# Nested TorchLayer
**URL:** https://discuss.pennylane.ai/t/nested-torchlayer/3093
**Category:** PennyLane Help
**Created:** [June 24, 2023, 8:38pm UTC](https://discuss.pennylane.ai/t/nested-torchlayer/3093 "2023-06-24T20:38:46Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@Solomon](https://discuss.pennylane.ai/u/Solomon)
#### Post date: [June 24, 2023, 8:38pm UTC](https://discuss.pennylane.ai/t/nested-torchlayer/3093/1 "2023-06-24T20:38:46Z")
Hello,
I am facing a rather complex scenario in an attempt to build a QCNN with nested elements.
Akin to classical CNN’s where we can have a torch nn.Conv2d repeatedly inside an nn.module, my code is as follows:
Assume I have a simple circuit as follows:
```auto
@qml.qnode(dev4)
def CONVCircuit(inputs, weights):
print("Shapes: inputs={}, params_arr={}, q_bits={}, q_depth={}".format(inputs.shape, weights, n_qubits, n_layers))
qml.templates.AmplitudeEmbedding(inputs, wires=[i for i in range(n_qubits)], normalize=True, pad_with=4)
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]
return (exp_vals) #type=,shape=(4,)
```
This is the quantum eqvivalent of of the torch Conv2d layer which uses the circuit above:
```auto
class Q2DCNN(nn.Module):
def __init__ (self,circ,patch_size,n_qubits):
super(Q2DCNN, self). __init__ ()
self.patch_size=patch_size
self.n_qubits=n_qubits
self.weight_shapes = {"weights": (n_layers, n_qubits,3)}
self.pqc = circ
self.qlayer = qml.qnn.TorchLayer(self.pqc, self.weight_shapes)
def forward(self, x):
# print("Q2DCNN start shape:",x.shape) # torch.Size([16, 16, 4])
x=self.process_image(x)
x=x.permute(0, 3, 1, 2)
# print("Q2DCNN final shape:",x.shape) # torch.Size([16, 16, 4])
return x
```
Note that I declared the weights (the heart of the problem here) correctly inside q2dcnn.
Now my real QCNN is using the Q2DCNN several times, like so:
```auto
class QNN(torch.nn.Module):
def __init__ (self, circ,patch_size,n_qubits,n_classes=2):
super(QNN, self). __init__ ()
# weight_shapes = {"weights": (n_layers, n_qubits)}
# self.pqc = circ
self.patch_size=patch_size
self.n_qubits=n_qubits
self.n_classes=n_classes
self.qconv2d1=Q2DCNN(circ,patch_size,n_qubits) # torch.Size([1, 1, 64, 64]) input
self.qconv2d2=Q2DCNN(circ,patch_size,n_qubits) # from H/ to H / 16
self.weight_shapes = {"weights": (n_layers, n_qubits,3)}
self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes)
self.qlayer2d2 = qml.qnn.TorchLayer(self.qconv2d2, self.weight_shapes)
```
Of course this line: `self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes)` throws an exception since TorchLayer is not expecting a torch layer (in my case Q2DCNN) but rather a circuit. BUT, how else can i associate a weight vector INSIDE the `QNN(torch.nn.Module)` to each of my `self.qconv2d1=Q2DCNN(circ,patch_size,n_qubits) # torch.Size([1, 1, 64, 64]) input` layers?
If on the other hand I delete these lines (which dont work anyway …):
```auto
self.qlayer2d1 = qml.qnn.TorchLayer(self.qconv2d1, self.weight_shapes)
self.qlayer2d2 = qml.qnn.TorchLayer(self.qconv2d2, self.weight_shapes)
```
Then the following exception is thrown:
```auto
Cell In[3], line 189, in QNN.forward(self, x)
186 def forward(self, x):
187 # print("QNN start shape:",x.shape)
188 # x=torch.tanh(x) * np.pi / 2.0
--> 189 x = self.qconv2d1(x)
190 # print("QNN first conv, before stacking:",x.shape) # torch.Size([batch, n_qubits, 16, 16])
192 f_maps = [] # List to store the feature maps
...
140 f"Weights tensor must have second dimension of length {len(wires)}; got {shape[1]}"
141 )
143 if shape[2] != 3:
```
How else custom torch.nn layers are supposed to be integrated into another torch.nn module?
Thanks.
---
### Author:  [@Solomon](https://discuss.pennylane.ai/u/Solomon)
#### Post date: [June 25, 2023, 6:56pm UTC](https://discuss.pennylane.ai/t/nested-torchlayer/3093/2 "2023-06-25T18:56:00Z")
Please ignore for the meanwhile … still debugging.
---
### Author:  [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)
#### Post date: [June 26, 2023, 1:35pm UTC](https://discuss.pennylane.ai/t/nested-torchlayer/3093/3 "2023-06-26T13:35:18Z")
Hey @Solomon!
> Please ignore for the meanwhile … still debugging.
Sure thing! Just respond back when you need our help, or let us know that you solved it 🙂!