spikingjelly.activation_based.encoding package
Module contents
- class spikingjelly.activation_based.encoding.StatelessEncoder(step_mode='s')[源代码]
基类：
Module,StepModule无状态编码器的基类。无状态编码器
encoder = StatelessEncoder()，直接调用encoder(x)即可将x编码为spike。The base class of stateless encoder. The stateless encoder
encoder = StatelessEncoder()can encodextospikebyencoder(x).- abstract forward(x: Tensor)[源代码]
-
- 参数:
x (torch.Tensor) – 输入数据
- 返回:
spike, shape 与x.shape相同- 返回类型:
- 参数:
x (torch.Tensor) – input data
- 返回:
spike, whose shape is same withx.shape- 返回类型:
- class spikingjelly.activation_based.encoding.StatefulEncoder(T: int, step_mode='s')[源代码]
基类：
MemoryModule- 参数:
T (int) – 编码周期。通常情况下，与SNN的仿真周期（总步长一致）
有状态编码器的基类。有状态编码器
encoder = StatefulEncoder(T)，编码器会在首次调用encoder(x)时对x进行编码。在第t次调用encoder(x)时会输出spike[t % T]encoder = StatefulEncoder(T) s_list = [] for t in range(T): s_list.append(encoder(x)) # s_list[t] == spike[t]
- 参数:
T (int) – the encoding period. It is usually same with the total simulation time-steps of SNN
The base class of stateful encoder. The stateful encoder
encoder = StatefulEncoder(T)will encodextospikeat the first time of callingencoder(x). It will outputspike[t % T]at thet-th callingencoder = StatefulEncoder(T) s_list = [] for t in range(T): s_list.append(encoder(x)) # s_list[t] == spike[t]
- single_step_forward(x: Optional[Tensor] = None)[源代码]
-
- 参数:
x (torch.Tensor) – 输入数据
- 返回:
spike, shape 与x.shape相同- 返回类型:
- 参数:
x (torch.Tensor) – input data
- 返回:
spike, whose shape is same withx.shape- 返回类型:
- abstract single_step_encode(x: Tensor)[源代码]
-
- 参数:
x (torch.Tensor) – 输入数据
- 返回:
spike, shape 与x.shape相同- 返回类型:
- 参数:
x (torch.Tensor) – input data
- 返回:
spike, whose shape is same withx.shape- 返回类型:
- class spikingjelly.activation_based.encoding.PeriodicEncoder(spike: Tensor, step_mode='s')[源代码]
-
- 参数:
spike (torch.Tensor) – 输入脉冲
周期性编码器，在第
t次调用时输出spike[t % T]，其中T = spike.shape[0]警告
不要忘记调用reset，因为这个编码器是有状态的。
- 参数:
spike (torch.Tensor) – the input spike
The periodic encoder that outputs
spike[t % T]att-th calling, whereT = spike.shape[0]Warning
Do not forget to reset the encoder because the encoder is stateful!
- class spikingjelly.activation_based.encoding.LatencyEncoder(T: int, enc_function='linear', step_mode='s')[源代码]
-
延迟编码器，将
0 <= x <= 1的输入转化为在0 <= t_f <= T-1时刻发放的脉冲。输入的强度越大，发放越早。 当enc_function == 'linear'\[t_f(x) = (T - 1)(1 - x)\]- 当
enc_function == 'log' - \[t_f(x) = (T - 1) - ln(\alpha * x + 1)\]
其中 \(lpha\) 满足 \(t_f(1) = T - 1\)
实例代码：
x = torch.rand(size=[8, 2]) print('x', x) T = 20 encoder = LatencyEncoder(T) for t om range(T): print(encoder(x))
警告
必须确保
0 <= x <= 1。警告
不要忘记调用reset，因为这个编码器是有状态的。
- 参数:
The latency encoder will encode
0 <= x <= 1to spike whose firing time is0 <= t_f <= T-1. A largerxwill cause a earlier firing time.- If
enc_function == 'linear' - \[t_f(x) = (T - 1)(1 - x)\]
- If
enc_function == 'log' - \[t_f(x) = (T - 1) - ln(\alpha * x + 1)\]
where \(lpha\) satisfies \(t_f(1) = T - 1\)
Example: .. code-block:: python
x = torch.rand(size=[8, 2]) print(‘x’, x) T = 20 encoder = LatencyEncoder(T) for t in range(T):
print(encoder(x))
Warning
The user must assert
0 <= x <= 1.Warning
Do not forget to reset the encoder because the encoder is stateful!
- 当
- class spikingjelly.activation_based.encoding.PoissonEncoder(step_mode='s')[源代码]
-
无状态的泊松编码器。输出脉冲的发放概率与输入
x相同。警告
必须确保
0 <= x <= 1。The poisson encoder will output spike whose firing probability is
x。Warning
The user must assert
0 <= x <= 1.
- class spikingjelly.activation_based.encoding.WeightedPhaseEncoder(K: int, step_mode='s')[源代码]
-
- 参数:
K (int) – 编码周期。通常情况下，与SNN的仿真周期（总步长一致）
Kim J, Kim H, Huh S, et al. Deep neural networks with weighted spikes[J]. Neurocomputing, 2018, 311: 373-386.
带权的相位编码，一种基于二进制表示的编码方法。
将输入按照二进制各位展开，从高位到低位遍历输入进行脉冲编码。相比于频率编码，每一位携带的信息量更多。编码相位数为 \(K\) 时， 可以对于处于区间 \([0, 1-2^{-K}]\) 的数进行编码。以下为原始论文中的示例：
Phase (K=8)
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警告
不要忘记调用reset，因为这个编码器是有状态的。
- 参数:
K (int) – the encoding period. It is usually same with the total simulation time-steps of SNN
The weighted phase encoder, which is based on binary system. It will flatten
xas a binary number. WhenT=k, it can encode \(x \in [0, 1-2^{-K}]\) to different spikes. Here is the example from the origin paper:Phase (K=8)
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Warning
Do not forget to reset the encoder because the encoder is stateful!
- class spikingjelly.activation_based.encoding.PopSpikeEncoderDeterministic(obs_dim, pop_dim, spike_ts, mean_range, std)[源代码]
基类：
ModuleLearnable Population Coding Spike Encoder with Deterministic Spike Trains