Question

我用MLP尝试了下面的代码，现在我需要用以下结构的CNN 8层替换此MLP代码：3×3×32卷积→3×3×64卷积→2×2 MaxPool→辍学→展平→ 1×128完全连接→退出→128×10完全连接→Softmax。

//declear path to your mnist data folder
img_path = 'c:/kaggleMNISTdata/trainingSet/trainingSet'
 //get the path list using the path object
image_paths = list(paths.list_images(img_path))
//apply our function
image_list, label_list = load(image_paths, verbose=10000)
// binarize the labels
lb = LabelBinarizer()
label_list = lb.fit_transform(label_list)
// split data into training and test set
X_train, X_test, y_train, y_test = train_test_split(image_list,
                                                    label_list,
                                                    test_size=0.1,
                                                    random_state=42)
data = list(zip(image_list, label_list))
random.shuffle(data)
class SimpleMLP:
    @staticmethod
    def build(shape, classes):
        model = Sequential()
        model.add(Dense(200, input_shape=(shape,)))
        model.add(Activation("relu"))
        model.add(Dense(200))
        model.add(Activation("relu"))
        model.add(Dense(classes))
        model.add(Activation("softmax"))
        return model

我尝试关注

def build(shape, classes):
        model = Sequential()
        model.add(Conv2D(200, input_shape=(shape,)))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Flatten(1))
        model.add(Dropout(1))
        model.add(Dense(200))
        model.add(Activation("softmax"))
        return model

对吗？

Answer 1

尝试使用给定的体系结构：

cnn_model = Sequential()
cnn_model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 1)))
cnn_model.add(Conv2D(64, (3, 3)))
cnn_model.add(MaxPooling2D((2, 2)))
cnn_model.add(Dropout(0.5))
cnn_model.add(Flatten())
cnn_model.add(Dense(128))
cnn_model.add(Dropout(0.5))
cnn_model.add(Dense(classes))
cnn_model.add(Activation("softmax"))

使用tensorflow和keras在Python中用CNN替换MLP代码

1 个答案: