NotImplementedError: Layers with arguments in `__init__` must override `get_config`
Question:
I’m trying to save my TensorFlow model using model.save(), however – I am getting this error.
The model summary is provided here:
Model Summary
The code for the transformer model:
def transformer(vocab_size, num_layers, units, d_model, num_heads, dropout, name="transformer"):
inputs = tf.keras.Input(shape=(None,), name="inputs")
dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")
enc_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='enc_padding_mask')(inputs)
# mask the future tokens for decoder inputs at the 1st attention block
look_ahead_mask = tf.keras.layers.Lambda(
create_look_ahead_mask,
output_shape=(1, None, None),
name='look_ahead_mask')(dec_inputs)
# mask the encoder outputs for the 2nd attention block
dec_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='dec_padding_mask')(inputs)
enc_outputs = encoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[inputs, enc_padding_mask])
dec_outputs = decoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])
outputs = tf.keras.layers.Dense(units=vocab_size, name="outputs")(dec_outputs)
return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)
I don’t understand why it’s giving this error since the model trains perfectly fine.
Any help would be appreciated.
My saving code for reference:
print("Saving the model.")
saveloc = "C:/tmp/solar.h5"
model.save(saveloc)
print("Model saved to: " + saveloc + " succesfully.")
Answers:
It’s not a bug, it’s a feature.
This error lets you know that TF can’t save your model, because it won’t be able to load it.
Specifically, it won’t be able to reinstantiate your custom Layer classes: encoder and decoder.
To solve this, just override their get_config method according to the new arguments you’ve added.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
For example, if your encoder class looks something like this:
class encoder(tf.keras.layers.Layer):
def __init__(
self,
vocab_size, num_layers, units, d_model, num_heads, dropout,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.num_layers = num_layers
self.units = units
self.d_model = d_model
self.num_heads = num_heads
self.dropout = dropout
# Other methods etc.
then you only need to override this method:
def get_config(self):
config = super().get_config().copy()
config.update({
'vocab_size': self.vocab_size,
'num_layers': self.num_layers,
'units': self.units,
'd_model': self.d_model,
'num_heads': self.num_heads,
'dropout': self.dropout,
})
return config
When TF sees this (for both classes), you will be able to save the model.
Because now when the model is loaded, TF will be able to reinstantiate the same layer from config.
Layer.from_config‘s source code may give a better sense of how it works:
@classmethod
def from_config(cls, config):
return cls(**config)
I think simple solution is to install the tensorflow==2.4.2 for gpu tensorflow-gpu==2.4.2 , i faced the issue and debug the whole day but it was not resolved. finally i installed the older stable version and error is gone
This problem is caused by mixing imports between the keras and tf.keras libraries, which is not supported.
Use tf.keras.models or usr keras.models everywhere
You should never mix imports between these libraries, as it will not work and produces all kinds of strange error messages. These errors change with versions of keras and tensorflow.
I suggest You try the following:
model = tf.keras.Model(...)
model.save_weights("some_path")
...
model.load_weights("some_path")
I’m trying to save my TensorFlow model using model.save(), however – I am getting this error.
The model summary is provided here:
Model Summary
The code for the transformer model:
def transformer(vocab_size, num_layers, units, d_model, num_heads, dropout, name="transformer"):
inputs = tf.keras.Input(shape=(None,), name="inputs")
dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")
enc_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='enc_padding_mask')(inputs)
# mask the future tokens for decoder inputs at the 1st attention block
look_ahead_mask = tf.keras.layers.Lambda(
create_look_ahead_mask,
output_shape=(1, None, None),
name='look_ahead_mask')(dec_inputs)
# mask the encoder outputs for the 2nd attention block
dec_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='dec_padding_mask')(inputs)
enc_outputs = encoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[inputs, enc_padding_mask])
dec_outputs = decoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])
outputs = tf.keras.layers.Dense(units=vocab_size, name="outputs")(dec_outputs)
return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)
I don’t understand why it’s giving this error since the model trains perfectly fine.
Any help would be appreciated.
My saving code for reference:
print("Saving the model.")
saveloc = "C:/tmp/solar.h5"
model.save(saveloc)
print("Model saved to: " + saveloc + " succesfully.")
It’s not a bug, it’s a feature.
This error lets you know that TF can’t save your model, because it won’t be able to load it.
Specifically, it won’t be able to reinstantiate your custom Layer classes: encoder and decoder.
To solve this, just override their get_config method according to the new arguments you’ve added.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
For example, if your encoder class looks something like this:
class encoder(tf.keras.layers.Layer):
def __init__(
self,
vocab_size, num_layers, units, d_model, num_heads, dropout,
**kwargs,
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.num_layers = num_layers
self.units = units
self.d_model = d_model
self.num_heads = num_heads
self.dropout = dropout
# Other methods etc.
then you only need to override this method:
def get_config(self):
config = super().get_config().copy()
config.update({
'vocab_size': self.vocab_size,
'num_layers': self.num_layers,
'units': self.units,
'd_model': self.d_model,
'num_heads': self.num_heads,
'dropout': self.dropout,
})
return config
When TF sees this (for both classes), you will be able to save the model.
Because now when the model is loaded, TF will be able to reinstantiate the same layer from config.
Layer.from_config‘s source code may give a better sense of how it works:
@classmethod
def from_config(cls, config):
return cls(**config)
I think simple solution is to install the tensorflow==2.4.2 for gpu tensorflow-gpu==2.4.2 , i faced the issue and debug the whole day but it was not resolved. finally i installed the older stable version and error is gone
This problem is caused by mixing imports between the keras and tf.keras libraries, which is not supported.
Use tf.keras.models or usr keras.models everywhere
You should never mix imports between these libraries, as it will not work and produces all kinds of strange error messages. These errors change with versions of keras and tensorflow.
I suggest You try the following:
model = tf.keras.Model(...)
model.save_weights("some_path")
...
model.load_weights("some_path")