Hi.
Using strawberryfields.tf and transforming the qnodes into keras layers, when trying to train a network that has some gates that are controlled by the inputs I get the error: Cannot convert 1.5707963267948966 to EagerTensor of dtype int32
Below a version of the code I created to pinpoint the problem:
dev = qml.device('strawberryfields.tf', wires=4, cutoff_dim=3)
def layer(theta, phi, wires):
M = len(wires)
phi_nonlinear = np.pi / 2
qml.templates.Interferometer(
theta, phi, np.zeros(M), wires=wires, mesh="rectangular",
)
for i in wires:
qml.Kerr(phi_nonlinear, wires=i)
@qml.qnode(dev)
def quantum_neural_net(inputs, var):
wires = list(range(len(inputs)-1))
activ = inputs[len(inputs)-1]
# Encode input x into a sequence of quantum fock states
for i in wires:
qml.FockState(inputs[i], wires=i)
qml.ops.cv.Beamsplitter(activ*(np.pi/2), (np.pi/2), wires = [0, 1])
# "layer" subcircuits
for i, v in enumerate(var):
layer(v[: len(v) // 2], v[len(v) // 2 :], wires)
return [qml.expval(qml.NumberOperator(w)) for w in wires]
X = np.array([[1, 0, 1, 0, 0],
[1, 0, 1, 0, 1]], requires_grad=False)
Y = np.array([[1, 0, 1, 0],
[0, 1, 0, 1]], requires_grad=False)
num_layers = 4
M = len(X[0])-1
num_variables_per_layer = M * (M - 1)
weight_shapes = {"var": (num_layers, num_variables_per_layer)}
qlayer = qml.qnn.KerasLayer(quantum_neural_net, weight_shapes, output_dim=6)
model = tf.keras.models.Sequential([qlayer])
LR_ST = 0.1
opt = tf.keras.optimizers.Adam(learning_rate = LR_ST)
model.compile(opt, loss="mae", metrics=["accuracy"])
fitting = model.fit(X, Y, epochs=10, verbose=2)
The problem seems to be generated by the line:
qml.ops.cv.Beamsplitter(activ*(np.pi/2), (np.pi/2), wires = [0, 1])
Where “activ” is one of the inputs passed which is either 0 or 1 and deactivates/activates the gate.
This code was working fine on pennylane 0.13 with pennylane-sf 0.12, but on pennylane 0.15.1 with pennylane-sf 0.15 it gives the error:
Epoch 1/10
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-2f1fafb4ef74> in <module>
----> 1 fitting = model.fit(X, Y, epochs=10, verbose=2)
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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()
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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:
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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')
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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)):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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)
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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:
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\keras\engine\functional.py in call(self, inputs, training, mask)
419 """
420 return self._run_internal_graph(
--> 421 inputs, training=training, mask=mask)
422
423 def compute_output_shape(self, input_shape):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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.
C:\ProgramData\Anaconda3\envs\Pennylane14TF\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:
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\pennylane\qnn\keras.py in call(self, inputs)
285 reconstructor = []
286 for x in tf.unstack(inputs):
--> 287 reconstructor.append(self.call(x))
288 return tf.stack(reconstructor)
289
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\pennylane\qnn\keras.py in call(self, inputs)
288 return tf.stack(reconstructor)
289
--> 290 return self._evaluate_qnode(inputs)
291
292 def _evaluate_qnode(self, x):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\pennylane\qnn\keras.py in _evaluate_qnode(self, x)
300 """
301 kwargs = {**{self.input_arg: x}, **{k: 1.0 * w for k, w in self.qnode_weights.items()}}
--> 302 return self.qnode(**kwargs)
303
304 def compute_output_shape(self, input_shape):
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\pennylane\qnode.py in __call__(self, *args, **kwargs)
550 if self.mutable or self.qtape is None:
551 # construct the tape
--> 552 self.construct(args, kwargs)
553
554 # execute the tape
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\pennylane\qnode.py in construct(self, args, kwargs)
479
480 with self.qtape:
--> 481 self.qfunc_output = self.func(*args, **kwargs)
482
483 if not isinstance(self.qfunc_output, Sequence):
<ipython-input-3-2535adc305fa> in quantum_neural_net(inputs, var)
9 qml.FockState(inputs[i], wires=i)
10
---> 11 qml.ops.cv.Beamsplitter(activ*(np.pi/2), (np.pi/2), wires = [0, 1])
12
13
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\ops\math_ops.py in binary_op_wrapper(x, y)
1248 return out
1249 except (TypeError, ValueError):
-> 1250 raise e
1251 else:
1252 raise
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\ops\math_ops.py in binary_op_wrapper(x, y)
1232 # r_binary_op_wrapper use different force_same_dtype values.
1233 x, y = maybe_promote_tensors(x, y, force_same_dtype=False)
-> 1234 return func(x, y, name=name)
1235 except (TypeError, ValueError) as e:
1236 # Even if dispatching the op failed, the RHS may be a tensor aware
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\ops\math_ops.py in _mul_dispatch(x, y, name)
1573 return sparse_tensor.SparseTensor(y.indices, new_vals, y.dense_shape)
1574 else:
-> 1575 return multiply(x, y, name=name)
1576
1577
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\ops\math_ops.py in multiply(x, y, name)
528 """
529
--> 530 return gen_math_ops.mul(x, y, name)
531
532
C:\ProgramData\Anaconda3\envs\Pennylane14TF\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in mul(x, y, name)
6234 try:
6235 _result = pywrap_tfe.TFE_Py_FastPathExecute(
-> 6236 _ctx, "Mul", name, x, y)
6237 return _result
6238 except _core._NotOkStatusException as e:
TypeError: Cannot convert 1.5707963267948966 to EagerTensor of dtype int32
Does anyone know how to avoid this issue?