用R语言写一个ResNeSt

ResNeSt是一种新型的神经网络结构，它在ResNet的基础上进行了改进，可以更好地提取图像特征。目前，R语言并没有原生支持ResNeSt，但可以使用Keras或TensorFlow等深度学习框架来实现。

以下是使用Keras实现ResNeSt的代码示例：

library(keras)

# 定义ResNeSt块
resnest_block <- function(filters, stride = 1, downsample = FALSE) {
  shortcut <- layer_input(shape = NULL)
  x <- layer_batch_normalization(shortcut)
  x <- layer_activation(x, activation = "relu")
  if (downsample) {
    shortcut <- layer_conv_2d(x, filters = filters, kernel_size = 1, strides = stride, use_bias = FALSE)
    x <- layer_conv_2d(x, filters = filters / 2, kernel_size = 1, strides = stride, use_bias = FALSE)
  } else {
    shortcut <- x
    x <- layer_conv_2d(x, filters = filters / 2, kernel_size = 1, strides = 1, use_bias = FALSE)
  }
  x <- layer_batch_normalization(x)
  x <- layer_activation(x, activation = "relu")
  x <- layer_conv_2d(x, filters = filters / 2, kernel_size = 3, strides = 1, padding = "same", use_bias = FALSE)
  x <- layer_batch_normalization(x)
  x <- layer_activation(x, activation = "relu")
  x <- layer_conv_2d(x, filters = filters, kernel_size = 1, strides = 1, use_bias = FALSE)
  x <- layer_add(list(x, shortcut))
  return(x)
}

# 定义ResNeSt网络
resnest_network <- function(input_shape, depth, base_filters = 64, num_classes = 1000) {
  input <- layer_input(shape = input_shape)
  x <- layer_conv_2d(input, filters = base_filters, kernel_size = 7, strides = 2, padding = "same", use_bias = FALSE)
  x <- layer_batch_normalization(x)
  x <- layer_activation(x, activation = "relu")
  x <- layer_max_pooling_2d(x, pool_size = 3, strides = 2, padding = "same")
  for (i in 1:depth) {
    if (i == 1) {
      x <- resnest_block(filters = base_filters * 2, stride = 1, downsample = TRUE)(x)
    } else {
      x <- resnest_block(filters = base_filters * 2, stride = 1, downsample = FALSE)(x)
    }
  }
  for (i in 1:depth) {
    if (i == 1) {
      x <- resnest_block(filters = base_filters * 4, stride = 2, downsample = TRUE)(x)
    } else {
      x <- resnest_block(filters = base_filters * 4, stride = 1, downsample = FALSE)(x)
    }
  }
  for (i in 1:depth) {
    if (i == 1) {
      x <- resnest_block(filters = base_filters * 8, stride = 2, downsample = TRUE)(x)
    } else {
      x <- resnest_block(filters = base_filters * 8, stride = 1, downsample = FALSE)(x)
    }
  }
  x <- layer_global_average_pooling_2d(x)
  output <- layer_dense(x, units = num_classes, activation = "softmax")
  model <- keras_model(input, output)
  return(model)
}

# 测试ResNeSt网络
model <- resnest_network(input_shape = c(224, 224, 3), depth = 18)
summary(model)

这段代码定义了一个ResNeSt网络，包括了ResNeSt块的定义和网络的构建。你可以更改depth、base_filters和num_classes的值来调整网络的深度和输出类别数。需要注意的是，这段代码仅供参考，实际应用时可能需要根据具体情况进行调整。