def test_network_and_plot_results(self):
accuracy_results = []
for training_iterations in self.training_iterations_list:
print "Testing network for " + str(
training_iterations) + " iterations"
# Initialise lists to store mean accuracy and standard error values
mean_accuracies = []
standard_errors = []
# Iterates through the hidden layer sizes specified
for hidden_layer_size in self.hidden_layer_sizes:
# initialise list to store accuracy results
accuracy_cache = []
print "Testing network with hidden layer size: " + str(
hidden_layer_size)
# 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(self.filepath)
# 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(
hidden_layer_size, training_iterations)
# 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_accuracies.append((float(sum(accuracy_cache)) / 10))
standard_errors.append(scipy.stats.sem(accuracy_cache))
# Plot accuracy vs number of hidden nodes with the standard error
plotter = ResultPlotter(self.hidden_layer_sizes, mean_accuracies,
standard_errors, training_iterations,
self.baseline_accuracy,
ntpath.basename(self.filepath))
plotter.generate_plot_with_errors()
accuracy_results.append(mean_accuracies)
if plotter:
plotter.generate_combined_plot(self.hidden_layer_sizes,
accuracy_results,
self.training_iterations_list)
