Back propagation on ChebyShev polynomial

Hi @mdeff,

l'm wondering if Chebyshev polynomial  is concerned by the backpropagation or just the result of Chebyshev polynomial.

in 
https://github.com/mdeff/cnn_graph/blob/master/lib/models.py#L495

    def _inference(self, x, dropout):
        with tf.name_scope('gconv1'):
            N, M = x.get_shape()  # N: number of samples, M: number of features
            M = int(M)
            # Transform to Chebyshev basis
            xc = tf.transpose(x)  # M x N
            def chebyshev(x):
                return graph.chebyshev(self.L, x, self.K)
            xc = tf.py_func(chebyshev, [xc], [tf.float32])[0]
            xc = tf.transpose(xc)  # N x M x K
            xc = tf.reshape(xc, [-1, self.K])  # NM x K
            # Filter
            W = self._weight_variable([self.K, self.F])
            y = tf.matmul(xc, W)  # NM x F
            y = tf.reshape(y, [-1, M, self.F])  # N x M x F
            # Bias and non-linearity
#            b = self._bias_variable([1, 1, self.F])
            b = self._bias_variable([1, M, self.F])
            y += b  # N x M x F
            y = tf.nn.relu(y)
        with tf.name_scope('fc1'):
            W = self._weight_variable([self.F*M, NCLASSES])
            b = self._bias_variable([NCLASSES])
            y = tf.reshape(y, [-1, self.F*M])
            y = tf.matmul(y, W) + b
return y

l would like to know if only :
y = tf.nn.relu(y) https://github.com/mdeff/cnn_graph/blob/master/lib/models.py#L514 which concerned by backpropagation or also :

            xc = tf.py_func(chebyshev, [xc], [tf.float32])[0]
            xc = tf.transpose(xc)  # N x M x K
            xc = tf.reshape(xc, [-1, self.K])  # NM x K
            # Filter
            W = self._weight_variable([self.K, self.F])
            y = tf.matmul(xc, W)  # NM x F
y = tf.reshape(y, [-1, M, self.F]) # N x M x F

https://github.com/mdeff/cnn_graph/blob/master/lib/models.py#L503

Thank you a lot for your answer.

mdeff / cnn_graph

Back propagation on ChebyShev polynomial #37