Merge issue when using stack size of 1

[MBSMGW]

When using a stack size of 1, is there an issue with also using skip connections? It just throws an error. I am not sure if it needs to be a bug report or a feature request for automatically deffering to no skip connections for a stack size of 1? anyway, here is the code to reproduce the error:

`def model_create(nb_filters, kernel_size, dropout_rate, lr, dilation):

#if K.backend() == 'tensorflow':
    #K.clear_session()
with mirrored_strategy.scope():
    model=Sequential()
    model = compiled_tcn(return_sequences=return_sequences,
                             num_feat=num_feat,
                             num_classes=num_classes,
                             nb_filters=nb_filters,
                             kernel_size=kernel_size,
                             dilations=[2 ** i for i in range(dilation)],
                             nb_stacks=1,
                             max_len=segment_length,
                             output_len=1,  # type: int
                             padding='causal',  # type: str
                             use_skip_connections=use_skip_connections,  # type: bool
                             regression=False,  # type: bool
                             dropout_rate=dropout_rate,  # type: float
                             name=name,  # type: str,
                             kernel_initializer=kernel_initializer,  # type: str,
                             activation=activation,  # type:str,
                             opt=opt,
                             lr=lr,
                             use_batch_norm=use_batch_norm,
                             use_layer_norm=use_layer_norm,
                             use_weight_norm=use_weight_norm)

    return model

model_grid = KerasClassifier(build_fn=model_create, epochs=15, verbose=0)

search_space['nb_filters'] = 12, 14, 16, 18, 20 search_space['kernel_size'] = 3, 5, 16, 64, 128 search_space['dropout_rate'] = 0.01, 0.03, 0.05, 0.10, 0.15 search_space['lr'] = 0.001, 0.005, 0.010, 0.015 search_space['dilation'] = 1, 3, 7, 9

param_grid = dict(nb_filters = search_space['nb_filters'], kernel_size = search_space['kernel_size'], dropout_rate = search_space['dropout_rate'], lr = search_space['lr'], dilation = search_space['dilation'])

cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=2, random_state=1) search = HalvingGridSearchCV(estimator = model_grid, param_grid = search_space,n_jobs=1, min_resources="exhaust", factor=3, cv = cv)

result = search.fit(xy_t[0][0], xy_t[0][1], batch_size=512)`

my data is 2400 sequence points long univariate time sequences of vibration data. and the following is the error received when running:

`/usr/local/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:614: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/usr/local/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 593, in _fit_and_score estimator.fit(X_train, y_train, fit_params) File "/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/wrappers/scikit_learn.py", line 223, in fit return super(KerasClassifier, self).fit(x, y, kwargs) File "/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/wrappers/scikit_learn.py", line 157, in fit self.model = self.build_fn(*self.filter_sk_params(self.build_fn)) File "", line 27, in model_create use_weight_norm=use_weight_norm) File "/home/ubuntu/tcn_ed.py", line 422, in compiled_tcn use_weight_norm, name=name)(input_layer) File "/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 926, in call input_list) File "/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1117, in _functional_construction_call outputs = call_fn(cast_inputs, args, **kwargs) File "/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py", line 258, in wrapper raise e.ag_error_metadata.to_exception(e) ValueError: in user code:

/home/ubuntu/tcn_ed.py:332 call  *
    x = layers.add(self.skip_connections)
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/layers/merge.py:771 add  **
    return Add(**kwargs)(inputs)
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:926 __call__
    input_list)
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:1098 _functional_construction_call
    self._maybe_build(inputs)
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:2643 _maybe_build
    self.build(input_shapes)  # pylint:disable=not-callable
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/utils/tf_utils.py:323 wrapper
    output_shape = fn(instance, input_shape)
/usr/local/share/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/layers/merge.py:97 build
    'Got ' + str(len(input_shape)) + ' inputs.')

ValueError: A merge layer should be called on a list of at least 2 inputs. Got 1 inputs.`

[philipperemy]

@MBSMGW a stack size of 1 with skip_connections should definitely work! By default the stack_size is 1. Try to use the TCN object instead of using compiled_tcn.

Here is an example that works with compiled_tcn (your case): https://github.com/philipperemy/keras-tcn/blob/1b2c1a6baaefc4a4fac88a74868a23845fb81495/tasks/copy_memory/main.py#L23

[MBSMGW]

@MBSMGW a stack size of 1 with skip_connections should definitely work! By default the stack_size is 1. Try to use the TCN object instead of using compiled_tcn.

Here is an example that works with compiled_tcn (your case):

https://github.com/philipperemy/keras-tcn/blob/1b2c1a6baaefc4a4fac88a74868a23845fb81495/tasks/copy_memory/main.py#L23

Yeah great suggestion, really should have embraced it at the start of thesis instead of trying to wrangle the compiled_tcn function (Which is great, especially starting..)

Thanks!