hyperparams = {
'circuit': circuit,
'task': 'supervised',
'loss': myloss,
'optimizer': 'SGD',
'init_learning_rate': 0.5,
'decay': 0.01,
'print_log': True,
'log_every': 10,
'warm_start': False
}
learner = CircuitLearner(hyperparams=hyperparams)
learner.train_circuit(X=X_train, Y=Y_train, steps=steps, batch_size=batch_size)
test_score = learner.score_circuit(
X=X_test, Y=Y_test, outputs_to_predictions=outputs_to_predictions)
# The score_circuit() function returns a dictionary of different metrics.
print("\nPossible scores to print: {}".format(list(test_score.keys())))
# We select the accuracy and loss.
print("Accuracy on test set: ", test_score['accuracy'])
print("Loss on test set: ", test_score['loss'])
outcomes = learner.run_circuit(X=X_pred,
outputs_to_predictions=outputs_to_predictions)
# The run_circuit() function returns a dictionary of different outcomes.
print("\nPossible outcomes to print: {}".format(list(outcomes.keys())))
# We select the predictions
print("Predictions for new inputs: {}".format(outcomes['predictions']))
def get_cost(self, steps):
learner = CircuitLearnerTF(hyperparams=self.hyperp)
learner.train_circuit(X=self.X, Y=self.Y, steps=steps)
evalu = learner.score_circuit(X=self.X, Y=self.Y)
cost = evalu['loss']
return cost
hyperparams = {'circuit': circuit,
'task': 'supervised',
'loss': myloss,
'optimizer': 'SGD',
'init_learning_rate': 0.01,
'print_log': False}
# Create a learner
learner = CircuitLearner(hyperparams=hyperparams)
# Train the learner
print("Training on: X{0} Y{1}".format(X_train, Y_train))
learner.train_circuit(X=X_train, Y=Y_train, steps=steps)
# Get the accuracy and loss for the test data
test_score = learner.score_circuit(X=X_test, Y=Y_test,
outputs_to_predictions=outputs_to_predictions)
# The score_circuit() function returns a dictionary of different metrics.
print("\nPossible scores to print: {}".format(list(test_score.keys())))
# We select the accuracy and loss.
print("Accuracy on test set: ", test_score['accuracy'])
print("Loss on test set: ", test_score['loss'])
outcomes = learner.run_circuit(X=X_pred, outputs_to_predictions=outputs_to_predictions)
print("Predicting based on X_Predict{0}".format(X_pred))
# The run_circuit() function returns a dictionary of different outcomes.
print("\nPossible outcomes to print: {}".format(list(outcomes.keys())))
# We select the predictions
print("Predictions for new inputs: {}".format(outcomes['predictions']))