6layerCNN.py 文件源码

def base_model():
    model = Sequential()

    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=x_train.shape[1:]))
    model.add(Dropout(0.2))

    model.add(Conv2D(32,(3,3),padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(64,(3,3),padding='same',activation='relu'))
    model.add(Dropout(0.2))

    model.add(Conv2D(64,(3,3),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Conv2D(128,(3,3),padding='same',activation='relu'))
    model.add(Dropout(0.2))

    model.add(Conv2D(128,(3,3),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))

    model.add(Flatten())
    model.add(Dropout(0.2))
    model.add(Dense(1024,activation='relu',kernel_constraint=maxnorm(3)))
    model.add(Dropout(0.2))
    model.add(Dense(num_classes, activation='softmax'))



    sgd = SGD(lr=0.01, momentum=0.9, decay=1e-6, nesterov=False)

# Train model

    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model