Efficient element-wise multiplication of a matrix and a vector in TensorFlow
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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.
Comments
-
Andrzej Pronobis almost 4 years
What would be the most efficient way to multiply (element-wise) a 2D tensor (matrix):
x11 x12 .. x1N ... xM1 xM2 .. xMN
by a vertical vector:
w1 ... wN
to obtain a new matrix:
x11*w1 x12*w2 ... x1N*wN ... xM1*w1 xM2*w2 ... xMN*wN
To give some context, we have
M
data samples in a batch that can be processed in parallel, and eachN
-element sample must be multiplied by weightsw
stored in a variable to eventually pick the largestXij*wj
for each rowi
.