if yt == 0:
        print score
    if score < 0.5:
        Y_pred.append(0)
    else:
        Y_pred.append(1)


tp, tn, fp, fn = _tp_tn_fp_fn(test_set_y, Y_pred)
print tp, tn, fp, fn
print "tp", "tn", "fp", "fn"

print "Accuracy ", (tp+tn)/(tp+tn+fp+fn), "Negative precision ", tn/(tn+fn+0.0001), "Precision ", tp/(tp+fp+0.00001)
print "True performance ", tn/(fp+tn)

Y_pred = []
for xt, yt in zip(test_set_x, test_set_y):
    score = e.network.predict(xt.reshape(1,-1))
    if yt == 1:
        print score
    if score < 0.5:
        Y_pred.append(0)
    else:
        Y_pred.append(1)



plot_layers(e.network.weights)
plt.tight_layout()
plt.show()