提高 CNN 模型的准确性（陷入非常低的准确性）

Question

Hereby is my code: for CNN training on image recognition

python

# definiton of code

def make_model():


    model = Sequential()
    model.add(Conv2D(16, (3,3),input_shape = (32,32,3), padding = "same", 
    kernel_initializer="glorot_uniform"))
    model.add(LeakyReLU(alpha=0.1))

    model.add(Conv2D(32, (3,3),input_shape = (32,32,3), padding = "same", 
    kernel_initializer="glorot_uniform"))
    model.add(LeakyReLU(alpha=0.1))
    model.add(MaxPooling2D(pool_size = (2,2),padding = "same"))
    model.add(Dropout(0.25))*

  

    model.add(Conv2D(32,(3,3), input_shape = (32,32,3), padding = "same"))
    
    model.add(LeakyReLU(alpha=0.1))

    model.add(Conv2D(64, (3,3),input_shape = (32,32,3), padding = "same"))
    model.add(LeakyReLU(alpha=0.1))

    model.add(MaxPooling2D(pool_size = (2,2),padding = "same"))
    model.add(Dropout(0.25))

    *layer*

    model.add(Flatten())
    model.add(Dense(256))

    *for activation*
    model.add(LeakyReLU(alpha=0.1))
    model.add(Dropout(0.5))
    
    model.add(Dense(10))

    *for activation*
    model.add(LeakyReLU(alpha=0.1))

    model.add(Activation("softmax"))

And then it stuck around with the result which freak me out:

loss: 7.4918; acc: 0.1226. 

I have been trying few more way but I don't know exactly what I should do for the right path.

Answer 1

Without details of the problem, it is difficult to investigate more.

But I would encourage you to look more into :

• BatchNormalization
• loss function
• learning rate
• optimizer
• hidden layers

Answer 2

The current state of the art is to apply convolution together with Batch Normalization and ReLU activation. The order should be the following:

1. Convolution
2. Batch Normalization
3. ReLU(It could also be leaky ReLu or any other activation)

So you should add BN after your convolutions and then you should also remove DropOut. It has been studied by many researchers that Dropout is not needed if BN is used and BN performs actually better.

Other than this you should probably play around with the parameters like learning rate, number of filters and etc.

Also make sure that you are using a correct loss and a output activation corresponding your loss.