Python 如何在迁移学习ResNet-50期间解决此问题？

我正在训练Resnet-50将9个班级进行分类。我使用下面的代码，转移学习，来训练模型

训练和测试的损失和准确性似乎还不错，但当我根据新图像测试网络时，我看到了很多错误。 我觉得这个模型学习不好，我想知道你是否能让我知道我的方法有什么问题？我如何解决这个问题

NUM_CLASSES = 9

CHANNELS = 3

IMAGE_RESIZE = 224
RESNET50_POOLING_AVERAGE = 'avg'
DENSE_LAYER_ACTIVATION = 'softmax'
OBJECTIVE_FUNCTION = 'categorical_crossentropy'

LOSS_METRICS = ['accuracy']

NUM_EPOCHS = 100
EARLY_STOP_PATIENCE = 3


STEPS_PER_EPOCH_TRAINING = 10
STEPS_PER_EPOCH_VALIDATION = 10


BATCH_SIZE_TRAINING = 100
BATCH_SIZE_VALIDATION = 100

BATCH_SIZE_TESTING = 1

from tensorflow.python.keras.applications import ResNet50
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense



resnet_weights_path = '/path/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
model = Sequential()

model.add(ResNet50(include_top = False, pooling = RESNET50_POOLING_AVERAGE, weights = resnet_weights_path))

model.add(Dense(NUM_CLASSES, activation = DENSE_LAYER_ACTIVATION))

model.layers[0].trainable = False

from tensorflow.python.keras import optimizers

sgd = optimizers.SGD(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)
model.compile(optimizer = sgd, loss = OBJECTIVE_FUNCTION, metrics = LOSS_METRICS)


model.summary()
from tensorflow.python.keras import optimizers

sgd = optimizers.SGD(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = True)
model.compile(optimizer = sgd, loss = OBJECTIVE_FUNCTION, metrics = LOSS_METRICS)


from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator

image_size = IMAGE_RESIZE


data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)


train_generator = data_generator.flow_from_directory(
        '/path train folder/train',
        target_size=(image_size, image_size),
        batch_size=BATCH_SIZE_TRAINING,
        class_mode='categorical')

validation_generator = data_generator.flow_from_directory(
        '/path test folder/test',
        target_size=(image_size, image_size),
        batch_size=BATCH_SIZE_VALIDATION,
        class_mode='categorical')


(BATCH_SIZE_TRAINING, len(train_generator), BATCH_SIZE_VALIDATION, len(validation_generator))


from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint

cb_early_stopper = EarlyStopping(monitor = 'val_loss', patience = EARLY_STOP_PATIENCE)
cb_checkpointer = ModelCheckpoint(filepath = '/path/best.hdf5', monitor = 'val_loss', save_best_only = True, mode = 'auto')




fit_history = model.fit_generator(
        train_generator,
        steps_per_epoch=STEPS_PER_EPOCH_TRAINING,
        epochs = NUM_EPOCHS,
        validation_data=validation_generator,
        validation_steps=STEPS_PER_EPOCH_VALIDATION,
        callbacks=[cb_checkpointer, cb_early_stopper]
)
model.load_weights("/path/best.hdf5")

model.save('transfer_resnet.h5')


print(fit_history.history.keys())

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这可能有很多原因。 首先，您的数据集可能太小或变化不够。

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您可以尝试在顶部添加一些更密集的层。

我认为您可以通过删除不必要的部分将代码减少一半。这将使我们更容易提供帮助。此外，还缺少一些关键部分，例如，你进行预测的部分。我的数据集不小，@Nicolas I编辑了我的帖子。