dssm.py 文件源码

def get_dssm():
  doc_pos = mx.sym.Variable('doc_pos')
  doc_neg = mx.sym.Variable('doc_neg')
  data_usr = mx.sym.Variable("data_usr", stype='csr')

  #with mx.AttrScope(ctx_group="cpu"):
  w_usr = mx.sym.Variable('usr_weight', stype='row_sparse', shape=(USR_NUM, OUT_DIM))
  # shared weights
  w1 = mx.sym.Variable('fc1_doc_weight')
  w2 = mx.sym.Variable('fc2_doc_weight')
  w3 = mx.sym.Variable('fc3_doc_weight')
  b1 = mx.sym.Variable('fc1_doc_bias')
  b2 = mx.sym.Variable('fc2_doc_bias')
  b3 = mx.sym.Variable('fc3_doc_bias')

  def cosine(usr, doc):
    dot = usr * doc
    dot = mx.sym.sum_axis(dot, axis=1)
    return dot

  def doc_mlp(data):
    fc1 = mx.sym.FullyConnected(data=data, num_hidden=num_hidden, name='fc1', weight=w1, bias=b1)
    fc1 = mx.sym.Activation(data=fc1, act_type='relu')
    fc2 = mx.sym.FullyConnected(data=fc1, num_hidden=num_hidden, name='fc2', weight=w2, bias=b2)
    fc2 = mx.sym.Activation(data=fc2, act_type='relu')
    fc3 = mx.sym.FullyConnected(data=fc2, num_hidden=OUT_DIM, name='fc3', weight=w3, bias=b3)
    fc3 = mx.sym.Activation(data=fc3, act_type='relu')
    fc3 = mx.sym.L2Normalization(data=fc3)
    return fc3

  # usr net
  #with mx.AttrScope(ctx_group="cpu"):
  usr1 = mx.sym.dot(data_usr, w_usr)
  usr = mx.sym.L2Normalization(data=usr1)
  # doc net
  mlp_pos = doc_mlp(doc_pos)
  mlp_neg = doc_mlp(doc_neg)

  cosine_pos = cosine(usr, mlp_pos)
  cosine_neg = cosine(usr, mlp_neg)
  exp = mx.sym.exp(data=(cosine_neg - cosine_pos))
  pred = mx.sym.log1p(data=exp)
  out = mx.sym.MAERegressionOutput(data=pred, name='mae')
  return out