# Local cost function - hybrid neural networks

**URL:** <https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756>\
**Category:** PennyLane Help\
**Created:** [March 23, 2022, 4:55pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756 "2022-03-23T16:55:22Z")\
**Posts on this page:** 14\
**Page:** 1

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [March 23, 2022, 4:55pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/1 "2022-03-23T16:55:22Z")

</div>

Hello,

I have a few questions regarding hybrid neural networks:

1. **Local Cost Function:** I am unable to make my cost function local rather than global. For instance, I have some input data with 4 features which I embed using `Angle encoding`. Next, I have 4-qubit `variational circuit` and at the end I have a classical neuron layer (output layer) to read the output. Now, when I measure all the qubits (Global cost) and pass it on to the classical output layer it works fine, however, if I measure a single qubit it gives me shapes mismatch error. I am not sure why is that the case since I have `Dense` Layers. Any help would be appreciated.

2. **Increasing qubit number and backend quantum device:**  
If I want to increase more qubits (\>8), I can not use `"default.qubit"` as a quantum device, right?. Since `"qiskit.aer"` allows to use maximum of 29 qubits, I though to use it instead. However, when I tried it for 8 qubits, I get the following error while I try and train the model.

`> **InvalidArgumentError:** cannot compute Mul as input #1(zero-based) was expected to be a float tensor but is a double tensor [Op:Mul]`  
_PS: the same model runs for `"default.qubit"` device perfectly well._

1. **Learning Percentage:** Since it is a hybrid network, is there any way that I can get a ratio of model’s learning from classical and quantum layer(s). Like how much quantum part in particular has contributed to the learning and eventually accuracy.

Thanks in advance,

Best,  
Muhammad Kashif

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [March 24, 2022, 2:47am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/2 "2022-03-24T02:47:10Z")

</div>

Hi @Muhammad_Kashif, could you please share the code you’re using? It will make it easier for us to help you.

You can use as many qubits as you want with `default.qubit`, the limitation is really on how powerful the machine you’re running on is. You should be able to get to about 20 qubits with a standard laptop.

As for the learning percentage I guess you could fix the parameters in the classical layers and see how much improvement you get from optimizing only the quantum layers. I’m not sure it’s exactly what you want but it might give you an idea of how much the quantum optimization is contributing to the result.

---

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [March 24, 2022, 7:24am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/3 "2022-03-24T07:24:35Z")

</div>

Hi @CatalinaAlbornoz,

Thanks for the response and clarifying my doubts…  
My Qnode consists of the following circuit:

```
n_qubits = 4
dev = qml.device("default.qubit", wires=n_qubits)
   
@qml.qnode(dev) 
 def qnode(inputs, weights):
       qml.templates.AngleEmbedding(inputs, wires=range(n_qubits), rotation = 'Y')
       qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits), rotation = qml.RY)
       return qml.expval(qml.PauliZ(wires=0)) 

  n_layers = 1
  weight_shapes = {"weights": (n_layers, n_qubits)}

```

the final model (to train) while adding above ciorcuit as KerasLayer is as follows:

```
qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)
clayer_1 = tf.keras.layers.Dense(10, activation="softmax")

inputs = tf.keras.Input(shape=(4,))
x = qlayer(inputs)
outputs = clayer_1(x)

model_hybrid = tf.keras.Model(inputs=inputs, outputs=outputs)

```

The above model does not train and gives shapes mismatch error whereas if we measure all the qubits as below, it works fine.

`return [qml.expval(qml.PauliZ(wires=[i])) for i in range(n_qubits)]`

Any pointers why is that the case?

Thanks

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [March 24, 2022, 11:54pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/4 "2022-03-24T23:54:01Z")

</div>

Hi @Muhammad_Kashif, I’ve just run your code and I don’t get any errors. What version of PennyLane, Python, and TensorFlow are you using?

---

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [March 25, 2022, 5:57am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/5 "2022-03-25T05:57:06Z")

</div>

Hi @CatalinaAlbornoz, I am using the following versions:

Pennylane: 0.21.0  
Python: 3.8.8  
TensorFlow: 2.5.0

Thanks

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [March 25, 2022, 8:13pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/6 "2022-03-25T20:13:27Z")

</div>

Hi @Muhammad_Kashif, if you add this line to your code do you still get the error?

`tf.keras.backend.set_floatx('float64')`

---

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [March 26, 2022, 8:09am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/7 "2022-03-26T08:09:47Z")

</div>

Hi @CatalinaAlbornoz,

I did add the above line you suggested but I still get the following error:

`ValueError: Input 0 of layer dense_4 is incompatible with the layer: : expected min_ndim=2, found ndim=1. Full shape received: (16,)`

**Overview of my model resulting in the above error:**  
input shape = 12 which is then passed on to 12-qubit parametrized circuit. _**Only the first qubit (q0) is measured**_ and then passed on to 10-neuron classical layer.

Looking at the error above, I thought may be measuring two qubits would resolve the issue. But in the same setting when I **measure two qubits** (q0 and q1), I get the following error:  
`ValueError: Input 0 of layer dense_5 is incompatible with the layer: expected axis -1 of input shape to have value 12 but received input with shape (16, 2)`

PS: 16 is the batch size i am using.

Thanks

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [March 28, 2022, 11:55pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/8 "2022-03-28T23:55:40Z")

</div>

Thanks for the additional info @Muhammad_Kashif. I’ll keep seeing if I can find a solution.

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [May 9, 2022, 6:27pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/9 "2022-05-09T18:27:58Z")

</div>

Hi @Muhammad_Kashif,

Sorry I took so long to respond. I still haven’t found the root cause of your problem.

I’m using Python 3.8.12, PennyLane 0.21.0, and tensorflow-macos 2.8.0 and I can’t reproduce it.

I would suggest upgrading your TensorFlow version.

Also, since PennyLane version 0.23.1 has already been released you may want to upgrade that one too (although that’s not what is causing the error).

I hope this solves your issue. If it doesn’t can you share the full error traceback? We may get some hints from there.

---

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [May 10, 2022, 8:49am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/10 "2022-05-10T08:49:46Z")

</div>

Hi @CatalinaAlbornoz,

Thanks for the response and suggestions. I tried upgrading the pennylane version and tensorflow version, and the error persists. It forces me to measure all the qubits (in hybrid neural network) even though the last classical neuron layer is a dense layer. If I try measuring one or two qubits I get the following kind of error (with full traceback).

* * *

```
ValueError Traceback (most recent call last)
<ipython-input-48-09a2ba6edca4> in <module>
      2 
      3 start = time()
----> 4 history = model_hybrid.fit(x_train, y_train, epochs=100, batch_size=16, validation_data=(x_test, y_test), callbacks = [es,rdc])
      5 print(time()-start)
      6 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1181 _r=1):
   1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
   1184 if data_handler.should_sync:
   1185 context.async_wait()

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_function(iterator)
    853 def train_function(iterator):
    854 """Runs a training execution with one step."""
--> 855 return step_function(self, iterator)
    856 
    857 else:

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in step_function(model, iterator)
    843 
    844 data = next(iterator)
--> 845 outputs = model.distribute_strategy.run(run_step, args=(data,))
    846 outputs = reduce_per_replica(
    847 outputs, self.distribute_strategy, reduction='first')

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in run( ***failed resolving arguments*** )
   1283 fn = autograph.tf_convert(
   1284 fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
-> 1285 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
   1286 
   1287 def reduce(self, reduce_op, value, axis):

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
   2831 kwargs = {}
   2832 with self._container_strategy().scope():
-> 2833 return self._call_for_each_replica(fn, args, kwargs)
   2834 
   2835 def _call_for_each_replica(self, fn, args, kwargs):

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
   3606 def _call_for_each_replica(self, fn, args, kwargs):
   3607 with ReplicaContext(self._container_strategy(), replica_id_in_sync_group=0):
-> 3608 return fn(*args, **kwargs)
   3609 
   3610 def _reduce_to(self, reduce_op, value, destinations, options):

~\Anaconda3\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs)
    595 def wrapper(*args, **kwargs):
    596 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.UNSPECIFIED):
--> 597 return func(*args, **kwargs)
    598 
    599 if inspect.isfunction(func) or inspect.ismethod(func):

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in run_step(data)
    836 
    837 def run_step(data):
--> 838 outputs = model.train_step(data)
    839 # Ensure counter is updated only if `train_step` succeeds.
    840 with ops.control_dependencies(_minimum_control_deps(outputs)):

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_step(self, data)
    793 # Run forward pass.
    794 with backprop.GradientTape() as tape:
--> 795 y_pred = self(x, training=True)
    796 loss = self.compiled_loss(
    797 y, y_pred, sample_weight, regularization_losses=self.losses)

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs)
   1028 with autocast_variable.enable_auto_cast_variables(
   1029 self._compute_dtype_object):
-> 1030 outputs = call_fn(inputs, *args, **kwargs)
   1031 
   1032 if self._activity_regularizer:

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in call(self, inputs, training, mask)
    418 a list of tensors if there are more than one outputs.
    419 """
--> 420 return self._run_internal_graph(
    421 inputs, training=training, mask=mask)
    422 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in _run_internal_graph(self, inputs, training, mask)
    554 
    555 args, kwargs = node.map_arguments(tensor_dict)
--> 556 outputs = node.layer(*args, **kwargs)
    557 
    558 # Update tensor_dict.

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs)
   1011 training=training_mode):
   1012 
-> 1013 input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
   1014 if eager:
   1015 call_fn = self.call

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    249 value = value.value
    250 if value is not None and shape_as_list[int(axis)] not in {value, None}:
--> 251 raise ValueError(
    252 'Input ' + str(input_index) + ' of layer ' + layer_name + ' is'
    253 ' incompatible with the layer: expected axis ' + str(axis) +

ValueError: Input 0 of layer dense_2 is incompatible with the layer: expected axis -1 of input shape to have value 8 but received input with shape (16, 1) 

```

Hope this helps you figure out the actual issue or if pennylane does not support to measure single qubits in multi-qubit hybrid QNN architecture (as of now)?

regards,  
kashif

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [May 10, 2022, 8:41pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/11 "2022-05-10T20:41:39Z")

</div>

Hi @Muhammad_Kashif, it seems that the error arises from a line different from the code you shared. Could you please share your full code including the data you’re using?

---

<div class="post-metadata">

**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [May 12, 2022, 11:11am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/12 "2022-05-12T11:11:15Z")

</div>

Hi @CatalinaAlbornoz,

Please find a sample full code below causing the error as I mentioned above.

**Imports**  
import pennylane as qml  
from pennylane import numpy as np  
import tensorflow as tf  
import matplotlib.pyplot as plt  
from sklearn.model\_selection import train\_test\_split  
from sklearn import datasets

**load dataset**  
`x_train, x_test = datasets.load_digits().data, datasets.load_digits().target`

**Split train and test data**  
x\_train, x\_test, y\_train, y\_test = train\_test\_split(  
x\_train, x\_test, test\_size=0.25, random\_state=42)

**quantum circuit**

```
n_qubits = 6
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
    qml.templates.AmplitudeEmbedding(inputs, wires=range(n_qubits), pad_with=2, normalize = True)
    qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits), rotation = qml.RY)

    return [qml.expval(qml.PauliZ(wires=[0]))]

n_layers = 1
weight_shapes = {"weights": (n_layers, n_qubits)}

```

**convert quantum circuit to Keraslayer**  
`qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)`

**creating and compiling neural network model**

```
clayer = tf.keras.layers.Dense(10, activation="softmax")
model = tf.keras.models.Sequential([qlayer, clayer])

opt = tf.keras.optimizers.SGD(learning_rate=0.2)
model.compile(opt, loss="sparse_categorical_crossentropy", metrics=["accuracy"])

```

**Start training**  
`history = model.fit(x_train, y_train, epochs=5, batch_size=16, validation_data=(x_test, y_test))`

**Error with full traceback**

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-61-92f97b8c4386> in <module>
----> 1 history = model.fit(x_train, y_train, epochs=5, batch_size=16, validation_data=(x_test, y_test))

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1181 _r=1):
   1182 callbacks.on_train_batch_begin(step)
-> 1183 tmp_logs = self.train_function(iterator)
   1184 if data_handler.should_sync:
   1185 context.async_wait()

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_function(iterator)
    853 def train_function(iterator):
    854 """Runs a training execution with one step."""
--> 855 return step_function(self, iterator)
    856 
    857 else:

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in step_function(model, iterator)
    843 
    844 data = next(iterator)
--> 845 outputs = model.distribute_strategy.run(run_step, args=(data,))
    846 outputs = reduce_per_replica(
    847 outputs, self.distribute_strategy, reduction='first')

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in run( ***failed resolving arguments*** )
   1283 fn = autograph.tf_convert(
   1284 fn, autograph_ctx.control_status_ctx(), convert_by_default=False)
-> 1285 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
   1286 
   1287 def reduce(self, reduce_op, value, axis):

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
   2831 kwargs = {}
   2832 with self._container_strategy().scope():
-> 2833 return self._call_for_each_replica(fn, args, kwargs)
   2834 
   2835 def _call_for_each_replica(self, fn, args, kwargs):

~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
   3606 def _call_for_each_replica(self, fn, args, kwargs):
   3607 with ReplicaContext(self._container_strategy(), replica_id_in_sync_group=0):
-> 3608 return fn(*args, **kwargs)
   3609 
   3610 def _reduce_to(self, reduce_op, value, destinations, options):

~\Anaconda3\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs)
    595 def wrapper(*args, **kwargs):
    596 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.UNSPECIFIED):
--> 597 return func(*args, **kwargs)
    598 
    599 if inspect.isfunction(func) or inspect.ismethod(func):

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in run_step(data)
    836 
    837 def run_step(data):
--> 838 outputs = model.train_step(data)
    839 # Ensure counter is updated only if `train_step` succeeds.
    840 with ops.control_dependencies(_minimum_control_deps(outputs)):

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_step(self, data)
    793 # Run forward pass.
    794 with backprop.GradientTape() as tape:
--> 795 y_pred = self(x, training=True)
    796 loss = self.compiled_loss(
    797 y, y_pred, sample_weight, regularization_losses=self.losses)

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs)
   1028 with autocast_variable.enable_auto_cast_variables(
   1029 self._compute_dtype_object):
-> 1030 outputs = call_fn(inputs, *args, **kwargs)
   1031 
   1032 if self._activity_regularizer:

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in call(self, inputs, training, mask)
    378 if not self.built:
    379 self._init_graph_network(self.inputs, self.outputs)
--> 380 return super(Sequential, self).call(inputs, training=training, mask=mask)
    381 
    382 outputs = inputs # handle the corner case where self.layers is empty

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in call(self, inputs, training, mask)
    418 a list of tensors if there are more than one outputs.
    419 """
--> 420 return self._run_internal_graph(
    421 inputs, training=training, mask=mask)
    422 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in _run_internal_graph(self, inputs, training, mask)
    554 
    555 args, kwargs = node.map_arguments(tensor_dict)
--> 556 outputs = node.layer(*args, **kwargs)
    557 
    558 # Update tensor_dict.

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs)
   1011 training=training_mode):
   1012 
-> 1013 input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
   1014 if eager:
   1015 call_fn = self.call

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    249 value = value.value
    250 if value is not None and shape_as_list[int(axis)] not in {value, None}:
--> 251 raise ValueError(
    252 'Input ' + str(input_index) + ' of layer ' + layer_name + ' is'
    253 ' incompatible with the layer: expected axis ' + str(axis) +

ValueError: Input 0 of layer dense_13 is incompatible with the layer: expected axis -1 of input shape to have value 6 but received input with shape (16, 1)

```

Hope it makes the issue clear. Thanks for the help.

Regards,  
M. Kashif  
​  
​

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<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [May 13, 2022, 9:01pm UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/13 "2022-05-13T21:01:35Z")

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Hi @Muhammad_Kashif,

The problem is that when you define the layer

`qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)`

you’re saying that the output dimension is equal to the number of qubits (6 in this case). However in the return statement for your circuit you’re returning a single value.

There are two ways how you can remove this error. On one hand you can change the return statement to be of size 6 using something such as:

`return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]`

On the other hand you could keep the return statement as you have it and change the output dimension to be 1.

Please let me know if this is clear and if this solves your problem!

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**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\
**Post date:** [May 15, 2022, 10:18am UTC](https://discuss.pennylane.ai/t/local-cost-function-hybrid-neural-networks/1756/14 "2022-05-15T10:18:04Z")

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Hi @CatalinaAlbornoz,

The suggested solution of setting the output dimension ogf Qlayer to 1 works fine.

Thanks for the great help.

Best,  
M. Kashif