Classifier

Classifier modules.

class lava.lib.dl.slayer.classifier.MovingWindow(time_window, mode='probability', eps=1e-06)

Moving window based classifier. It produces a timeseries of classification/prediction based on moving window estimate.

\text{rate: } {\bf r}(t) &= \frac{1}{W}\int_{t-W}^T {\bf s}(t)\,\text dt \ \text{confidence: } {\bf c}(t) &= \begin{cases} \frac{{\bf r}(t)}{{\bf r}(t)^\top {\bf1}} &\text{ if mode=probability} \\ \frac{\exp({\bf r}(t))}{\exp({\bf r}(t))^\top \bf1} &\text{ if mode=softmax} \\ \log\left( \frac{\exp({\bf r}(t))}{\exp({\bf r}(t))^\top \bf1} \right) &\text{ if mode=softmax} \end{cases} \ \text{prediction: } p(t) &= \arg\max({\bf r}(t))

Parameters:

• time_window (int) – size of moving window.

• mode (str) – confidence mode. One of ‘probability’|’softmax’|’logsoftmax’. Defaults to ‘probability’.

• eps (float) – infinitesimal value. Defaults to 1e-6.

Examples

>>> classifier = MovingWindow(20)
>>> prediction = classifier(spike)

confidence(spike, mode=None)

Moving window confidence.

Parameters:

• spike (torch tensor) – spike input.

• mode (str) – confidence mode. If it is None, the object’s mode is used. Defaults to None.

Returns:

output confidence.

Return type:

torch tensor

Examples

>>> confidence = classifier.confidence(spike)

forward(spike)

predict(spike)

Moving window prediction.

Parameters:

spike (torch tensor) – spike input.

Returns:

output prediction.

Return type:

torch tensor

Examples

>>> prediction = classifier.predict(spike)

rate(spike)

Moving window spike rate.

Parameters:

spike (torch tensor) – spike input.

Returns:

spike rate.

Return type:

torch tensor

Examples

>>> rate = classifier.rate(spike)

class lava.lib.dl.slayer.classifier.Rate

Global rate based classifier. It considers the event rate of the spike train over the entire duration as the confidence score.

\text{rate: } {\bf r} &= \frac{1}{T}\int_T{\bf s}(t)\,\text dt\ \text{confidence: } {\bf c} &= \begin{cases} \frac{\bf r}{\bf r^\top 1} &\text{ if mode=probability} \\ \frac{\exp({\bf r})}{\exp({\bf r})^\top \bf1} &\text{ if mode=softmax} \\ \log\left( \frac{\exp({\bf r})}{\exp({\bf r})^\top \bf1} \right) &\text{ if mode=softmax} \end{cases} \\ \text{prediction: } p &= \arg\max(\bf r)

Examples

>>> classifier = Rate
>>> prediction = classifier(spike)

static confidence(spike, mode='probability', eps=1e-06)

Given spike train, returns the confidence of the output class based on spike rate.

Parameters:

• spike (torch tensor) – spike tensor. First dimension is assumed to be batch, and last dimension is assumed to be time. Spatial dimensions are collapsed by default.

• mode (str) – confidence mode. One of ‘probability’|’softmax’|’logsoftmax’. Defaults to ‘probability’.

• eps (float) – infinitesimal value. Defaults to 1e-6.

Returns:

confidence.

Return type:

torch tensor

Examples

>>> confidence = classifier.confidence(spike)
>>> confidence = Rate.confidence(spike)

forward(spike)

static predict(spike)

Given spike train, predicts the output class based on spike rate.

Parameters:

spike (torch tensor) – spike tensor. First dimension is assumed to be batch, and last dimension is assumed to be time. Spatial dimensions are collapsed by default.

Returns:

indices of max spike activity.

Return type:

torch tensor

Examples

>>> prediction = classifier.predict(spike)
>>> prediction = Rate.predict(spike)

static rate(spike)

Given spike train, returns the output spike rate.

Parameters:

spike (torch tensor) – spike tensor. First dimension is assumed to be batch, and last dimension is assumed to be time. Spatial dimensions are collapsed by default.

Returns:

spike rate.

Return type:

torch tensor

Examples

>>> rate = classifier.rate(spike)
>>> rate = Rate.rate(spike)