Hello! i’m working on a tutorial quantum nodes into kersa layer and using qnn layer for a modulation dataset. dataset contains 2D images of 11 different modulation types. these images contains 2 channels I and Q. i converted these images to a numpy file which has shape (2200, 100,100,2) where 2200 is the total no of images with size 100x100 and represent 2 channels. labels file shape is (2200,11). total classes are 11 and labels are categorical type. i want to apply this image dataset on qnn layer. i have tried but dont know how i modify this part of code shown below according to image dataset.
import pennylane as qml
n_qubits = 2
dev = qml.device(“default.qubit”, wires=n_qubits)
@qml.qnode(dev)
def qnode(inputs, weights):
qml.AngleEmbedding(inputs, wires=range(n_qubits))
qml.BasicEntanglerLayers(weights, wires=range(n_qubits))
return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]
also should i have to add flatten layer here
clayer_1 = tf.keras.layers.Dense(2)
clayer_2 = tf.keras.layers.Dense(2, activation=“softmax”)
model = tf.keras.models.Sequential([clayer_1, qlayer, clayer_2])