# Pad\_with causing error in Amplitude Embedding **URL:** \ **Category:** PennyLane Help\ **Created:** [May 31, 2022, 2:34pm UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933 "2022-05-31T14:34:27Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\ **Post date:** [May 31, 2022, 2:34pm UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933/1 "2022-05-31T14:34:27Z")
Hi all, I am working on a problem where my input feature size is 64. I wanted to encode my features in qubit amplitudes. Given the input size I need atleast 6 qubits to encode my data. Works fine… ✌✌ However when I increased the number of qubits, the input size is same (64). I did use the `pad-with` argument in `AmplitudeEmbedding` function. around a week back it was working fine. can some please have a look. Below is my code snippet and error traceback. **Sample Code:** ```auto import pennylane as qml from pennylane import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets ``` **dataset import and split** ```auto x_train, x_test = datasets.load_digits().data, datasets.load_digits().target x_train, x_test, y_train, y_test = train_test_split( x_train, x_test, test_size=0.25, random_state=42) ``` **Qlayer** ```auto qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits) ``` **QNODE** ```auto n_qubits = 8 dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def qnode(inputs, weights): qml.templates.AmplitudeEmbedding(inputs, wires=range(n_qubits), pad_with = 0., normalize = True) qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits), rotation = qml.RY) return [qml.expval(qml.PauliZ(wires=[i])) for i in range(n_qubits)] n_layers = 4 weight_shapes = {"weights": (n_layers, n_qubits)} ``` **Model** ```auto clayer = tf.keras.layers.Dense(10, activation="softmax") model = tf.keras.models.Sequential([qlayer, clayer]) opt = tf.keras.optimizers.SGD(learning_rate=0.2) model.compile(opt, loss="sparse_categorical_crossentropy", metrics=["accuracy"]) ``` **training** ```auto history = model.fit(x_train, y_train, epochs=50, batch_size=16, validation_data=(x_test, y_test)) ``` **Error Traceback:** ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 history = model.fit(x_train, y_train, epochs=50, batch_size=16, validation_data=(x_test, y_test)) ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing) 1181 _r=1): 1182 callbacks.on_train_batch_begin(step) -> 1183 tmp_logs = self.train_function(iterator) 1184 if data_handler.should_sync: 1185 context.async_wait() ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_function(iterator) 853 def train_function(iterator): 854 """Runs a training execution with one step.""" --> 855 return step_function(self, iterator) 856 857 else: ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in step_function(model, iterator) 843 844 data = next(iterator) --> 845 outputs = model.distribute_strategy.run(run_step, args=(data,)) 846 outputs = reduce_per_replica( 847 outputs, self.distribute_strategy, reduction='first') ~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in run( ***failed resolving arguments*** ) 1283 fn = autograph.tf_convert( 1284 fn, autograph_ctx.control_status_ctx(), convert_by_default=False) -> 1285 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) 1286 1287 def reduce(self, reduce_op, value, axis): ~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs) 2831 kwargs = {} 2832 with self._container_strategy().scope(): -> 2833 return self._call_for_each_replica(fn, args, kwargs) 2834 2835 def _call_for_each_replica(self, fn, args, kwargs): ~\Anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs) 3606 def _call_for_each_replica(self, fn, args, kwargs): 3607 with ReplicaContext(self._container_strategy(), replica_id_in_sync_group=0): -> 3608 return fn(*args, **kwargs) 3609 3610 def _reduce_to(self, reduce_op, value, destinations, options): ~\Anaconda3\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs) 595 def wrapper(*args, **kwargs): 596 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.UNSPECIFIED): --> 597 return func(*args, **kwargs) 598 599 if inspect.isfunction(func) or inspect.ismethod(func): ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in run_step(data) 836 837 def run_step(data): --> 838 outputs = model.train_step(data) 839 # Ensure counter is updated only if `train_step` succeeds. 840 with ops.control_dependencies(_minimum_control_deps(outputs)): ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in train_step(self, data) 793 # Run forward pass. 794 with backprop.GradientTape() as tape: --> 795 y_pred = self(x, training=True) 796 loss = self.compiled_loss( 797 y, y_pred, sample_weight, regularization_losses=self.losses) ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs) 1028 with autocast_variable.enable_auto_cast_variables( 1029 self._compute_dtype_object): -> 1030 outputs = call_fn(inputs, *args, **kwargs) 1031 1032 if self._activity_regularizer: ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in call(self, inputs, training, mask) 378 if not self.built: 379 self._init_graph_network(self.inputs, self.outputs) --> 380 return super(Sequential, self).call(inputs, training=training, mask=mask) 381 382 outputs = inputs # handle the corner case where self.layers is empty ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in call(self, inputs, training, mask) 418 a list of tensors if there are more than one outputs. 419 """ --> 420 return self._run_internal_graph( 421 inputs, training=training, mask=mask) 422 ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py in _run_internal_graph(self, inputs, training, mask) 554 555 args, kwargs = node.map_arguments(tensor_dict) --> 556 outputs = node.layer(*args, **kwargs) 557 558 # Update tensor_dict. ~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__ (self, *args, **kwargs) 1028 with autocast_variable.enable_auto_cast_variables( 1029 self._compute_dtype_object): -> 1030 outputs = call_fn(inputs, *args, **kwargs) 1031 1032 if self._activity_regularizer: ~\Anaconda3\lib\site-packages\pennylane\qnn\keras.py in call(self, inputs) 299 reconstructor = [] 300 for x in tf.unstack(inputs): --> 301 reconstructor.append(self.call(x)) 302 return tf.stack(reconstructor) 303 ~\Anaconda3\lib\site-packages\pennylane\qnn\keras.py in call(self, inputs) 302 return tf.stack(reconstructor) 303 --> 304 return self._evaluate_qnode(inputs) 305 306 def _evaluate_qnode(self, x): ~\Anaconda3\lib\site-packages\pennylane\qnn\keras.py in _evaluate_qnode(self, x) 317 **{k: 1.0 * w for k, w in self.qnode_weights.items()}, 318 } --> 319 return self.qnode(**kwargs) 320 321 def compute_output_shape(self, input_shape): ~\Anaconda3\lib\site-packages\pennylane\qnode.py in __call__ (self, *args, **kwargs) 564 565 # construct the tape --> 566 self.construct(args, kwargs) 567 568 cache = self.execute_kwargs.get("cache", False) ~\Anaconda3\lib\site-packages\pennylane\qnode.py in construct(self, args, kwargs) 481 482 with self.tape: --> 483 self._qfunc_output = self.func(*args, **kwargs) 484 self._tape._qfunc_output = self._qfunc_output 485 in qnode(inputs, weights) 4 def qnode(inputs, weights): 5 ----> 6 qml.templates.AmplitudeEmbedding(inputs, wires=range(n_qubits), pad_with = 0., normalize = True) 7 qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits), rotation = qml.RY) 8 ~\Anaconda3\lib\site-packages\pennylane\templates\embeddings\amplitude.py in __init__ (self, features, wires, pad_with, normalize, do_queue, id) 127 self.pad_with = pad_with 128 self.normalize = normalize --> 129 features = self._preprocess(features, wires, pad_with, normalize) 130 super(). __init__ (features, wires=wires, do_queue=do_queue, id=id) 131 ~\Anaconda3\lib\site-packages\pennylane\templates\embeddings\amplitude.py in _preprocess(features, wires, pad_with, normalize) 206 padding = [pad_with] * (2 ** len(wires) - n_features) 207 if ( --> 208 hasattr(feature_set, "device") and feature_set.device.type == "cuda" 209 ): # pragma: no cover 210 ## Torch tensor, send to same GPU AttributeError: 'str' object has no attribute 'type' ``` Any help would be appreciated. Thanks ---
**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:** [June 1, 2022, 10:54pm UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933/3 "2022-06-01T22:54:08Z")
Hi @Muhammad_Kashif, I’m not being able to fix your problem. Can you confirm that week ago it was working with the exact same code and now it’s not working? Could you also please share the output of qml.about() ? ---
**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\ **Post date:** [June 2, 2022, 6:13am UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933/4 "2022-06-02T06:13:03Z")
Hi @CatalinaAlbornoz, Thanks for looking into my query and responding. Yes, I am pretty sure that almost the same code was working around a week back. I went upto 14 qubits and it was working. But now, i am (kind of) being forced to use the exactly `n` qubits for `2^n` features when I am using amplitude encoding. Going beyond `n` results in error. Secondly, I checked that the `AmplitudeEmbedding` separately in the `qnode` works fine for the same input data as shown below . ``` n_qubits = 10 dev = qml.device("default.qubit", wires=n_qubits) @qml.qnode(dev) def qnode(inputs,weights): qml.templates.AmplitudeEmbedding(inputs, wires=range(n_qubits), pad_with = 0., normalize = True) qml.templates.BasicEntanglerLayers(weights, wires=range(n_qubits), rotation = qml.RY) return [qml.expval(qml.PauliZ(wires=[i])) for i in range(n_qubits)] shape = qml.BasicEntanglerLayers.shape(n_layers=2, n_wires=10) w = np.random.random(size=shape) exp_val = qnode(inputs=x_train[1], weights=w) print(exp_val) ``` **Result:** `[0.01450765 0.01874857 0.0038299 0.01640479 0.02875179 -0.00828447 0.02064254 -0.04912628 0.01601941 -0.06728389]` Since the above code snippet works fine even with `10` qubits (which is more than `6` qubits for input size of `64`), i believe the issue is somewhere, when we convert that `qnode` (utilizing `pad_with` in `amplitude embedding`) to KerasLayer and use it in model and train. Below is the output of qml.about(): ``` Name: PennyLane Version: 0.23.1 Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc. Home-page: https://github.com/XanaduAI/pennylane Author: Author-email: License: Apache License 2.0 Location: /usr/local/lib/python3.8/site-packages Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, retworkx, scipy, semantic-version, toml Required-by: PennyLane-Lightning Platform info: Linux-5.8.0-59-generic-x86_64-with-glibc2.10 Python version: 3.8.12 Numpy version: 1.22.3 Scipy version: 1.7.3 Installed devices: - default.gaussian (PennyLane-0.23.1) - default.mixed (PennyLane-0.23.1) - default.qubit (PennyLane-0.23.1) - default.qubit.autograd (PennyLane-0.23.1) - default.qubit.jax (PennyLane-0.23.1) - default.qubit.tf (PennyLane-0.23.1) - default.qubit.torch (PennyLane-0.23.1) - lightning.qubit (PennyLane-Lightning-0.23.0). ``` I hope this helps you diagnose and resolve the issue. Thanks for your help. Regards… ---
**Author:** ![Guillermo\_Alonso](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/guillermo_alonso/32/816_2.png) [@Guillermo\_Alonso](https://discuss.pennylane.ai/u/Guillermo_Alonso)\ **Post date:** [June 2, 2022, 2:11pm UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933/5 "2022-06-02T14:11:09Z")
Hey! It seems that there is a problem with the types, for the moment, until we find the bug, it would be enough that you change this line ```python qml.templates.AmplitudeEmbedding(inputs, wires=range(n_qubits), pad_with = 0., normalize = True) ``` for this other ```python qml.templates.AmplitudeEmbedding([a for a in inputs], wires=range(n_qubits), pad_with = 0., normalize = True) ``` let me know if you have any problems 🙂 ---
**Author:** ![Muhammad\_Kashif](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/muhammad_kashif/32/558_2.png) [@Muhammad\_Kashif](https://discuss.pennylane.ai/u/Muhammad_Kashif)\ **Post date:** [June 3, 2022, 8:07am UTC](https://discuss.pennylane.ai/t/pad-with-causing-error-in-amplitude-embedding/1933/6 "2022-06-03T08:07:20Z")
Hi @Guillermo_Alonso, The solution works fine. Thanks…