Weird layer suffix issue running in Jupyter Notebook The Next CEO of Stack OverflowKeras - All layer names should be uniquewrap a general python function in tensorflowError when checking model target: expected dense_2 to have shape (None, 29430) but got array with shape (1108, 1)Getting the output of layer as a feature vector (KERAS)Keras Sequential model input layerWhat is the role of TimeDistributed layer in Keras?How does Keras read input data?Keras Model - Functional API - adding layers to existing modelKerras the definition of a model changes when the input tensor of the model is the output of another modelUse Glove vectors without Embedding layers in LSTMKeras functional api gives error “expected ndim=3, found ndim=4”
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Weird layer suffix issue running in Jupyter Notebook
The Next CEO of Stack OverflowKeras - All layer names should be uniquewrap a general python function in tensorflowError when checking model target: expected dense_2 to have shape (None, 29430) but got array with shape (1108, 1)Getting the output of layer as a feature vector (KERAS)Keras Sequential model input layerWhat is the role of TimeDistributed layer in Keras?How does Keras read input data?Keras Model - Functional API - adding layers to existing modelKerras the definition of a model changes when the input tensor of the model is the output of another modelUse Glove vectors without Embedding layers in LSTMKeras functional api gives error “expected ndim=3, found ndim=4”
from keras import layers as KL
def create_model():
inp = KL.Input(shape=(None,), name='input')
embedding = KL.Embedding(input_dim=10, output_dim=10)(inp)
out = KL.Dense(1, activation='sigmoid', name='dense')(embedding)
model = KM.Model(inputs=[inp], outputs=[out])
return model
model1 = create_model()
model1.summary()
model2 = create_model()
model2.summary()
The output for model1:
embedding_1 (Embedding)
model2:
embedding_2 (Embedding)
Why the name of the layer is not fixed? If I run create_model() again, the name will be suffixed with _3.
Any idea? Does this has anything to do with running in Jupyter? Does Jupyter kernel somehow cache the variables? Thanks!
keras jupyter
edited Mar 7 at 15:46
desertnaut
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
add a comment |
from keras import layers as KL
def create_model():
inp = KL.Input(shape=(None,), name='input')
embedding = KL.Embedding(input_dim=10, output_dim=10)(inp)
out = KL.Dense(1, activation='sigmoid', name='dense')(embedding)
model = KM.Model(inputs=[inp], outputs=[out])
return model
model1 = create_model()
model1.summary()
model2 = create_model()
model2.summary()
The output for model1:
embedding_1 (Embedding)
model2:
embedding_2 (Embedding)
Why the name of the layer is not fixed? If I run create_model() again, the name will be suffixed with _3.
Any idea? Does this has anything to do with running in Jupyter? Does Jupyter kernel somehow cache the variables? Thanks!
keras jupyter
edited Mar 7 at 15:46
desertnaut
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
1
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Does Keras maintain the layer names globally?create_model()defines a new model every time it is called.
– zs2020
Mar 7 at 15:47
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56
add a comment |
from keras import layers as KL
def create_model():
inp = KL.Input(shape=(None,), name='input')
embedding = KL.Embedding(input_dim=10, output_dim=10)(inp)
out = KL.Dense(1, activation='sigmoid', name='dense')(embedding)
model = KM.Model(inputs=[inp], outputs=[out])
return model
model1 = create_model()
model1.summary()
model2 = create_model()
model2.summary()
The output for model1:
embedding_1 (Embedding)
model2:
embedding_2 (Embedding)
Why the name of the layer is not fixed? If I run create_model() again, the name will be suffixed with _3.
Any idea? Does this has anything to do with running in Jupyter? Does Jupyter kernel somehow cache the variables? Thanks!
keras jupyter
edited Mar 7 at 15:46
desertnaut
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
from keras import layers as KL
def create_model():
inp = KL.Input(shape=(None,), name='input')
embedding = KL.Embedding(input_dim=10, output_dim=10)(inp)
out = KL.Dense(1, activation='sigmoid', name='dense')(embedding)
model = KM.Model(inputs=[inp], outputs=[out])
return model
model1 = create_model()
model1.summary()
model2 = create_model()
model2.summary()
The output for model1:
embedding_1 (Embedding)
model2:
embedding_2 (Embedding)
Why the name of the layer is not fixed? If I run create_model() again, the name will be suffixed with _3.
Any idea? Does this has anything to do with running in Jupyter? Does Jupyter kernel somehow cache the variables? Thanks!
keras jupyter
keras jupyter
edited Mar 7 at 15:46
desertnaut
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
edited Mar 7 at 15:46
desertnaut
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
edited Mar 7 at 15:46
desertnaut
20.4k74379
edited Mar 7 at 15:46
desertnaut
20.4k74379
edited Mar 7 at 15:46
desertnaut
20.4k74379
20.4k74379
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
asked Mar 7 at 15:39
zs2020zs2020
45.2k23133194
45.2k23133194
1
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Does Keras maintain the layer names globally?create_model()defines a new model every time it is called.
– zs2020
Mar 7 at 15:47
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56
add a comment |
1
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Does Keras maintain the layer names globally?create_model()defines a new model every time it is called.
– zs2020
Mar 7 at 15:47
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56
1
1
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Does Keras maintain the layer names globally?
create_model() defines a new model every time it is called.– zs2020
Mar 7 at 15:47
Does Keras maintain the layer names globally?
create_model() defines a new model every time it is called.– zs2020
Mar 7 at 15:47
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56
add a comment |
1 Answer
1
active
oldest
votes
Each layer has a parameter called name, which sets the layer name. You can use this to put your own fixed names to layers, so you can operate on them later.
For example:
conv1 = Conv2D(..., name='conv1')(some_input)
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
This won't work. The name will beembedding_1/embeddingorembedding_2/embeddingafter ported as Tensorflow format
– zs2020
Mar 7 at 15:59
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Each layer has a parameter called name, which sets the layer name. You can use this to put your own fixed names to layers, so you can operate on them later.
For example:
conv1 = Conv2D(..., name='conv1')(some_input)
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
This won't work. The name will beembedding_1/embeddingorembedding_2/embeddingafter ported as Tensorflow format
– zs2020
Mar 7 at 15:59
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
add a comment |
Each layer has a parameter called name, which sets the layer name. You can use this to put your own fixed names to layers, so you can operate on them later.
For example:
conv1 = Conv2D(..., name='conv1')(some_input)
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
This won't work. The name will beembedding_1/embeddingorembedding_2/embeddingafter ported as Tensorflow format
– zs2020
Mar 7 at 15:59
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
add a comment |
Each layer has a parameter called name, which sets the layer name. You can use this to put your own fixed names to layers, so you can operate on them later.
For example:
conv1 = Conv2D(..., name='conv1')(some_input)
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
Each layer has a parameter called name, which sets the layer name. You can use this to put your own fixed names to layers, so you can operate on them later.
For example:
conv1 = Conv2D(..., name='conv1')(some_input)
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
answered Mar 7 at 15:54
Matias ValdenegroMatias Valdenegro
32.2k45681
32.2k45681
This won't work. The name will beembedding_1/embeddingorembedding_2/embeddingafter ported as Tensorflow format
– zs2020
Mar 7 at 15:59
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
add a comment |
This won't work. The name will beembedding_1/embeddingorembedding_2/embeddingafter ported as Tensorflow format
– zs2020
Mar 7 at 15:59
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
This won't work. The name will be
embedding_1/embedding or embedding_2/embedding after ported as Tensorflow format– zs2020
Mar 7 at 15:59
This won't work. The name will be
embedding_1/embedding or embedding_2/embedding after ported as Tensorflow format– zs2020
Mar 7 at 15:59
1
1
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
@zsong I get the feeling that we have a XY problem here, because you don't explain what exactly is the problem, but you are trying to make your solution work. Please state the full problem.
– Matias Valdenegro
Mar 7 at 16:04
add a comment |
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1
Keras does this, because layer names have to be unique.
– Matias Valdenegro
Mar 7 at 15:45
Does Keras maintain the layer names globally?
create_model()defines a new model every time it is called.– zs2020
Mar 7 at 15:47
It creates them so they are globally unique but they don't have to. Note that this has nothing to do with variable names.
– Matias Valdenegro
Mar 7 at 15:49
Is there a way to reset the counter? I need the layer names to be fixed because I need to port the model as tensforflow format and use it in C#. If the name of the layer is changed every time when the model is trained, I have to update the code.
– zs2020
Mar 7 at 15:52
Possibly of help: Keras - All layer names should be unique
– desertnaut
Mar 7 at 15:56