python
复制代码
import torch
from torch import nn
from d2l import torch as d2l
net = nn.Sequential(
# 采用了11*11的卷积核来捕捉对象,因为原始输入数据比较大
#步幅为4 ,可减少输出的高度核宽度。
#输出通道为96,远大于Lenet
#卷积之后大小的计算:H_2 = (H_1 + 2 * padding - kernel_size) / stride + 1,向下取整
# 因为这里我们用的数据是fahsion_mnist ,输入通道就为1
# [1,224,224] => [1,96,54,54]
nn.Conv2d(in_channels=1,out_channels=96,kernel_size=11,stride=4,padding=1),nn.ReLU(),
#池化后的大小计算公式:H_2 = (H1 + 2 * padding - kernel_size) / stride + 1
# [1,96,54,54] => [1,96,26,26]
nn.MaxPool2d(kernel_size=3,stride=2),
#减小卷积窗口大小,使用padding = 2 来保证输入与输出的宽高一致,且增大输出通道。
# [1,96,26,26] => [1,256,26,26] H2=(26 + 2*2 -5)/1 +1
nn.Conv2d(in_channels=96,out_channels=256,kernel_size=5,padding=2),nn.ReLU(),
#[1,256,26,26] => [1,256,12,12]
nn.MaxPool2d(kernel_size=3,stride=2),
#三个卷积层
# [1,256,12,12] => [1,384,12,12]
nn.Conv2d(256,384, kernel_size=3, padding=1),nn.ReLU(),
# [1,384,12,12] => [1,384,12,12]
nn.Conv2d(384,384, kernel_size=3, padding=1),nn.ReLU(),
# [1,384,12,12] => [1,,256,12,12]
nn.Conv2d(384,256, kernel_size=3, padding=1),nn.ReLU(),
#[1,,256,12,12] => [1,256,5,5] 因为 H2 = (12 -3 )/2 +1 = 5
nn.MaxPool2d(kernel_size=3, stride=2),
#输入数据打平,传送给全连接层.
nn.Flatten(), # 256 * 5 * 5 = 6400
# 由于输入的数据过大,远远大于leNet网络,为了避免过拟合,可采用 dropout
nn.Linear(6400,4096),nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096,4096),nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096,10)
)
#加载数据集
batch_size = 128
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size,resize=224)
#训练模型:
#开始训练
lr,num_epochs = 0.01,10
d2l.train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())