acc_GBDT[n, 1], _ = test_GBDT(model, x_test_0, y_test_0)
acc_GBDT[n, 2], _ = test_GBDT(model, x_test_1, y_test_1)
acc_GBDT[n, 3], _ = test_GBDT(model, x_test_2, y_test_2)
else:
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=30,
validation_split=0.2)
if baseline == 'ResNet':
acc_ResNet[n, 0], _ = test_CNN(model, x_test, y_test)
acc_ResNet[n, 1], indics_0 = test_CNN(model, x_test_0,
y_test_0)
acc_ResNet[n, 2], indics_1 = test_CNN(model, x_test_1,
y_test_1)
acc_ResNet[n, 3], indics_2 = test_CNN(model, x_test_2,
y_test_2)
model.save('./results/ResNet_aug_ObjectVariation')
elif baseline == 'CWT+ResNet':
acc_CWT[n, 0], _ = test_CNN(model, x_test, y_test)
acc_CWT[n, 1], _ = test_CNN(model, x_test_0, y_test_0)
acc_CWT[n, 2], _ = test_CNN(model, x_test_1, y_test_1)
acc_CWT[n, 3], _ = test_CNN(model, x_test_2, y_test_2)
# model.save('./results/CWTResNet_aug_ObjectVariation')
elif baseline == 'HT+ResNet':
acc_HT[n, 0], _ = test_CNN(model, x_test, y_test)
acc_HT[n, 1], _ = test_CNN(model, x_test_0, y_test_0)
acc_HT[n, 2], _ = test_CNN(model, x_test_1, y_test_1)
acc_HT[n, 3], _ = test_CNN(model, x_test_2, y_test_2)
rs_init += np.random.randint(1, 50)
acc_GBDT[n, 1], _ = test_GBDT(model, x_test_0, y_test_0)
acc_GBDT[n, 2], _ = test_GBDT(model, x_test_1, y_test_1)
acc_GBDT[n, 3], _ = test_GBDT(model, x_test_2, y_test_2)
acc_GBDT[n, 4], _ = test_GBDT(model, x_test_3, y_test_3)
else:
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=30,
validation_split=0.2)
if baseline == 'ResNet':
acc_ResNet[n, 0], _ = test_CNN(model, x_test, y_test)
acc_ResNet[n, 1], _ = test_CNN(model, x_test_0, y_test_0)
acc_ResNet[n, 2], _ = test_CNN(model, x_test_1, y_test_1)
acc_ResNet[n, 3], _ = test_CNN(model, x_test_2, y_test_2)
acc_ResNet[n, 4], _ = test_CNN(model, x_test_3, y_test_3)
elif baseline == 'CWT+ResNet':
acc_CWT[n, 0], _ = test_CNN(model, x_test, y_test)
acc_CWT[n, 1], _ = test_CNN(model, x_test_0, y_test_0)
acc_CWT[n, 2], _ = test_CNN(model, x_test_1, y_test_1)
acc_CWT[n, 3], _ = test_CNN(model, x_test_2, y_test_2)
acc_CWT[n, 4], _ = test_CNN(model, x_test_3, y_test_3)
elif baseline == 'HT+ResNet':
acc_HT[n, 0], _ = test_CNN(model, x_test, y_test)
acc_HT[n, 1], _ = test_CNN(model, x_test_0, y_test_0)
acc_HT[n, 2], _ = test_CNN(model, x_test_1, y_test_1)
acc_HT[n, 3], _ = test_CNN(model, x_test_2, y_test_2)
acc_HT[n, 4], _ = test_CNN(model, x_test_3, y_test_3)
model.save('./results/HTResNet_aug_InterferenceVariation')
rs_init += np.random.randint(1, 50)