Question

我正在与keras一起在预先训练的网络上应用转移学习。我有带有二进制类别标签的图像补丁，并且想使用CNN预测范围为[0; 1]用于看不见的图像补丁。

网络：ResNet50经过imageNet的预培训，并向其中添加了3层
数据：70305个训练样本，8000个验证样本，66823个测试样本，所有这些样本均带有均衡数量的两个类别标签
图像：3个波段（RGB）和224x224像素
设置：32个批次，转换大小层：16
结果：经过几个纪元，我的准确度几乎为1，而损失接近0，而在验证数据中，准确度仍为0.5，每个纪元的损失有所不同。最后，CNN会针对所有看不见的补丁预测仅一个类别。
问题：我的网络似乎过拟合了。

以下策略可以减少过度拟合：

增加批量大小
减小全连接层的大小
添加退出层
添加数据增强
通过修改损失函数应用正则化
解冻更多的预训练层
使用不同的网络架构

我尝试使用最大为512的批处理大小，并且更改了全连接层的大小，但没有成功。在仅对其余部分进行随机测试之前，我想问一下如何调查出问题的原因，以便找出以上哪种策略最具潜力。

在我的代码下面：

def generate_data(imagePathTraining, imagesize, nBatches):
    datagen = ImageDataGenerator(rescale=1./255)
    generator = datagen.flow_from_directory\
        (directory=imagePathTraining,                           # path to the target directory
         target_size=(imagesize,imagesize),                     # dimensions to which all images found will be resize
         color_mode='rgb',                                      # whether the images will be converted to have 1, 3, or 4 channels
         classes=None,                                          # optional list of class subdirectories
         class_mode='categorical',                              # type of label arrays that are returned
         batch_size=nBatches,                                   # size of the batches of data
         shuffle=True)                                          # whether to shuffle the data
    return generator

def create_model(imagesize, nBands, nClasses):
    print("%s: Creating the model..." % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
    # Create pre-trained base model
    basemodel = ResNet50(include_top=False,                     # exclude final pooling and fully connected layer in the original model
                         weights='imagenet',                    # pre-training on ImageNet
                         input_tensor=None,                     # optional tensor to use as image input for the model
                         input_shape=(imagesize,                # shape tuple
                                      imagesize,
                                      nBands),
                         pooling=None,                          # output of the model will be the 4D tensor output of the last convolutional layer
                         classes=nClasses)                      # number of classes to classify images into
    print("%s: Base model created with %i layers and %i parameters." %
          (datetime.now().strftime('%Y-%m-%d_%H-%M-%S'),
           len(basemodel.layers),
           basemodel.count_params()))

    # Create new untrained layers
    x = basemodel.output
    x = GlobalAveragePooling2D()(x)                             # global spatial average pooling layer
    x = Dense(16, activation='relu')(x)                         # fully-connected layer
    y = Dense(nClasses, activation='softmax')(x)                # logistic layer making sure that probabilities sum up to 1

    # Create model combining pre-trained base model and new untrained layers
    model = Model(inputs=basemodel.input,
                  outputs=y)
    print("%s: New model created with %i layers and %i parameters." %
          (datetime.now().strftime('%Y-%m-%d_%H-%M-%S'),
           len(model.layers),
           model.count_params()))

    # Freeze weights on pre-trained layers
    for layer in basemodel.layers:
        layer.trainable = False

    # Define learning optimizer
    optimizerSGD = optimizers.SGD(lr=0.01,                      # learning rate.
                                  momentum=0.0,                 # parameter that accelerates SGD in the relevant direction and dampens oscillations
                                  decay=0.0,                    # learning rate decay over each update
                                  nesterov=False)               # whether to apply Nesterov momentum

    # Compile model
    model.compile(optimizer=optimizerSGD,                       # stochastic gradient descent optimizer
                  loss='categorical_crossentropy',              # objective function
                  metrics=['accuracy'],                         # metrics to be evaluated by the model during training and testing
                  loss_weights=None,                            # scalar coefficients to weight the loss contributions of different model outputs
                  sample_weight_mode=None,                      # sample-wise weights
                  weighted_metrics=None,                        # metrics to be evaluated and weighted by sample_weight or class_weight during training and testing
                  target_tensors=None)                          # tensor model's target, which will be fed with the target data during training
    print("%s: Model compiled." % datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
    return model

def train_model(model, nBatches, nEpochs, imagePathTraining, imagesize, nSamples, valX,valY, resultPath):
    history = model.fit_generator(generator=generate_data(imagePathTraining, imagesize, nBatches),
                                  steps_per_epoch=nSamples//nBatches,     # total number of steps (batches of samples)
                                  epochs=nEpochs,               # number of epochs to train the model
                                  verbose=2,                    # verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch
                                  callbacks=None,               # keras.callbacks.Callback instances to apply during training
                                  validation_data=(valX,valY),  # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch
                                  class_weight=None,            # optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function
                                  max_queue_size=10,            # maximum size for the generator queue
                                  workers=32,                   # maximum number of processes to spin up when using process-based threading
                                  use_multiprocessing=True,     # whether to use process-based threading
                                  shuffle=True,                 # whether to shuffle the order of the batches at the beginning of each epoch
                                  initial_epoch=0)              # epoch at which to start training
    print("%s: Model trained." % datetime.now().strftime('%Y-%m-%d_%H-%M-%S')) 
    return history

Answer 1

这些结果似乎太糟糕了，无法解决过度拟合的问题。相反，我怀疑用于训练和验证的数据存在差异。

我注意到，对于训练数据，您正在使用ImageDataGenerator(rescale=1./255)，但是对于valX，我看不到任何此类处理。我建议对验证数据也使用具有相同缩放比例配置的单独ImageDataGenerator。这样差异就尽可能小。

Answer 2

根据上述建议，我进行了以下修改：

我修改了学习优化器（将学习率降低到0.001，并使其适应衰变）
我统一了数据生成器（用于培训和验证的ImageDataGenerator相同）
我使用了另一种经过预训练的基础CNN（VGG19代替ResNet50）
我将可训练的全连接层中的节点数从16个增加到1024个，从而提高了最终验证的准确性。
我将辍学率从0.5提高到0.8，从而最大程度地减少了训练和验证准确性之间的差距，从而限制了过度拟合的可能性

    def generate_data(path, imagesize, nBatches):
        datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
        generator = datagen.flow_from_directory(directory=path,     # path to the target directory
             target_size=(imagesize,imagesize),                     # dimensions to which all images found will be resize
             color_mode='rgb',                                      # whether the images will be converted to have 1, 3, or 4 channels
             classes=None,                                          # optional list of class subdirectories
             class_mode='categorical',                              # type of label arrays that are returned
             batch_size=nBatches,                                   # size of the batches of data
             shuffle=True,                                          # whether to shuffle the data
             seed=42)                                               # random seed for shuffling and transformations
        return generator

    def create_model(imagesize, nBands, nClasses):
        # Create pre-trained base model
        basemodel = VGG19(include_top=False,                        # exclude final pooling and fully connected layer in the original model
                             weights='imagenet',                    # pre-training on ImageNet
                             input_tensor=None,                     # optional tensor to use as image input for the model
                             input_shape=(imagesize,                # shape tuple
                                          imagesize,
                                          nBands),
                             pooling=None,                          # output of the model will be the 4D tensor output of the last convolutional layer
                             classes=nClasses)                      # number of classes to classify images into

        # Freeze weights on pre-trained layers
        for layer in basemodel.layers:
            layer.trainable = False   

        # Create new untrained layers
        x = basemodel.output
        x = GlobalAveragePooling2D()(x)                             # global spatial average pooling layer
        x = Dense(1024, activation='relu')(x)                       # fully-connected layer
        x = Dropout(rate=0.8)(x)                                    # dropout layer
        y = Dense(nClasses, activation='softmax')(x)                # logistic layer making sure that probabilities sum up to 1

        # Create model combining pre-trained base model and new untrained layers
        model = Model(inputs=basemodel.input,
                      outputs=y)

        # Define learning optimizer
        optimizerSGD = optimizers.SGD(lr=0.001,                     # learning rate.
                                      momentum=0.9,                 # parameter that accelerates SGD in the relevant direction and dampens oscillations
                                      decay=learningRate/nEpochs,   # learning rate decay over each update
                                      nesterov=True)                # whether to apply Nesterov momentum
        # Compile model
        model.compile(optimizer=optimizerSGD,                       # stochastic gradient descent optimizer
                      loss='categorical_crossentropy',              # objective function
                      metrics=['accuracy'],                         # metrics to be evaluated by the model during training and testing
                      loss_weights=None,                            # scalar coefficients to weight the loss contributions of different model outputs
                      sample_weight_mode=None,                      # sample-wise weights
                      weighted_metrics=None,                        # metrics to be evaluated and weighted by sample_weight or class_weight during training and testing
                      target_tensors=None)                          # tensor model's target, which will be fed with the target data during training
        return model

    def train_model(model, nBatches, nEpochs, trainGenerator, valGenerator, resultPath):
        history = model.fit_generator(generator=trainGenerator,
                                      steps_per_epoch=trainGenerator.samples // nBatches,   # total number of steps (batches of samples)
                                      epochs=nEpochs,               # number of epochs to train the model
                                      verbose=2,                    # verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch
                                      callbacks=None,               # keras.callbacks.Callback instances to apply during training
                                      validation_data=valGenerator, # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch
                                      validation_steps=
                                      valGenerator.samples // nBatches,                     # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
                                      class_weight=None,            # optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function
                                      max_queue_size=10,            # maximum size for the generator queue
                                      workers=1,                    # maximum number of processes to spin up when using process-based threading
                                      use_multiprocessing=False,    # whether to use process-based threading
                                      shuffle=True,                 # whether to shuffle the order of the batches at the beginning of each epoch
                                      initial_epoch=0)              # epoch at which to start training

        return history, model

通过这些修改，在训练了100个时期后，我达到了以下指标（批次大小为32）：

train_acc：0.831
train_loss：0.436
val_acc：0.692
val_loss：0.568

由于以下原因，我认为这些设置是最佳的：

准确性和损失曲线的行为与训练和验证相似
train_acc仅在30个纪元后才超过val_acc
最小过度拟合（train_acc和val_acc之间的微小差异）
train_loss和val_loss不断减少

但是，我想知道：

如果我应该训练更多的纪元以增加val_acc为代价，则以过度拟合为代价

sklearn.metrics classification_report()

predict_generator()

f1-score

precision

recall

也许，我最好在这些问题上提出一个新问题。

Answer 3

对于您为减少ResNet50的过拟合而不是将经过预先训练的CNN本身更改为VGG所实施的工作，我表示赞赏。

如何选择减少过度拟合的策略？

3 个答案: