画像で異常検知をしようとしたのですが学習をした後、評価画像との比較画像の出し方がわかりません

# 学習データの読み込み＆前処理
train_images = glob.glob("//*.png")
train = []
for i in train_images:
    image = cv2.imread(i)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    train.append(image)

train = np.array(train)
train = train.astype('float32') / 255

# 学習用ハイパーパラメータ
LEARNING_RATE = 0.0005
BATCH_SIZE = 8
Z_DIM = 50
EPOCHS = 3

# エンコーダ
encoder_input = Input(shape=(128,128,3), name='encoder_input')
x = encoder_input
x = Conv2D(filters=32, kernel_size=3, strides=1, padding='same', name='encoder_conv_0')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=32, kernel_size=3, strides=1, padding='same', name='encoder_conv_0_1')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=2, padding='same', name='encoder_conv_1')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=2, padding='same', name='encoder_conv_2')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', name='encoder_conv_3')(x)
x = LeakyReLU()(x)
shape_before_flattening = K.int_shape(x)[1:]
x = Flatten()(x)
encoder_output = Dense(Z_DIM, name='encoder_output')(x)
encoder = Model(encoder_input, encoder_output)

# デコーダ
decoder_input = Input(shape=(Z_DIM,), name='decoder_input')
x = Dense(np.prod(shape_before_flattening))(decoder_input)
x = Reshape(shape_before_flattening)(x)
x = Conv2DTranspose(filters=64, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_0')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=64, kernel_size=3, strides=2, padding='same', name='decoder_conv_t_1')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=32, kernel_size=3, strides=2, padding='same', name='decoder_conv_t_2')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=32, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_2_5')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=3, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_3')(x)
x = Activation('sigmoid')(x)
decoder_output = x
decoder = Model(decoder_input, decoder_output)

# エンコーダ/デコーダ連結
model_input = encoder_input
model_output = decoder(encoder_output)
model = Model(model_input, model_output)

# 学習用設定設定（最適化関数、損失関数）
optimizer = Adam(learning_rate=LEARNING_RATE)

def r_loss(y_true, y_pred):
  return K.mean(K.square(y_true - y_pred), axis=[1,2,3])

model.compile(optimizer=optimizer, loss=r_loss)

# 学習実行
model.fit(
    train,
    train,
    batch_size=BATCH_SIZE,
    epochs=EPOCHS
)

#テスト用画像の読み込み
original_images = glob.glob("//*.png")
test = []
for n in original_images:
    img = cv2.imread(n)
    img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    test.append(img)

test = np.array(test)
test = test.astype('float32') / 255

# 画像の復元
z_points = encoder.predict(test)
reconst_images = decoder.predict(z_points)

# 元画像との差分を計算
diff_images = np.absolute(reconst_images - test)