theanets/layers/convolution.py
def add_conv_weights(self, name, mean=0, std=None, sparsity=0):
        '''Add a convolutional weight array to this layer's parameters.

        Parameters
        ----------
        name : str
            Name of the parameter to add.
        mean : float, optional
            Mean value for randomly-initialized weights. Defaults to 0.
        std : float, optional
            Standard deviation of initial matrix values. Defaults to
            :math:`1 / sqrt(n_i + n_o)`.
        sparsity : float, optional
            Fraction of weights to set to zero. Defaults to 0.
        '''
        nin = self.input_size
        nout = self.output_size
        mean = self.kwargs.get(
            'mean_{}'.format(name),
            self.kwargs.get('mean', mean))
        std = self.kwargs.get(
            'std_{}'.format(name),
            self.kwargs.get('std', std or 1 / np.sqrt(nin + nout)))
        sparsity = self.kwargs.get(
            'sparsity_{}'.format(name),
            self.kwargs.get('sparsity', sparsity))
        arr = np.zeros((nout, nin) + self.filter_size, util.FLOAT)
        for r in range(self.filter_size[0]):
            for c in range(self.filter_size[1]):
                arr[:, :, r, c] = util.random_matrix(
                    nout, nin, mean, std, sparsity=sparsity, rng=self.rng)
        self._params.append(theano.shared(arr, name=self._fmt(name)))
def add_conv_weights(self, name, mean=0, std=None, sparsity=0):
        '''Add a convolutional weight array to this layer's parameters.

        Parameters
        ----------
        name : str
            Name of the parameter to add.
        mean : float, optional
            Mean value for randomly-initialized weights. Defaults to 0.
        std : float, optional
            Standard deviation of initial matrix values. Defaults to
            :math:`1 / sqrt(n_i + n_o)`.
        sparsity : float, optional
            Fraction of weights to set to zero. Defaults to 0.
        '''
        nin = self.input_size
        nout = self.output_size
        mean = self.kwargs.get(
            'mean_{}'.format(name),
            self.kwargs.get('mean', mean))
        std = self.kwargs.get(
            'std_{}'.format(name),
            self.kwargs.get('std', std or 1 / np.sqrt(nin + nout)))
        sparsity = self.kwargs.get(
            'sparsity_{}'.format(name),
            self.kwargs.get('sparsity', sparsity))
        arr = np.zeros((nout, nin) + self.filter_size, util.FLOAT)
        for r in range(self.filter_size[0]):
            for c in range(self.filter_size[1]):
                arr[:, :, r, c] = util.random_matrix(
                    nout, nin, mean, std, sparsity=sparsity, rng=self.rng)
        self._params.append(theano.shared(arr, name=self._fmt(name)))
def add_conv_weights ( self , name , mean = 0 , std = None , sparsity = 0 ) : nin = self . input_size nout = self . output_size mean = self . kwargs . get ( 'mean_{}' . format ( name ) , self . kwargs . get ( 'mean' , mean ) ) std = self . kwargs . get ( 'std_{}' . format ( name ) , self . kwargs . get ( 'std' , std or 1 / np . sqrt ( nin + nout ) ) ) sparsity = self . kwargs . get ( 'sparsity_{}' . format ( name ) , self . kwargs . get ( 'sparsity' , sparsity ) ) arr = np . zeros ( ( nout , nin ) + self . filter_size , util . FLOAT ) for r in range ( self . filter_size [ 0 ] ) : for c in range ( self . filter_size [ 1 ] ) : arr [ : , : , r , c ] = util . random_matrix ( nout , nin , mean , std , sparsity = sparsity , rng = self . rng ) self . _params . append ( theano . shared ( arr , name = self . _fmt ( name ) ) )
Add a convolutional weight array to this layer's parameters.

        Parameters
        ----------
        name : str
            Name of the parameter to add.
        mean : float, optional
            Mean value for randomly-initialized weights. Defaults to 0.
        std : float, optional
            Standard deviation of initial matrix values. Defaults to
            :math:`1 / sqrt(n_i + n_o)`.
        sparsity : float, optional
            Fraction of weights to set to zero. Defaults to 0.
Add a convolutional weight array to this layer s parameters .