spikingjelly.clock_driven.ann2snn.examples package

Submodules

spikingjelly.clock_driven.ann2snn.examples.if_cnn_mnist module

class spikingjelly.clock_driven.ann2snn.examples.cnn_mnist.ANN

基类：torch.nn.modules.module.Module

forward(x)

training: bool

spikingjelly.clock_driven.ann2snn.examples.cnn_mnist.main(log_dir=None)

返回

None

使用Conv-ReLU-[Conv-ReLU]-全连接-ReLU的网络结构训练并转换为SNN，进行MNIST识别。运行示例：

>>> import spikingjelly.clock_driven.ann2snn.examples.cnn_mnist as cnn_mnist
>>> cnn_mnist.main()
输入运行的设备，例如“cpu”或“cuda:0”
 input device, e.g., "cpu" or "cuda:0": cuda:15
输入保存MNIST数据集的位置，例如“./”
 input root directory for saving MNIST dataset, e.g., "./": ./mnist
输入batch_size，例如“64”
 input batch_size, e.g., "64": 128
输入学习率，例如“1e-3”
 input learning rate, e.g., "1e-3": 1e-3
输入仿真时长，例如“100”
 input simulating steps, e.g., "100": 100
输入训练轮数，即遍历训练集的次数，例如“10”
 input training epochs, e.g., "10": 10
输入模型名字，用于自动生成日志文档，例如“cnn_mnist”
 input model name, for log_dir generating , e.g., "cnn_mnist"

Epoch 0 [1/937] ANN Training Loss:2.252 Accuracy:0.078
Epoch 0 [101/937] ANN Training Loss:1.423 Accuracy:0.669
Epoch 0 [201/937] ANN Training Loss:1.117 Accuracy:0.773
Epoch 0 [301/937] ANN Training Loss:0.953 Accuracy:0.795
Epoch 0 [401/937] ANN Training Loss:0.865 Accuracy:0.788
Epoch 0 [501/937] ANN Training Loss:0.807 Accuracy:0.792
Epoch 0 [601/937] ANN Training Loss:0.764 Accuracy:0.795
Epoch 0 [701/937] ANN Training Loss:0.726 Accuracy:0.835
Epoch 0 [801/937] ANN Training Loss:0.681 Accuracy:0.880
Epoch 0 [901/937] ANN Training Loss:0.641 Accuracy:0.889
100%|██████████| 100/100 [00:00<00:00, 116.12it/s]
Epoch 0 [100/100] ANN Validating Loss:0.327 Accuracy:0.881
Save model to: cnn_mnist-XXXXX\cnn_mnist.pkl
......
--------------------simulator summary--------------------
time elapsed: 46.55072790000008 (sec)
---------------------------------------------------------

Module contents