deep-learning - 训练和验证准确性产生过拟合

Question

下面是 CNN 模型的代码，问题是训练准确率为 96%，验证准确率为 69%。帮助我提高验证的准确性。

`model = Sequential()`
`model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape=(128,128,1), padding ='same', name='Conv_1'))`
`model.add(MaxPooling2D((2,2),name='MaxPool_1'))
`model.add(Conv2D(64, (3, 3), activation = 'relu',padding ='same', name='Conv_2'))
`model.add(MaxPooling2D((2,2),name='MaxPool_2'))
`model.add(Conv2D(128, (3, 3), activation = 'relu', padding ='same', name='Conv_3'))
`model.add(Flatten(name='Flatten'))`
`model.add(Dropout(0.5,name='Dropout'))
`model.add(Dense(128, kernel_initializer='normal', activation='relu', name='Dense_1'))
`model.add(Dense(1, kernel_initializer='normal', activation='sigmoid', name='Dense_2'))`
`model.summary()`

`model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])`

`history = model.fit(x_train2, y_train2, epochs=25, batch_size=10, verbose=2, validation_data=(x_test, y_test))`

结果：训练：准确度 = 0.937500；损失 = 0.125126 测试：准确度 = 0.662508 ；损失 = 1.089228