Tensorflow function to compute cosine similarity between a column vector and the rows of a matrix.

cosine_similarity.py

import tensorflow as tf
import numpy as np


def cosine_similarity(matrix, vector):
    ''' Computes cosine similarity of a given vector with vector rows from matrix'''

    # normalize input
    norm_matrix = tf.nn.l2_normalize(matrix, 1)
    norm_vector = tf.nn.l2_normalize(vector, 0)

    # multiply row i with row j using transpose
    similarity = tf.matmul(norm_matrix, norm_vector)

    return similarity



with tf.Session() as sess:

    input_vector = tf.placeholder(tf.float32, shape=(None ), name='input_vector')
    input_matrix = tf.placeholder(tf.float32, shape=(None, None), name='input_matrix')
    sim = cosine_similarity(input_matrix, input_vector)
    
    input_matrix_ = np.array(
        [[ 1, 1, 1 ],
         [ 0, 1, 1 ],
         [ 0, 0, 1 ],
         ],
        dtype = 'float32')

    input_vector_ = np.ones((3,1))


    print (sess.run([sim], feed_dict = { input_matrix: input_matrix_, input_vector: input_vector_}))