def main():
print("loading model")
model_t = keras.models.load_model('model/model_t.h5', compile=False)
model_r = keras.models.load_model('model/model_r.h5', compile=False)
ds = DocDataset()
x_train_snicor, _, _ = ds.load_train_data()
x_test_snicer, x_test_boot = ds.load_test_data()
train = model_t.predict(x_train_snicor)
test_s = model_t.predict(x_test_snicer)
test_b = model_t.predict(x_test_boot)
train = train.reshape((len(x_train_snicor), -1))
test_s = test_s.reshape((len(x_test_snicer), -1))
test_b = test_b.reshape((len(x_test_boot), -1))
#0-1に変換
ms = MinMaxScaler()
train = ms.fit_transform(train)
test_s = ms.transform(test_s)
test_b = ms.transform(test_b)
# fit the model
clf = LocalOutlierFactor(n_neighbors=5)
y_pred = clf.fit(train)
# 異常スコア
Z1 = -clf._decision_function(test_s)
Z2 = -clf._decision_function(test_b)
#ROC曲線の描画
y_true = np.zeros(len(test_s) + len(test_b))
y_true[len(test_s):] = 1 #0:正常、1：異常
# FPR, TPR(, しきい値) を算出
fpr, tpr, _ = metrics.roc_curve(y_true, np.hstack((Z1, Z2)))
# AUC
auc = metrics.auc(fpr, tpr)
# ROC曲線をプロット
plt.plot(fpr, tpr, label='DeepOneClassification(AUC = %.2f)' % auc)
plt.legend()
plt.title('ROC curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.grid(True)
plt.show()
def main():
print("loading model")
model_r, model_t = DocNetWork()
# dataset
ds = DocDataset()
x_train_snicor, x_train_ref, y_train_ref = ds.load_train_data()
# 損失関数の値を保存
loss_c, loss_d = [], []
# ターゲットデータセットは、one class
for i in range(epoch_num):
tmp_lc, tmp_ld = [], []
print(("epoch {} training network ... ").format(i))
for i in range(int(len(x_train_snicor) / batchsize)):
range_s = batchsize * i
range_e = batchsize * ( i + 1 )
batch_target = x_train_snicor[range_s:range_e]
batch_ref = x_train_ref[range_s:range_e]
batch_y = y_train_ref[range_s:range_e]
print("training target network ... ")
tmp_lc.append(model_t.train_on_batch(batch_target, np.zeros((batchsize, feature_out))))
print("training reference network ... ")
tmp_ld.append(model_r.train_on_batch(batch_ref, batch_y))
loss_c.append(np.mean(tmp_lc))
loss_d.append(np.mean(tmp_ld))
if (epoch_num+1) % 5 == 0:
print("epoch:",epoch_num+1)
print("Compact loss", loss_c[-1])
print("Descriptive loss:", loss_d[-1])
if not os.path.exists(model_path):
os.mkdir(model_path)
model_t.save(os.path.join(model_path, "model_t.h5"), include_optimizer=False)
model_r.save(os.path.join(model_path, "model_r.h5"), include_optimizer=False)
