| library(mxnet) |
| |
| get_symbol <- function(num_classes = 1000) { |
| input_data <- mx.symbol.Variable(name = "data") |
| # stage 1 |
| conv1 <- mx.symbol.Convolution(data = input_data, kernel = c(11, 11), stride = c(4, 4), num_filter = 96) |
| relu1 <- mx.symbol.Activation(data = conv1, act_type = "relu") |
| lrn1 <- mx.symbol.LRN(data = relu1, alpha = 0.0001, beta = 0.75, knorm = 2, nsize = 5) |
| pool1 <- mx.symbol.Pooling(data = lrn1, kernel = c(3, 3), stride = c(2, 2), pool_type = "max") |
| # stage 2 |
| conv2 <- mx.symbol.Convolution(data = lrn1, kernel = c(5, 5), pad = c(2, 2), num_filter = 256) |
| relu2 <- mx.symbol.Activation(data = conv2, act_type = "relu") |
| lrn2 <- mx.symbol.LRN(data = relu2, alpha = 0.0001, beta = 0.75, knorm = 2, nsize = 5) |
| pool2 <- mx.symbol.Pooling(data = lrn2, kernel = c(3, 3), stride = c(2, 2), pool_type = "max") |
| # stage 3 |
| conv3 <- mx.symbol.Convolution(data = lrn2, kernel = c(3, 3), pad = c(1, 1), num_filter = 384) |
| relu3 <- mx.symbol.Activation(data = conv3, act_type = "relu") |
| conv4 <- mx.symbol.Convolution(data = relu3, kernel = c(3, 3), pad = c(1, 1), num_filter = 384) |
| relu4 <- mx.symbol.Activation(data = conv4, act_type = "relu") |
| conv5 <- mx.symbol.Convolution(data = relu4, kernel = c(3, 3), pad = c(1, 1), num_filter = 256) |
| relu5 <- mx.symbol.Activation(data = conv5, act_type = "relu") |
| pool3 <- mx.symbol.Pooling(data = relu5, kernel = c(3, 3), stride = c(2, 2), pool_type = "max") |
| # stage 4 |
| flatten <- mx.symbol.Flatten(data = pool3) |
| fc1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = 4096) |
| relu6 <- mx.symbol.Activation(data = fc1, act_type = "relu") |
| dropout1 <- mx.symbol.Dropout(data = relu6, p = 0.5) |
| # stage 5 |
| fc2 <- mx.symbol.FullyConnected(data = dropout1, num_hidden = 4096) |
| relu7 <- mx.symbol.Activation(data = fc2, act_type = "relu") |
| dropout2 <- mx.symbol.Dropout(data = relu7, p = 0.5) |
| # stage 6 |
| fc3 <- mx.symbol.FullyConnected(data = dropout2, num_hidden = num_classes) |
| softmax <- mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax') |
| return(softmax) |
| } |
