python - autoencoder.fit() raises 'KeyError: 'Exception encountered when calling Functional.call()' - St

I'm trying to build a stacked LSTM sequence auto-encoder that takes a signal of 430 timesteps with each timestep having 1 value. You can see the code for the model below:

feat_dim = 1
timesteps = 430
inputs = keras.Input(shape = (timesteps, feat_dim), dtype = 'float32')
x = layers.LSTM(320, activation = 'relu', return_sequences = True)(inputs)
x = layers.LSTM(256, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(128, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(64, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(32, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(16, activation = 'relu', return_sequences = True)(x)
encoded = layers.LSTM(encoding_dim, activation = 'relu', return_sequences = False)(x)

x = layers.RepeatVector(timesteps)(encoded)
x = layers.LSTM(16, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(32, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(64, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(128, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(256, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(320, activation = 'relu', return_sequences = True)(x)
decoded = layers.TimeDistributed(layers.Dense(feat_dim, activation = 'sigmoid'))(x)

autoencoder = keras.Model(input_dim, decoded)
encoder = keras.Model(input_dim, encoded)
encoded_input = keras.Input(shape=(timesteps, encoding_dim,))
decoded_layer = autoencoder.layers[-7](encoded_input)
decoded_layer = autoencoder.layers[-6](decoded_layer)
decoded_layer = autoencoder.layers[-5](decoded_layer)
decoded_layer = autoencoder.layers[-4](decoded_layer)
decoded_layer = autoencoder.layers[-3](decoded_layer)
# decoded_layer = autoencoder.layers[-2](decoded_layer)
decoded_layer = autoencoder.layers[-1](decoded_layer)
decoder = keras.Model(encoded_input, decoded)
autoencoderpile(optimizer = 'adam', loss = 'mean_squared_error')

autoencoder.fit(xtrainsc, xtrainsc,
               epochs = 50,
               batch_size = 128,
               shuffle = True,
               validation_data = (xtestsc, xtestsc))

When I run autoencoder.summary() I get the model summary just fine but when I run autoencoder.fit() I get the following error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-136-aff0d412291f> in <cell line: 1>()
----> 1 autoencoder.fit(xtrainsc, xtrainsc,
      2                epochs = 50,
      3                batch_size = 128,
      4                shuffle = True,
      5                validation_data = (xtestsc, xtestsc))

/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
    120             # To get the full stack trace, call:
    121             # `keras.config.disable_traceback_filtering()`
--> 122             raise e.with_traceback(filtered_tb) from None
    123         finally:
    124             del filtered_tb

/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py in _run_through_graph(self, inputs, operation_fn, call_fn)
    177         output_tensors = []
    178         for x in self.outputs:
--> 179             output_tensors.append(tensor_dict[id(x)])
    180 
    181         return tree.pack_sequence_as(self._outputs_struct, output_tensors)

KeyError: 'Exception encountered when calling Functional.call().\n\n\x1b[1m139922652929696\x1b[0m\n\nArguments received by Functional.call():\n  • inputs=tf.Tensor(shape=(None, 430, 1), dtype=float32)\n  • training=True\n  • mask=None'

I've reshaped my dataset already to (batch, timesteps, feature) and when I try the code below adapted from the original keras tutorial on building autoencoders, it works, just that the train and validation loss start giving NaN values after a number of epochs.

encoded = layers.LSTM(encoding_dim, activation = 'relu')(inputs)
x = layers.RepeatVector(timesteps)(encoded)
decoded = layers.LSTM(encoding_dim, activation = 'relu', return_sequences = True)(x)
decoded = layers.TimeDistributed(layers.Dense(feat_dim, activation = 'sigmoid'))(decoded)

autoencoder = keras.Model(inputs, decoded)
encoder = keras.Model(inputs, encoded)
encoded_input = keras.Input(shape=(timesteps, encoding_dim))
decoder = keras.Model(encoded_input, decoded)

Has anyone encountered a similar error and fixed it? I'd love to get some help on this. Thanks in advance.

I'm trying to build a stacked LSTM sequence auto-encoder that takes a signal of 430 timesteps with each timestep having 1 value. You can see the code for the model below:

feat_dim = 1
timesteps = 430
inputs = keras.Input(shape = (timesteps, feat_dim), dtype = 'float32')
x = layers.LSTM(320, activation = 'relu', return_sequences = True)(inputs)
x = layers.LSTM(256, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(128, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(64, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(32, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(16, activation = 'relu', return_sequences = True)(x)
encoded = layers.LSTM(encoding_dim, activation = 'relu', return_sequences = False)(x)

x = layers.RepeatVector(timesteps)(encoded)
x = layers.LSTM(16, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(32, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(64, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(128, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(256, activation = 'relu', return_sequences = True)(x)
x = layers.LSTM(320, activation = 'relu', return_sequences = True)(x)
decoded = layers.TimeDistributed(layers.Dense(feat_dim, activation = 'sigmoid'))(x)

autoencoder = keras.Model(input_dim, decoded)
encoder = keras.Model(input_dim, encoded)
encoded_input = keras.Input(shape=(timesteps, encoding_dim,))
decoded_layer = autoencoder.layers[-7](encoded_input)
decoded_layer = autoencoder.layers[-6](decoded_layer)
decoded_layer = autoencoder.layers[-5](decoded_layer)
decoded_layer = autoencoder.layers[-4](decoded_layer)
decoded_layer = autoencoder.layers[-3](decoded_layer)
# decoded_layer = autoencoder.layers[-2](decoded_layer)
decoded_layer = autoencoder.layers[-1](decoded_layer)
decoder = keras.Model(encoded_input, decoded)
autoencoder.compile(optimizer = 'adam', loss = 'mean_squared_error')

autoencoder.fit(xtrainsc, xtrainsc,
               epochs = 50,
               batch_size = 128,
               shuffle = True,
               validation_data = (xtestsc, xtestsc))

When I run autoencoder.summary() I get the model summary just fine but when I run autoencoder.fit() I get the following error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-136-aff0d412291f> in <cell line: 1>()
----> 1 autoencoder.fit(xtrainsc, xtrainsc,
      2                epochs = 50,
      3                batch_size = 128,
      4                shuffle = True,
      5                validation_data = (xtestsc, xtestsc))

/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
    120             # To get the full stack trace, call:
    121             # `keras.config.disable_traceback_filtering()`
--> 122             raise e.with_traceback(filtered_tb) from None
    123         finally:
    124             del filtered_tb

/usr/local/lib/python3.10/dist-packages/keras/src/ops/function.py in _run_through_graph(self, inputs, operation_fn, call_fn)
    177         output_tensors = []
    178         for x in self.outputs:
--> 179             output_tensors.append(tensor_dict[id(x)])
    180 
    181         return tree.pack_sequence_as(self._outputs_struct, output_tensors)

KeyError: 'Exception encountered when calling Functional.call().\n\n\x1b[1m139922652929696\x1b[0m\n\nArguments received by Functional.call():\n  • inputs=tf.Tensor(shape=(None, 430, 1), dtype=float32)\n  • training=True\n  • mask=None'

I've reshaped my dataset already to (batch, timesteps, feature) and when I try the code below adapted from the original keras tutorial on building autoencoders, it works, just that the train and validation loss start giving NaN values after a number of epochs.

encoded = layers.LSTM(encoding_dim, activation = 'relu')(inputs)
x = layers.RepeatVector(timesteps)(encoded)
decoded = layers.LSTM(encoding_dim, activation = 'relu', return_sequences = True)(x)
decoded = layers.TimeDistributed(layers.Dense(feat_dim, activation = 'sigmoid'))(decoded)

autoencoder = keras.Model(inputs, decoded)
encoder = keras.Model(inputs, encoded)
encoded_input = keras.Input(shape=(timesteps, encoding_dim))
decoder = keras.Model(encoded_input, decoded)

Has anyone encountered a similar error and fixed it? I'd love to get some help on this. Thanks in advance.

• python
• tensorflow
• keras
• deep-learning
• lstm

Share Improve this question Follow edited Jan 2 at 19:29 Albas asked Jan 2 at 19:05 AlbasAlbas 9711 gold badge11 silver badge77 bronze badges 4

• 1 It shouldn't be "inputs" instead of "input_dim" as keras.model() parameters? what is input_dim? – rehaqds Commented Jan 3 at 15:02
• @rehaqds you're right, it's supposed to be inputs not input_dim, the input_dim was the dimension of the input data which I used for an MLP network. It's working now, however the issue I'm having now is that when I start training the network the losses start becoming nan after the 3rd epoch. You can check out the full code here and see kaggle.com/code/faisalalbasu/asderg-autoencoder – Albas Commented Jan 3 at 16:50
• "webpage not found" – rehaqds Commented Jan 3 at 18:59
• I've fixed it, try again now. – Albas Commented Jan 3 at 19:03

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1 Answer 1

Sorted by: Reset to default 0

To solve the error message, replace input_dim by inputs as keras.model() parameter.

For the problem with the loss value becoming NaN after 3 epochs:

• first check that there is no NaN in your data but as the first epochs run it should be ok

• probably that you are dealing with exploding gradients. Several possible solutions to try:

  • replace ReLU by tanh

  • decrease the learning rate

  • add gradient clipping

  • add weights regularisation