# qml.QNSPSAOptimizer() giving AttributeError: 'function' object has no attribute 'construct'
**URL:** https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535
**Category:** PennyLane Help
**Created:** [April 28, 2024, 11:49pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535 "2024-04-28T23:49:56Z")
**Posts on this page:** 16
**Page:** 1
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [April 28, 2024, 11:49pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/1 "2024-04-28T23:49:56Z")
```import
from pennylane import numpy as np
dev = qml.device("default.qubit", wires=3)
@qml.qnode(dev)
def circuit(params, data):
qml.AngleEmbedding(data, wires=[0, 1, 2])
qml.StronglyEntanglingLayers(params, wires=[0, 1, 2])
return qml.expval(qml.PauliZ(2))
data = np.random.random([3], requires_grad=False)
params = np.random.random(qml.StronglyEntanglingLayers.shape(3, 3), requires_grad=True)
def cost(params, single_sample):
return (1 - circuit(params, single_sample)) ** 2
opt = qml.QNSPSAOptimizer()
for it in range(10):
cost_fn = lambda p: cost(p, data)
metric_fn = lambda p: qml.metric_tensor(circuit, approx="block-diag")(p, data)
params, loss = opt.step_and_cost(cost_fn, params, metric_tensor_fn=metric_fn)
print(f"Epoch: {it} | Loss: {loss} |")
```
the code works with all other optimizers. but i need to use spsa and qpsa. it gives ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[25], line 24
21 cost\_fn = lambda p: cost(p, data)
22 metric\_fn = lambda p: qml.metric\_tensor(circuit, approx=“block-diag”)(p, data)
—\> 24 params, loss = opt.step\_and\_cost(cost\_fn, params, metric\_tensor\_fn=metric\_fn)
26 print(f"Epoch: {it} | Loss: {loss} |")
File ~/.conda/envs/cent7/2020.11-py38/xyz/lib/python3.8/site-packages/pennylane/optimize/qnspsa.py:184, in QNSPSAOptimizer.step\_and\_cost(self, cost, \*args, \*\*kwargs)
171 def step\_and\_cost(self, cost, \*args, \*\*kwargs):
172 r""“Update trainable parameters with one step of the optimizer and return
173 the corresponding objective function value after the step.
174
(…)
182 function output prior to the step
183 “””
→ 184 params\_next = self.\_step\_core(cost, args, kwargs)
186 if not self.blocking:
187 loss\_curr = cost(\*args, \*\*kwargs)
File ~/.conda/envs/cent7/2020.11-py38/xyz/lib/python3.8/site-packages/pennylane/optimize/qnspsa.py:212, in QNSPSAOptimizer.\_step\_core(self, cost, args, kwargs)
209 all\_tensor\_dirs =
210 for \_ in range(self.resamplings):
211 # grad\_tapes contains 2 tapes for the gradient estimation
→ 212 grad\_tapes, grad\_dirs = self.\_get\_spsa\_grad\_tapes(cost, args, kwargs)
213 # metric\_tapes contains 4 tapes for tensor estimation
214 metric\_tapes, tensor\_dirs = self.\_get\_tensor\_tapes(cost, args, kwargs)
File ~/.conda/envs/cent7/2020.11-py38/xyz/lib/python3.8/site-packages/pennylane/optimize/qnspsa.py:355, in QNSPSAOptimizer.\_get\_spsa\_grad\_tapes(self, cost, args, kwargs)
352 args\_plus[index] = arg + self.finite\_diff\_step \* direction
353 args\_minus[index] = arg - self.finite\_diff\_step \* direction
→ 355 cost.construct(args\_plus, kwargs)
356 tape\_plus = cost.tape.copy(copy\_operations=True)
357 cost.construct(args\_minus, kwargs)
AttributeError: ‘function’ object has no attribute ‘construct’. I used regularization also. But did not work. Can you please look into it.
and output of qml.about()
Name: PennyLane
Version: 0.28.0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: [GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.](https://github.com/XanaduAI/pennylane)
Author:
Author-email:
License: Apache License 2.0
Location: /home/bhatia87/.conda/envs/cent7/2020.11-py38/xyz/lib/python3.8/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, retworkx, scipy, semantic-version, toml
Required-by: PennyLane-Lightning
Platform info: Linux-3.10.0-1160.108.1.el7.x86\_64-x86\_64-with-glibc2.10
Python version: 3.8.5
Numpy version: 1.21.0
Scipy version: 1.9.3
Installed devices:
- default.gaussian (PennyLane-0.28.0)
- default.mixed (PennyLane-0.28.0)
- default.qubit (PennyLane-0.28.0)
- default.qubit.autograd (PennyLane-0.28.0)
- default.qubit.jax (PennyLane-0.28.0)
- default.qubit.tf (PennyLane-0.28.0)
- default.qubit.torch (PennyLane-0.28.0)
- default.qutrit (PennyLane-0.28.0)
- null.qubit (PennyLane-0.28.0)
- lightning.qubit (PennyLane-Lightning-0.30.0)
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [April 29, 2024, 9:53pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/2 "2024-04-29T21:53:59Z")
Hi @Amandeep,
The [qml.QNSPSAOptimizer](https://docs.pennylane.ai/en/latest/code/api/pennylane.QNSPSAOptimizer.html) only works with cost functions that are QNodes. This means that you can’t perform the postprocessing that you currently have.
On the other hand I see that you’re using a very old version of PennyLane. Are you able to use the current development version of PennyLane? We will be releasing v0.36 next week so you should use that version.
To use the development version you can run `pip install git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane`
Starting on Tuesday next week this will be the latest stable version so you can update your PennyLane version by running `pip install pennylane --upgrade`.
Once you have this version installed you can run the code below. Notice that I’ve changed the optimization loop so that it uses the circuit directly instead of the cost function you had before.
```auto
import pennylane as qml
from pennylane import numpy as pnp
dev = qml.device("default.qubit", wires=3)
@qml.qnode(dev)
def circuit(params, data):
qml.AngleEmbedding(data, wires=[0, 1, 2])
qml.StronglyEntanglingLayers(params, wires=[0, 1, 2])
return qml.expval(qml.PauliZ(2))
data = pnp.random.random([3], requires_grad=False)
params = pnp.random.random(qml.StronglyEntanglingLayers.shape(3, 3), requires_grad=True)
opt = qml.QNSPSAOptimizer()
for it in range(10):
[params, data], loss = opt.step_and_cost(circuit, params, data) # modified
print(f"Epoch: {it} | Loss: {loss} |")
```
I hope this helps you!
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [April 29, 2024, 10:11pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/3 "2024-04-29T22:11:45Z")
@CatalinaAlbornoz Thank you for your response. I have upgraded to 0.32.0. The other concern is when i use amplitude instead of angle embedding either with QNGoptimizer and default.qubit or lightning. The execution remains slow.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [April 30, 2024, 6:48pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/4 "2024-04-30T18:48:09Z")
Hi @Amandeep,
Is there a reason why you prefer not to use the current development version of PennyLane? Do you get any errors while using it? I also noticed that you’re using Python 3.8. I would recommend that you use Python 3.10 if possible. We stopped supporting Python 3.8 since v0.32 and we will stop supporting Python 3.9 in the near future.
Do you have some numbers for how much slower amplitude embedding is for you?
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 4, 2024, 2:26pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/5 "2024-05-04T14:26:58Z")
> [@CatalinaAlbornoz](#):
>
> `@qml.qnode(dev)`
@CatalinaAlbornoz I have upgraded pennylane. In above QNSPS code. I used but when i want to use batch size it gives value error of shape between params and data. When i set batch=1. Then it starts working.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [May 8, 2024, 12:41am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/6 "2024-05-08T00:41:00Z")
Hi @Amandeep,
Can you please show me where/how you added the batch size? Can you please share the minimal (but self-contained) version of your code that shows this?
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 8, 2024, 5:14pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/7 "2024-05-08T17:14:10Z")
@CatalinaAlbornoz Thank you for your response.
```auto
import pennylane as qml
n_qubits = 5
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def circuit( weights,inputs):
qml.AmplitudeEmbedding(inputs, wires=range(n_qubits), normalize=True)
qml.StronglyEntanglingLayers(weights=weights, wires=range(5))
return qml.expval(qml.PauliZ(0))
from pennylane import numpy as np
num_layers=6
params = np.random.random( size=(num_layers,n_qubits,3), requires_grad=True)
params.shape
def opt_fn(initial_learning_rate):
return qml.QNSPSAOptimizer( regularization=0.001)
initial_learning_rate = 0.01
for round in range(10):
opt =opt_fn(initial_learning_rate)
batch_size =1
n_batches = len(X_train) // batch_size
for batch_idx in range(n_batches):
X_batch = X_train[batch_idx * batch_size : (batch_idx + 1) * batch_size]
y_batch = y_train[batch_idx * batch_size : (batch_idx + 1) * batch_size]
# Perform optimization step
[params, X_batch], loss_val = opt.step_and_cost(circuit, params, X_batch )
# Store the loss for the current batch
loss.append(loss_val)
```
when i set batch size=1, then only works. Else if i set batchsize=16, then it gives a value error ValueError: operands could not be broadcast together with shapes (16,) (6,5,3). I want to work with different batch size and classical SPSA as well QSPSA optiizer for same code.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [May 10, 2024, 2:10am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/8 "2024-05-10T02:10:49Z")
Hi @Amandeep, I get `NameError: name 'X_train' is not defined`.
Could you please share a self-contained version of your code?
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 13, 2024, 1:58pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/9 "2024-05-13T13:58:29Z")
@CatalinaAlbornoz Thank you for your response. Please find the attached code.
```auto
# i took random sample of 100 records and 32 features (amplitude encoding #5 qubits)
num_samples = 100
num_features = 32
random_array = np.random.rand(num_samples, num_features)
# Generate random labels for the samples
labels = np.random.randint(2, size=num_samples) # Assuming binary labels
# Divide the data into train and test sets with an 80:20 ratio
X_train, X_test, y_train, y_test = train_test_split(random_array, labels, test_size=0.2, random_state=42)
y_train=y_train*2-1
y_test=y_test*2-1
print("Shape of training data:", X_train.shape)
print("Shape of test data:", X_test.shape)
print("Shape of training labels:", y_train.shape)
print("Shape of test labels:", y_test.shape)
```
I need to work on QPSA optimizer and classical SPSA optimizer. Although the same code works for QNGD, GD optimizer. Can you please check. I really need this to work. Thank you once again.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [May 14, 2024, 12:00am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/10 "2024-05-14T00:00:02Z")
Hi @Amandeep,
I think the issue here is using AmplitudeEmbedding. There doesn’t seem to be a way of decomposing this. You could try manually creating the embedding with rotations and other operators that can then be decomposed. Unfortunately I don’t see any feasible way of using the AmplitudeEmbedding template together with QPSA.
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 14, 2024, 3:01am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/11 "2024-05-14T03:01:28Z")
@CatalinaAlbornoz Thank you for your response. I did it with AngleEmbedding too. But did not work.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [May 14, 2024, 11:44pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/12 "2024-05-14T23:44:53Z")
Hi @Amandeep,
Have you tried creating an embedding directly with rotation gates? So instead of using a template adding the rotations one by one. Ideally you would need less inputs than qubits but here’s an example (which may or may not work) of encoding the inputs into several layers if needed.
```auto
import pennylane as qml
from pennylane import numpy as pnp
n_qubits = 3
dev = qml.device("default.qubit", wires=n_qubits)
@qml.qnode(dev)
def circuit(weights, inputs):
# Iterate over all inputs
for i in range(len(inputs)):
# Iterate over all qubits
for n in range(n_qubits):
# Encode all inputs one by one on all qubits.
# If there are more inputs than qubits they will be encoded in successive layers
if i%n_qubits==n:
qml.RX(inputs[i],wires=n)
qml.StronglyEntanglingLayers(weights, wires = range(n_qubits))
return qml.expval(qml.PauliZ(0))
shape = qml.StronglyEntanglingLayers.shape(n_layers=2, n_wires=n_qubits)
weights = pnp.random.random(size=shape, requires_grad=True)
inputs = pnp.random.random(5, requires_grad=False)
qml.draw_mpl(circuit,decimals=1)(weights,inputs)
```
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 14, 2024, 11:52pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/13 "2024-05-14T23:52:42Z")
@CatalinaAlbornoz Thank you for your response. I did encoded as you are saying. But it did not work. Although, the recent query [QNSPSA error using a dataset](https://discuss.pennylane.ai/t/qnspsa-error-using-a-dataset/4153)
is exactly the same I am doing. I am not sure how they solved the problem of QNSPSA optimizer. Hope it can help at your end.
---
### Author:  [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)
#### Post date: [May 15, 2024, 12:29am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/14 "2024-05-15T00:29:36Z")
Hi @Amandeep ,
Your error seems very different to the one in the thread that you shared. You can try running the [code shared by Isaac](https://discuss.pennylane.ai/t/qnspsa-error-using-a-dataset/4153/21) but note that it’s a different workflow from what you have been working with so far.
Of course, if this is what you needed then that’s great!
---
### Author:  [@Amandeep](https://discuss.pennylane.ai/u/Amandeep)
#### Post date: [May 15, 2024, 1:02am UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/15 "2024-05-15T01:02:05Z")
@CatalinaAlbornoz Error was different because of different version of pennylane. When I installed latest, the error is exactly same as in their thread. The code of @isaacdevlugt works, but when i try to implement batch wise. It starts giving errors. ☹ Initially in that thread @Christophe_Pere is trying to implement same thing and getting exactly same error. I traced the thread. But later I don’t know how the things work with QNSPA and using batches of data.
---
### Author:  [@Christophe\_Pere](https://discuss.pennylane.ai/u/Christophe_Pere)
#### Post date: [May 15, 2024, 7:19pm UTC](https://discuss.pennylane.ai/t/qml-qnspsaoptimizer-giving-attributeerror-function-object-has-no-attribute-construct/4535/16 "2024-05-15T19:19:41Z")
Hi,
In fact, as we discussed in my post with @isaacdevlugt, the QN-SPSA optimizer and the QNG optimizer don’t accept batches of data. you have to loop on all your data points in your batch.
Like this:
```auto
for data in range(num_batches):
for points in data:
params, cost = opt.step_and_cost(qnode, params, X=point)
params = np.array(params, requires_grad=True)
```
Where `params` are the parameters for your model and `X` is the parameter of the `qnode`. I used the `qnode` as a function to change the number of qubits during the learning process.
best,
C.