最近在训练一个多位数字手写体的模型，然后发现，我用ModelCheckpoint 保存了训练过程中的结果最好一轮的参数。后续用模型来预测新样本的时候，就从直接本地加载训练的模型，代码如下：

def build_model():
    input_ = Input(shape=(64, 64, 1))
    
    #block1
    model = Conv2D(64, (5, 5), activation ='relu', padding='same')(input_)
    model = Conv2D(64, (5, 5), activation ='relu', padding='same')(model)
    model = MaxPool2D(pool_size=(2, 2))(model)
    #block2
    model = Conv2D(128, (5, 5), activation ='relu', padding='same')(model)
    model = Conv2D(128, (5, 5), activation ='relu', padding='same')(model)
    model = MaxPool2D(pool_size=(2, 2))(model)
    #block3
    model = Conv2D(256, (5, 5), activation ='relu', padding='same')(model)
    model = Conv2D(256, (5, 5), activation ='relu', padding='same')(model)
    model = Conv2D(256, (5, 5), activation ='relu', padding='same')(model)
    model = MaxPool2D(pool_size=(2, 2))(model)
#     #block4
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = MaxPool2D(pool_size=(2, 2))(model)
#     #block5
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = Conv2D(512, (3, 3), activation ='relu', padding='same')(model)
#     model = MaxPool2D(pool_size=(2, 2))(model)
    
    # fully connected layer
    model = Flatten()(model)

    model = Dense(1024, activation='relu')(model)
    model = Dropout(0.6)(model)
    
    x1 = Dense(11, activation='softmax')(model)
    x2 = Dense(11, activation='softmax')(model)

    x = [x1, x2]

    model = Model(inputs=input_, outputs=x)
    return model

model = build_model()
model.load_weights('best_weights.h5',by_name=True)

我发现，本来训练的模型测试集和验证集效果都很好，但是预测的时候效果特别差，而且每次效果都不一样，我就觉得我训练的参数模型没有用上，模型应该是随机参数，所以才导致预测效果差，而且每次都不一样。想了一想，觉得问题只可能出在加载模型model.load_weights('best_weights.h5',by_name=True)上面，所以研究了by_name这个参数。
下面是官方说明：

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