python - CNN过度拟合(带有输出和代码)

我有一个包含两万张黑白图像的数据集，我想对它们进行分类（这些图像看起来有点像天气预报或股市图表，所以我不能使用预先训练的网络）数据集已被分割成18000个图像，用于
用于测试的培训和2000张图片我正在用凯拉斯卷积神经网络训练它。我有96%的准确率
使用训练集，但使用测试集得到的结果不好（在50个阶段后，它停留在82-83%的水平）我想可能是因为太合身了。拜托
你能给我提些解决问题的建议吗我把最后的输出和代码留给你看。
281/281[=========]-132s-损失：0.1024-账户：0.9612-价值损失：0.5836-价值账户：0.8210

from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.optimizers import SGD, RMSprop, Adam
from keras.callbacks import ModelCheckpoint


filepath="weights/weights-improvement-{epoch:02d}-{val_acc:.4f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]


img_width, img_height = 100, 1296

train_data_dir = 'dataset/train'
validation_data_dir = 'dataset/validation'
nb_train_samples = 18000
nb_validation_samples = 2000
epochs = 100
batch_size = 64

input_shape = (img_width, img_height, 1)

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.1))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.4))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

model.add(Flatten())

model.add(Dense(256))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(256))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
               optimizer=Adam(lr=0.001),
              metrics=['accuracy'])

model.save('model.h5')

train_datagen = ImageDataGenerator(
    rescale=None,
)

test_datagen = ImageDataGenerator(rescale=None)

train_generator = train_datagen.flow_from_directory(
    train_data_dir, color_mode='grayscale',
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir, color_mode='grayscale',
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,callbacks=callbacks_list)

最佳答案

你的网络中有大量的参数-由于有几个大的卷积层和两个大的完全连接层。这可能导致过度安装我建议减小它们的尺寸，也许完全去掉几层。