def test_qnode_weights_with_spec(self, get_circuit, monkeypatch, output_dim, n_qubits):
"""Test if the build() method correctly passes on user specified weight_specs to the
inherited add_weight() method. This is done by monkeypatching add_weight() so that it
simply returns its input keyword arguments. The qnode_weights dictionary should then have
values that are the input keyword arguments, and we check that the specified weight_specs
keywords are there."""
def add_weight_dummy(*args, **kwargs):
"""Dummy function for mocking out the add_weight method to simply return the input
keyword arguments"""
return kwargs
weight_specs = {
"w1": {"initializer": "random_uniform", "trainable": False},
"w2": {"initializer": tf.keras.initializers.RandomNormal(mean=0, stddev=0.5)},
"w3": {},
"w4": {},
"w5": {},
"w6": {},
"w7": {},
}
with monkeypatch.context() as m:
m.setattr(tf.keras.layers.Layer, "add_weight", add_weight_dummy)
c, w = get_circuit
layer = KerasLayer(c, w, output_dim, weight_specs=weight_specs)
layer.build(input_shape=(10, n_qubits))
for weight in layer.weight_shapes:
assert all(
item in layer.qnode_weights[weight].items()
for item in weight_specs[weight].items()
)
def test_qnode_weights(self, get_circuit, n_qubits, output_dim):
"""Test if the build() method correctly initializes the weights in the qnode_weights
dictionary, i.e., that each value of the dictionary has correct shape and name."""
c, w = get_circuit
layer = KerasLayer(c, w, output_dim)
layer.build(input_shape=(10, n_qubits))
for weight, shape in layer.weight_shapes.items():
assert layer.qnode_weights[weight].shape == shape
assert layer.qnode_weights[weight].name[:-2] == weight
