# Error when using CVNeuralNetLayers **URL:** \ **Category:** PennyLane Help\ **Created:** [July 5, 2022, 7:53am UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017 "2022-07-05T07:53:19Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![PhilippHS](https://avatars.discourse-cdn.com/v4/letter/p/7ab992/32.png) [@PhilippHS](https://discuss.pennylane.ai/u/PhilippHS)\ **Post date:** [July 5, 2022, 7:53am UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017/1 "2022-07-05T07:53:19Z")
Hello, I am working on an experiment trying different implementations of hybrid quantum neuronal networks: I have a larger classical Neuronal network and replace the final dense layer with different quantum layers. So far, I tried the pennylane basic- and strong entanglement layer templates as well as a self-developed circuit. All of these worked very fine and generated good results. Now tried implementations of the CVNeuralNetLayers described in Killoran et.al. 2018. ( [https://pennylane.readthedocs.io/en/stable/code/api/pennylane.CVNeuralNetLayers.html](https://pennylane.readthedocs.io/en/stable/code/api/pennylane.CVNeuralNetLayers.html) ) However, using this layer I run into wired Tensorflow errors when trying to train the model: _**tensorflow.python.framework.errors\_impl.InvalidArgumentError: cannot compute Mul as input #1(zero-based) was expected to be a float tensor but is a double tensor [Op:Mul]**_ Does anyone know what this error means or at least how I can avoid this? Best wishes, Philipp * * * Here my Implementation A) The Qnode ``` import pennylane as qml from pennylane import numpy as np def get_CVQuantumLayer( num_inputs, num_outputs, n_layers=1, device="strawberryfields.fock" , cutoff_dim=10): n_qubits = num_inputs if num_outputs>n_qubits: raise ValueError("Number of output measurements can not be larger then number of Q-Bits") # 0) Initialisations # 0.1) define device dev = qml.device( device , wires=n_qubits , cutoff_dim=cutoff_dim ) # 0.2) Define weight shapes (following Killoran et.al. 2018 ) L = n_layers M = n_qubits K = int( M/2*(M-1) ) weight_shapes = {'theta_1':(L,K), 'phi_1':(L,K), 'varphi_1':(L,M), 'r':(L,M), 'phi_r':(L,M), 'theta_2':(L,K), 'phi_2': (L,K), 'varphi_2':(L,M), 'a':(L,M), 'phi_a':(L,M), 'k':(L,M) } # 1) Define QNode to build quantum layer @qml.qnode(dev,interface='tf') def qnode(inputs, theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k): # 1.1) Squeezing Embedding qml.templates.SqueezingEmbedding(inputs, wires=range(n_qubits) ) # 1.2) CVNN-Layers qml.templates.CVNeuralNetLayers(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k, wires=[i for i in range(n_qubits)] ) return [qml.expval(qml.NumberOperator(wires=i)) for i in range(num_outputs)] # 2) make QLayer qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=num_outputs) return qlayer ``` B) The Network (simplified version) ``` import tensorflow as tf input = tf.keras.Input( shape=input_data_size ) layer_dense = tf.keras.layers.Dense(dense_layer_size)(input) # 1.Q) Generate Quantum Layer as output # 1.Q.0) Define parameters num_qubits = 3 # number of Qbits used num_qlayers= 1 # number of layers in Qlayer. # 1.Q.1) add additional dense layer to reduce dimension to reduce number of qubits required layer_quant_in = tf.keras.layers.Dense( num_qubits )(layer_dense) # 1.Q.2) Load quantum layer from QLayers import get_CVQuantumLayer qlayer = get_CVQuantumLayer(num_qubits, output_data_size, num_qlayers )(layer_quant_in) # 3.2) Assemble Network model = tf.keras.Model(inputs=input_data, outputs=qlayer) model.compile(optimizer=tf.optimizers.Adam(), loss=tf.keras.losses.MeanSquaredError()) ``` ---
**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:** [July 5, 2022, 9:00pm UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017/2 "2022-07-05T21:00:31Z")
Hi @PhilippHS, welcome to the forum! I’m having trouble to replicate your problem because I don’t have access to the following variables: input\_data, input\_data\_size, dense\_layer\_size, and output\_data\_size. In the past I’ve seen this problem happens sometimes with Keras and it can be solved by adding the following line to you code. `tf.keras.backend.set_floatx('float64')` Please let me know if this works for you! ---
**Author:** ![PhilippHS](https://avatars.discourse-cdn.com/v4/letter/p/7ab992/32.png) [@PhilippHS](https://discuss.pennylane.ai/u/PhilippHS)\ **Post date:** [July 6, 2022, 12:57pm UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017/4 "2022-07-06T12:57:03Z")
Thank you very much @CatalinaAlbornoz using ``` tf.keras.backend.set_floatx('float64') ``` indeed solved the problem 🙂 ---
**Author:** ![PhilippHS](https://avatars.discourse-cdn.com/v4/letter/p/7ab992/32.png) [@PhilippHS](https://discuss.pennylane.ai/u/PhilippHS)\ **Post date:** [July 7, 2022, 1:31pm UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017/5 "2022-07-07T13:31:15Z")
Update: I accidentally found another solution to the problem. Using the device “strawberryfields.tf” (instead of “strawberryfields.fock” ) also solves the issue and seems to make the code a bit faster. ---
**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:** [July 7, 2022, 7:16pm UTC](https://discuss.pennylane.ai/t/error-when-using-cvneuralnetlayers/2017/6 "2022-07-07T19:16:28Z")
That’s great to hear @PhilippHS! Indeed “strawberryfields.tf” uses the TensorFlow interface ([see more here](https://pennylane-sf.readthedocs.io/en/latest/devices/tf.html)) so by changing it to the fock device (“strawberryfields.fock”) you no longer have the “float64” issue.