1.授業内容

[DLBasic]深い学習の基本用語の説明

深い学習における重要な要素
1)Data that model can learn from
2)Model how to transform the data
3)Loss Fn that quantifies the badness of the model
4)Algorithm to adjust the parameters to minimize the loss

[DLBasic]新しいネットワーク-MLP

2.課題実行過程/成果整理

class MultiLayerPerceptronClass(nn.Module):
    """
        Multilayer Perceptron (MLP) Class
    """
    def __init__(self,name='mlp',xdim=784,hdim=256,ydim=10):
        super(MultiLayerPerceptronClass,self).__init__()
        self.name = name
        self.xdim = xdim
        self.hdim = hdim
        self.ydim = ydim
        self.lin_1 = nn.Linear(
        self.xdim, self.hdim
        )
        self.lin_2 = nn.Linear(
        self.hdim, self.ydim
        )
        self.init_param() # initialize parameters
        
    def init_param(self):
        nn.init.kaiming_normal_(self.lin_1.weight)
        nn.init.zeros_(self.lin_1.bias)
        nn.init.kaiming_normal_(self.lin_2.weight)
        nn.init.zeros_(self.lin_2.bias)

    def forward(self,x):
        net = x
        net = self.lin_1(net)
        net = F.relu(net)
        net = self.lin_2(net)
        return net

M = MultiLayerPerceptronClass(name='mlp',xdim=784,hdim=256,ydim=10).to(device)
loss = nn.CrossEntropyLoss()
optm = optim.Adam(M.parameters(),lr=1e-3)코드를 입력하세요

3.ピアセッション

学習内容の共有

1.課題コードコメント

1.授業内容と深化内容を討論する

3.その他の議論