,), initializer='zeros',
trainable=True, name="beta")
This should workaround the problem.
I found another solution, although from a different scenario.
I was using Keras-tuner to do some hyper parameter tuning, and when building (E.G 4 models), the layers of the models would have the same name, for each of the models.
As i was testing depth of the network as a parameter, i would have multiple lstm,lstm_1, and dense layers in my group. An example of a single model is shown below for context.
Layer (type) Output Shape Param#
=================================================================
lstm (LSTM) (None, 12, 320) 536320
_________________________________________________________________
lstm_1 (LSTM) (None, 12, 64) 98560
_________________________________________________________________
dense (Dense) (None, 12, 1) 65
I found that changing the name of the layer using the name parameter, so something unique, meant that i wouldn’t get this error.
Example for illustration purposes:
unique_id = random.randint(1,99999999) # quite unlike
model.add(LSTM(64, name="my_layer_name_{}".format(unique_id)))
When adding the unique_id, onto the custom name, i ensured that each layer for each model in my keras-tuner group would have unique layers.
In your case, you have a single model, however, as the layers are custom, i’m not quite sure how i would name them (given the current format they have).
Can you check if this is the case ?
Addition to the accepted answer
You may encounter the exact same error even without custom defined weights or layers. In jupyter notebooks, after re-compiling a loaded checkpoint, it can happen, depending on the optimizer, that the optimizers weights has duplicate names. To get rid of the bad state without losing your trained model:
model.save("model.h5", include_optimizer=False)
# Restart kernel
model = load_model("model.h5")
model.compile(**args)
Leaving this here not as strict answer to the question but as a broader hint. Merely, because this thread was the first that popped up when i searched for the problem.