Help understanding biases update during training

I need help understanding what exactly the following piece of code does (it's from pointnet). I don't have tensorflow so I cannot run it to test it.

weights = tf.get_variable('weights', [256, 9], initializer=tf.constant_initializer(0.0), dtype=tf.float32) biases = tf.get_variable('biases', [9], initializer=tf.constant_initializer(0.0), dtype=tf.float32) biases += tf.constant([1,0,0,0,1,0,0,0,1], dtype=tf.float32) transform = tf.matmul(net, weights) transform = tf.nn.bias_add(transform, biases) 

From my understanding, after the first run the transform will be equal to biases, i.e. [1,0,0,0,1,0,0,0,1] , because weights initially are zero. During the training, the weights and …

biases deeplearning training