human
I am working on the file "Korepanova/Korepanova_theanets.ipynb".
The first task is:
sparsity: A float giving the proportion of parameter values in the layer that should be initialized to zero. Nonzero values in the parameters will be drawn from a Gaussian distribution and then an appropriate number of these parameter values will randomly be reset to zero to make the parameter “sparse.”
Custom Layers
To create a custom layer, just create a subclass of theanets.Layer and give it the functionality you want.
An example of a normal feedforward layer but without a bias term:
Can you write Python code for it?
 Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

import theanets
import theano.tensor as TT

class NoBias(theanets.Layer):
    # Transform the inputs for this layer into an output for the layer.
    def transform(self, inputs):
        return TT.dot(inputs, self.find('w'))
    
    #Helper method to create a new weight matrix.
    def setup(self):
        self.add_weights('w', nin=self.input_size, nout=self.size)
        

layer = theanets.Layer.build('nobias', size=4)
net = theanets.Autoencoder(layers=[4, (3, 'nobias', 'linear'), (4, 'tied', 'linear')])