def plot3(trainf):
print("Running Test 3")
nn = NeuralNet(trainf)
#nn.evaluate(folds, epochs, learning_rate)
nn.evaluate(10, 50, 0.1)
x, y = nn.evaluate_roc()
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.set_title('ROC for Neural Net')
ax2.set_xlabel('False Positive Rate')
ax2.set_ylabel('True Positive Rate')
ax2.plot(x, y, c='b', marker='o')
