<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Convis Linear Filters
Step1:  One Dimensional Filtering
The Conv1d filter affects only the temporal dimension of the input by convolving it with a temporal weight.
Step2:  Two Dimensional Filtering
The Conv2d filter affects only the spatial dimension of the input by convolving it with a spatial weight.
Step3:  Thre Dimensional Filtering
The Conv3d filter is the most flexible filter and convolves the input with a spatio-temporal weight.
Step4:  RF
<ASSISTANT_TASK:>
Python Code:
%pylab inline
import convis
Explanation: Convis Linear Filters
End of explanation
model = convis.filters.Conv1d()
model.exponential(0.01)
model.plot_impulse()
Explanation: One Dimensional Filtering
The Conv1d filter affects only the temporal dimension of the input by convolving it with a temporal weight.
End of explanation
model = convis.filters.Conv2d()
model.gaussian(0.1)
model.plot_impulse((10,21,21))
Explanation: Two Dimensional Filtering
The Conv2d filter affects only the spatial dimension of the input by convolving it with a spatial weight.
End of explanation
model = convis.filters.Conv3d()
model.set_weight(randn(10,5,5))
model.plot_impulse((100,21,21))
Explanation: Thre Dimensional Filtering
The Conv3d filter is the most flexible filter and convolves the input with a spatio-temporal weight.
End of explanation
model = convis.filters.RF()
model.set_weight(randn(10,5,5))
model.plot_impulse((100,9,9))
model = convis.filters.RF(bias=False)
model.set_weight(randn(3,1,10,20,20))
inp = convis.samples.random_checker_stimulus(10,20,20).repeat(20,0)
o = model.run(inp,dt=200)
o
Explanation: RF: Receptive Field
The RF class has only a 1 pixel output, so different receptive fields have to be created by using additional out_channels that each has a 3d filter.
Similar to the Conv3d filter, RF does a 3d convolution, but it removes padding and cuts the input to the exact size of the weight. The result is a spatio-temporal receptive field that will give a single time series as an output.
End of explanation
<END_TASK>