def test_learning_rates(self):
mean_accuracies = []
standard_errors = []
best_mean_accuracy = 0.0
best_learning_rate = 0.0
for learning_rate in self.learning_rates:
# initialise list to store accuracy results
accuracy_cache = []
print "Testing network with learning rate: " + str(learning_rate)
# The Neural network is trained and tested for the specified number of iterations for each
# hidden layer size value
for i in range(0, 10, 1):
# Reinitialise dataset importer object
csv_delegate = CSVFileDelegate("Datasets/owls15.csv")
# Initialise Neural Network Classifier with data and target from data importer
neural_network_classifier = NeuralNetworkClassifier(
csv_delegate.training_data, csv_delegate.training_target)
# Build and train network with <hidden_layer_size> nodes in the hidden layer
neural_network_classifier.build_network(3, 2000, learning_rate)
# Use classifier to classify testing data
results = neural_network_classifier.classify_set(
csv_delegate.testing_data)
# Compare results to testing target
accuracy = self.compare_result_to_target(
results, csv_delegate.testing_target)
accuracy_cache.append(accuracy)
print accuracy
# Store the mean and standard error values for each set of results
mean_accuracy = (float(sum(accuracy_cache)) / 10)
if mean_accuracy > best_mean_accuracy:
best_mean_accuracy = mean_accuracy
best_learning_rate = learning_rate
mean_accuracies.append(mean_accuracy)
standard_errors.append(scipy.stats.sem(accuracy_cache))
print "Best learning rate = " + str(best_learning_rate)
plotter = ResultPlotter(self.learning_rates, mean_accuracies,
standard_errors, 0, 0, "Datasets/owls15.csv")
plotter.generate_learning_rate_plot_with_errors(
self.learning_rates, mean_accuracies, standard_errors)
