def call(self, inputs):
"""
:Note: Equivalent to __call__()
:param inputs: Tensor to be applied
:type inputs: tf.Tensor
:return: Tensor after applying the layer which is just n-deg P(inputs)
:rtype: tf.Tensor
"""
polylist = []
output_list = []
for k in range(self.output_units):
for j in range(self.input_dim):
polylist.append([
tf.multiply(intpow_avx2(inputs[:, j], i), self.kernel[i, j,
k])
for i in range(self.deg + 1)
])
if self.use_bias:
polylist[j].append(inputs[:, j])
output_list.append(
tf.add_n(
[tf.add_n(polylist[jj]) for jj in range(self.input_dim)]))
output = tf.stack(output_list, axis=-1)
if self.activation is not None:
output = self.activation(output)
return output
def _ndegmul(self, inputs, kernel):
polylist = []
output_list = []
for k in range(self.output_units):
for j in range(self.input_dim):
polylist.append([tf.multiply(intpow_avx2(inputs[:, j], i),
kernel[i, j, k]) for i in range(self.deg + 1)])
if self.use_bias:
polylist[j].append(inputs[:, j])
output_list.append(tf.add_n([tf.add_n(polylist[jj]) for jj in range(self.input_dim)]))
output = tf.stack(output_list, axis=-1)
return output