ds = SemEvalData(blank=True)
for j in range(num_folds):
if j != i:
ds = ds + datasets[j]
input, tags, order = NeuralNet.format_dataset(ds, embedder)
# Train the network.
network = NeuralNet(hidden=128, layers=2, input=len(input[0]), output=2)
# Train on non-test datasets.
loss, accuracy = network.train(input, tags, iterations=100)
accuracies += [accuracy]
# Test on test dataset.
test, _, order = NeuralNet.format_dataset(datasets[i], embedder)
network_predictions.update(network.predict(test, order))
# Evaluate neural network.
neural_cm = Evaluator.confidence_compare(dataset.tags, network_predictions)
print("Neural Network Accuracy: " + str(round(
Evaluator.accuracy(neural_cm)*100, 2)) + "%")
# Save similarity measures to csv.
with open(args.csv_file(), 'w') as writefile:
writefile.write(embedder.to_csv(embed_predictions))
# Save neural network to csv.
with open('output.csv', 'w') as writefile:
writefile.write(NeuralNet.pred_to_csv(network_predictions))
