将libFM模型变换成tensorflow可serving的形式

 import tensorflow as tf

 # libFM model

 def load_fm_model(file_name):

     state = ''

     fid = 0

     max_fid = 0

     w0 = 0.0

     wj = {}

     v = {}

     k = 0

     with open(file_name) as f:

         for line in f:

             line = line.rstrip()

             if 'global bias W0' in line:

                 state = 'w0'

                 fid = 0

                 continue

             elif 'unary interactions Wj' in line:

                 state = 'wj'

                 fid = 0

                 continue

             elif 'pairwise interactions Vj,f' in line:

                 state = 'v'

                 fid = 0

                 continue

             if state == 'w0':

                 fv = float(line)

                 w0 = fv

             elif state == 'wj':

                 fv = float(line)

                 if fv != 0:

                     wj[fid] = fv

                 fid += 1

                 max_fid = max(max_fid, fid)

             elif state == 'v':

                 fv = [float(_v) for _v in line.split(' ')]

                 k = len(fv)

                 if any([_v!=0 for _v in fv]):

                     v[fid] = fv

                 fid += 1

                 max_fid = max(max_fid, fid)

     return w0, wj, v, k, max_fid

 _w0, _wj, _v, _k, _max_fid = load_fm_model('libfm_model_file')

 # max feature_id

 n = _max_fid

 print 'n', n

 # vector dimension

 k = _k

 print 'k', k

 # write fm algorithm

 w0 = tf.constant(_w0)

 w1c = tf.constant([_wj.get(fid, 0) for fid in xrange(n)], shape=[n])

 w1 = tf.Variable(w1c)

 #print 'w1', w1

 vec = []

 for fid in xrange(n):

     vec.append(_v.get(fid, [0]*k))

 w2c = tf.constant(vec, shape=[n,k])

 w2 = tf.Variable(w2c)

 print 'w2', w2

 # inputs

 x = tf.placeholder(tf.string, [None])

 batch = tf.shape(x)[0]

 x_s = tf.string_split(x)

 inds = tf.stack([tf.cast(x_s.indices[:,0], tf.int64), tf.string_to_number(x_s.values, tf.int64)], axis=1)

 x_sparse = tf.sparse.SparseTensor(indices=inds, values=tf.ones([tf.shape(inds)[0]]), dense_shape=[batch,n])

 x_ = tf.sparse.to_dense(x_sparse)

 w2_rep = tf.reshape(tf.tile(w2, [batch,1]), [-1,n,k])

 print 'w2_rep', w2_rep

 x_rep = tf.reshape(tf.tile(tf.reshape(x_, [batch*n, 1]), [1,k]), [-1,n,k])

 print 'x_rep', x_rep

 x_rep2 = tf.square(x_rep)

 #print tf.multiply(w2_rep,x_rep)

 #print tf.reduce_sum(tf.multiply(w2_rep,x_rep), axis=1)

 q = tf.square(tf.reduce_sum(tf.multiply(w2_rep, x_rep), axis=1))

 h = tf.reduce_sum(tf.multiply(tf.square(w2_rep), x_rep2), axis=1)

 y = w0 + tf.reduce_sum(tf.multiply(x_, w1), axis=1) +\

     1.0/2 * tf.reduce_sum(q-h, axis=1)

 saver = tf.train.Saver()

 with tf.Session() as sess:

     sess.run(tf.global_variables_initializer())

     #a = sess.run(y, feed_dict={x_:x_train,y_:y_train,batch:70})

     #print a

     save_path = "./model.ckpt"

     tf.saved_model.simple_save(sess, save_path, inputs={"x": x}, outputs={"y": y})