strides=(1, 1), padding="same", activation="relu", kernel_initializer="uniform"
时间: 2024-05-18 12:15:03 浏览: 22
这是一段代码,用于定义一个卷积层的参数。其中strides=(1,1)表示卷积核在水平和竖直方向上的步长均为1;padding="same"表示在卷积运算时,对输入数据进行填充,保证输出特征图的尺寸与输入特征图一致;activation="relu"表示使用ReLU作为激活函数;kernel_initializer="uniform"表示使用均匀分布初始化卷积核参数。
相关问题
def cnn(rate, convDim, dim, optimizer): model = Sequential() model.add(Conv2D(2 * convDim, (5, 5), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) model.add(Conv2D(convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (2, 2), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) model.add(Flatten()) model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=2 * dim, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=2 * dim, kernel_initializer='uniform', activation='relu')) # model.add(Dense(256, kernel_initializer = 'uniform', activation='softmax')) model.add(Dense(7, kernel_initializer='uniform', activation='softmax')) ###############################改组数 model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy']) return model 减少这个模型的神经元数量
要减少这个模型的神经元数量,可以通过减少每个层的 units 参数来实现。例如,将第一个 Dense 层的 units 参数从 dim 减少到 dim/2,将第二个 Dense 层的 units 参数从 2 * dim 减少到 dim。具体修改如下:
```
def cnn(rate, convDim, dim, optimizer):
model = Sequential()
model.add(Conv2D(2 * convDim, (5, 5), strides=1, activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(2 * convDim, (3, 3), strides=1, activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate))
model.add(Conv2D(convDim, (3, 3), strides=1, activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(2 * convDim, (2, 2), strides=1, activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate))
model.add(Flatten())
model.add(Dense(units=dim/2, kernel_initializer='uniform', activation='relu'))
model.add(Dropout(rate / 2))
model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu'))
model.add(Dropout(rate / 2))
model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu'))
model.add(Dense(7, kernel_initializer='uniform', activation='softmax'))
model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
return model
```
conv_1 = tf.keras.layers.Conv2D(filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same', activation="relu", name="conv_1", kernel_initializer='glorot_uniform')(inputs)
这是一段使用 TensorFlow 的卷积神经网络实现的代码,使用了 Conv2D 层。这段代码旨在在输入数据上执行 2D 卷积。filters 参数指定卷积核数量,kernel_size 参数指定卷积核的大小,strides 参数指定步长大小。padding 参数控制填充方式。activation 参数指定激活函数。kernel_initializer 参数则指定卷积核初始化方式。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)