计算该卷积神经网络的网络层数和网络参数量 conv_layers = [ # 先创建包含多层的列表 # unit 1 # 64 个 3x3 卷积核, 输入输出同大小 layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 2 # 输出通道提升至 128，高宽大小减半 layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 3 # ,输出通道提升至 256，高宽大小减半 layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 4 # 输出通道提升至 512，高宽大小减半 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 5 # 输出通道提升至 512，高宽大小减半 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same') ]

该卷积神经网络的网络层数为 10 层，其中每个 unit 包含两个卷积层和一个最大池化层，共有 5 个 unit。该网络的参数量为 14,714,688，可以通过以下代码计算：

total_params = 0
for layer in conv_layers:
    if isinstance(layer, layers.Conv2D):
        layer_params = tf.reduce_prod(layer.kernel.shape) + tf.reduce_prod(layer.bias.shape)
        total_params += layer_params
print("Total number of parameters:", total_params.numpy())

相关问题

阐述该卷积神经网络conv_layers = [ # 先创建包含多层的列表 # unit 1 # 64 个 3x3 卷积核, 输入输出同大小 layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 2 # 输出通道提升至 128，高宽大小减半 layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 3 # ,输出通道提升至 256，高宽大小减半 layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 4 # 输出通道提升至 512，高宽大小减半 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), # unit 5 # 输出通道提升至 512，高宽大小减半 layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same') ]

该代码段是一个包含多个卷积层的卷积神经网络，其输入为一个二维图像。该卷积神经网络的设计遵循经典的 VGG 网络结构，包含 5 个卷积单元和池化层。其中每个卷积单元都由两个卷积层和一个池化层组成，每个卷积层都使用了 3x3 的卷积核和 ReLU 激活函数。第一个卷积单元的输出通道数为 64，接下来每个卷积单元都将输出通道数翻倍，依次为 128、256、512 和 512。每个池化层的大小为 2x2，步长为 2，采用了 same padding（即边界补零）的方式保持输入输出同大小。该卷积神经网络最终输出的特征图可以送入全连接层进行分类或回归任务。