return tf.nn.l2_loss(regularized_params)
X_train = np.array([[0, 1], [0, 2], [0, 3], [0, 4]])
hyperparams = {
'circuit': circuit,
'task': 'unsupervised',
'optimizer': 'SGD',
'init_learning_rate': 0.1,
'loss': myloss,
'regularizer': myregularizer,
'regularization_strength': 0.1
}
learner = CircuitLearner(hyperparams=hyperparams)
learner.train_circuit(X=X_train, steps=steps)
outcomes = learner.run_circuit()
final_distribution = outcomes['outputs']
# Use a helper function to sample fock states from this state.
# They should show a similar distribution to the training data
for i in range(10):
sample = sample_from_distribution(distribution=final_distribution)
print("Fock state sample {}:{} \n".format(i, sample))
# Print out the final circuit parameters
learner.get_circuit_parameters(only_print=True)
def test_samplefromdistr_returns_sample(self):
desired_result = np.array([0, 1])
self.assertAlmostEqualArray(
sample_from_distribution(self.distribution), desired_result)