基于预训练的图像多分类集成学习模型

Question

我正在尝试为医学图像分类任务创建一个包含三个预先训练好的VGG16、InceptionV3和EfficientNetB0的集成。以下是我基于Keras和Tensorflow后端的代码：

def load_all_models():
    all_models = []
    model_names = ['model1.h5', 'model2.h5', 'model3.h5']
    for model_name in model_names:
        filename = os.path.join('models', model_name)
        model = tf.keras.models.load_model(filename)
        all_models.append(model)
        print('loaded:', filename)
    return all_models


models = load_all_models()
for i, model in enumerate(models):
    for layer in model.layers:
        layer.trainable = False

print("[INFO] evaluation network ...")
model.evaluate(X_test, verbose=1)
predIdxs = model.predict(X_test, verbose=1)

predprobabilities = model.predict(X_test, verbose=1)
predIdxs = np.argmax(predprobabilities, axis=1)

print(classification_report(y_test.argmax(axis=1), predIdxs, target_names=lb.classes_))

前面的代码提供了以下输出：

​

然后，我将三个网络的输出连接到一个Dense层，如以下代码所示：

ensemble_visible = [model.input for model in models]
ensemble_outputs = [model.output for model in models]
merge = tf.keras.layers.concatenate(ensemble_outputs)
merge = tf.keras.layers.Dense(10, activation='relu')(merge)
output = tf.keras.layers.Dense(3, activation='sigmoid')(merge)
model = tf.keras.models.Model(inputs=ensemble_visible, outputs=output)

但是当我执行代码时，我得到了这个错误：

​

感谢您的帮助或建议，谢谢！

Answer 1

我们加载了三个模型，错误显示Flatten层的名称重复了三次。我们只需要改变名字，

models = load_all_models()
for i, model in enumerate(models):
   for layer in model.layers:
       if layer.name == "Flatten":
          layer.name = "Flatten_{}".format( i )
       layer.trainable = False

因此，我们将为Flatten_0、Flatten_1和Flatten_2这三个Flatten层提供唯一的名称。