# Quantum Neural Networks / Quanvolutional NN

**URL:** https://discuss.pennylane.ai/t/quantum-neural-networks-quanvolutional-nn/1111
**Category:** PennyLane Help
**Created:** [June 11, 2021, 2:19pm UTC](https://discuss.pennylane.ai/t/quantum-neural-networks-quanvolutional-nn/1111 "2021-06-11T14:19:35Z")
**Posts on this page:** 1
**Showing post:** 7

<div class="post-metadata">

### Author: ![Tom\_Bromley](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/tom_bromley/32/129_2.png) [@Tom\_Bromley](https://discuss.pennylane.ai/u/Tom_Bromley)
#### Post date: [June 16, 2021, 2:37pm UTC](https://discuss.pennylane.ai/t/quantum-neural-networks-quanvolutional-nn/1111/7 "2021-06-16T14:37:38Z")

</div>

Hey @Muhammad_Kashif,

> In tutorials what is n\_layers (only used while defining weight\_shapes) and weight\_shapes, in the first tutorial weight\_shapes is two parameters and in the second one, it has three-parameter. Does it depend on the no. of qubits used, if yes then what of we want to lets say more than 10 qubits, what would be the weight\_shapes then?

The `weight_shapes` parameter is intended to let the `qml.qnn.KerasLayer` know the shapes of the trainable parameters in the QNode. It should be a dictionary that maps argument name to shape, for example:

```python
@qml.qnode(dev)
def qnode(inputs, w1, w2, w3):
    ...
    qml.RX(w1, wires=0)
    qml.Rot(w2, wires=1)
    qml.templates.StronglyEntanglingLayers(w3, wires=range(2))
    ...

```

In this case, we should have `weight_shapes = {"w1": 1, "w2": 3, "w3": (n_layers, 2, 3)}`. It is easy to see this for `w1` and `w2`, since `w1` feeds into the single-parameter `RX` gate and `w2` feeds into the three-parameter `Rot` gate. The shape of `w3` is a bit more complicated because it feeds into [StronglyEntanglingLayers](https://pennylane.readthedocs.io/en/stable/code/api/pennylane.templates.layers.StronglyEntanglingLayers.html). In this case, the shape must be `(n_layers, n_wires, 3)`. For `StronglyEntanglingLayers`, we have multiple layers that look like:

 ![image](https://canada1.discourse-cdn.com/flex012/uploads/pennylane/original/1X/f49714d80e7583819261cb18d50d1c468d09ad75.png)  
Each qubit has a `Rot` gate applied followed by an entangling block. We must hence specify the number of layers and number of wires. Since `Rot` has three parameters, the overall shape is `(n_layers, n_wires, 3)`.

> Woud the different data embedding techniques like amplitude embedding, angle embedding among others effect the underlying model’s performance? and also what could be the potential effect of `StronglyEntanglingLayers` or `BasicEntanglingLayers` on model performance?

Definitely! There is a lot of room to play about with different embeddings and layers - check out the literature above to get more of an understanding.

> while printing the model summary in keras (for a model with quantum layers) does not show any zero trainable paramters for quantum layer and “unused” notification as well, why is that so? Below is the model code and corresponding screenshot

🤔 That’s odd. I just tried the code below and the summary printed ok:

```python
import pennylane as qml
import tensorflow as tf

n_qubits = 2
dev = qml.device("default.qubit", wires=n_qubits)

@qml.qnode(dev)
def qnode(inputs, weights):
    qml.templates.AngleEmbedding(inputs, wires=range(n_qubits))
    qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits))
    return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]

n_layers = 6
weight_shapes = {"weights": (n_layers, n_qubits)}

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

clayer_1 = tf.keras.layers.Dense(4)
qlayer_1 = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)
qlayer_2 = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)
clayer_2 = tf.keras.layers.Dense(2, activation="softmax")

# construct the model
inputs = tf.keras.Input(shape=(2,))
x = clayer_1(inputs)
x_1, x_2 = tf.split(x, 2, axis=1)
x_1 = qlayer_1(x_1)
x_2 = qlayer_2(x_2)
x = tf.concat([x_1, x_2], axis=1)
outputs = clayer_2(x)

model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.predict(tf.ones((5, 2)))

```

with the result

```pycon
>>> model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to                     
==================================================================================================
input_1 (InputLayer) [(None, 2)] 0                                            
__________________________________________________________________________________________________
dense (Dense) (None, 4) 12 input_1[0][0]                    
__________________________________________________________________________________________________
tf.split (TFOpLambda) [(None, 2), (None, 2 0 dense[0][0]                      
__________________________________________________________________________________________________
keras_layer_1 (KerasLayer) (None, 2) 12 tf.split[0][0]                   
__________________________________________________________________________________________________
keras_layer_2 (KerasLayer) (None, 2) 12 tf.split[0][1]                   
__________________________________________________________________________________________________
tf.concat (TFOpLambda) (None, 4) 0 keras_layer_1[0][0]              
                                                                 keras_layer_2[0][0]              
__________________________________________________________________________________________________
dense_1 (Dense) (None, 2) 10 tf.concat[0][0]                  
==================================================================================================
Total params: 46
Trainable params: 46
Non-trainable params: 0
__________________________________________________________________________________________________

```

(note the code is adapted from [this](https://pennylane.ai/qml/demos/tutorial_qnn_module_tf.html) tutorial).

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