Efficient element-wise multiplication of a matrix and a vector in TensorFlow
The simplest code to do this relies on the broadcasting behavior of tf.multiply()*, which is based on numpy's broadcasting behavior:
x = tf.constant(5.0, shape=[5, 6])w = tf.constant([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])xw = tf.multiply(x, w)max_in_rows = tf.reduce_max(xw, 1)sess = tf.Session()print sess.run(xw)# ==> [[0.0, 5.0, 10.0, 15.0, 20.0, 25.0],# [0.0, 5.0, 10.0, 15.0, 20.0, 25.0],# [0.0, 5.0, 10.0, 15.0, 20.0, 25.0],# [0.0, 5.0, 10.0, 15.0, 20.0, 25.0],# [0.0, 5.0, 10.0, 15.0, 20.0, 25.0]]print sess.run(max_in_rows)# ==> [25.0, 25.0, 25.0, 25.0, 25.0]* In older versions of TensorFlow, tf.multiply() was called tf.mul(). You can also use the * operator (i.e. xw = x * w) to perform the same operation.