model.load_model(path)
noise = model.gen_noise(0, 1, (NUM, LATENT_DIM))
x = model.generator(noise)
x = x.data.cpu().numpy().reshape(x.size(0), -1)
y = np.zeros(NUM) + i
x_gen.append(x)
y_gen.append(y)
x_gen = np.concatenate(x_gen)
y_gen = np.concatenate(y_gen)
data, label = load_npz()
(x_train, y_train), (x_test, y_test) = load_data()
clf_model = KNeighborsClassifier(n_neighbors=1)
clf_model.fit(x_train, y_train)
pred_data = clf_model.predict(data)
pred_test = clf_model.predict(x_test)
pred_gen = clf_model.predict(x_gen)
classify_analysis(label, pred_data, labels=LABELS)
classify_analysis(y_test, pred_test, labels=LABELS)
classify_analysis(y_gen, pred_gen, labels=LABELS)
print('-' * 20 + 'TSTR' + '-' * 20)
clf_model.fit(x_gen, y_gen)
pred_data = clf_model.predict(data)
pred_test = clf_model.predict(x_test)
pred_gen = clf_model.predict(x_gen)
classify_analysis(label, pred_data, labels=LABELS)
classify_analysis(y_test, pred_test, labels=LABELS)
plot_errorband({'fake': x_gen, 'real': data})
epochs=EPOCHS,
sample_cycle=SAMPLE_CYCLE,
target=TARGET,
)
model.plot_history(name=TARGET)
toc = time.time()
print('Training costs {:.2f}s'.format(toc - tic))
else:
path =\
'./ckpt/{:s}_{:d}.pkl'.format(model.__class__.__name__, TARGET)
model.load_model(path)
(x_train, y_train), (x_test, y_test) = load_data(NAME)
print(x_train[y_train == TARGET].shape, x_train[y_train != TARGET].shape,
x_test[y_test == TARGET].shape, x_test[y_test != TARGET].shape)
detector = AnoGAN(model, LATENT_DIM)
score = detector.anomaly_score(x_test, lambda_score=0.1)
score = score.cpu().data.numpy()
pred = score > 10
print(pred)
pred = (pred - 0.5) * 2
print(pred)
classify_analysis(y_test, pred, labels=LABELS)
plt.figure(figsize=(15, 5))
plt.scatter(range(len(pred)), pred, label='anomaly score', marker='o')
plt.scatter(range(len(y_test)), y_test, label='label', marker='x')
plt.legend(loc='best')
plt.show()