Model(
(network): Network(
(Conv2d_1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))
(Relu_1): ReLU()
(AvgPool_1): AvgPool2d(kernel_size=2, stride=2, padding=0)
(Linear_1): Linear(in_features=1690, out_features=100, bias=True)
(Dropout_1): Dropout(p=0.1, inplace=False)
(Elu_1): ELU(alpha=1.0)
(Linear_2): Linear(in_features=100, out_features=50, bias=True)
(Dropout_2): Dropout(p=0.1, inplace=False)
(Tanh_1): Tanh()
(Linear_3): Linear(in_features=50, out_features=10, bias=True)
(Dropout_3): Dropout(p=0.1, inplace=False)
(LogSoftMax_1): LogSoftmax(dim=None)
)
)| 参数 | 范围 | 意义 |
|---|---|---|
| type_pool | 0, 1 | 0:MaxPool 1:AvgPool |
| type_active | 0,1,2 | 0:Relu 1:Elu 2:Tanh |
| out_channels | [5, 16] | 通道数 |
| kernel_size | 3, 5 | 卷积核的大小 |
| padding | 0, 1, 2 | Padding尺寸 |
| linear_layer_out_1 | [100, 200] | 线性层1的output_size |
| drop_out_1 | [0., 0.6] | 线性层1的dropout |
| active_type_1 | 0,1,2 | 0:Relu 1:Elu 2:Tanh |
| linear_layer_out_2 | [20, 50] | 线性层2的output_size |
| drop_out_2 | [0., 0.6] | 线性层2的dropout |
| active_type_2 | 0,1,2 | 0:Relu 1:Elu 2:Tanh |
| linear_layer_out_3 | 10 | 线性层3的output_size |
| drop_out_3 | [0., 0.6] | 线性层3的dropout |
| last_layer_type | 0,1,2,3 | 0:Sigmoid 1:LogSigmoid 2:SoftMax 3:LogSoftMax |
| optim_type | 0,1 | 0:SGD 1:Adam |
| lr | [10e-3, 10e-1] | 学习率 |
