tf.py 文件源码

def get_mixture_coef(output, KMIX=24, OUTPUTDIM=1):
  out_pi = tf.placeholder(dtype=tf.float32, shape=[None,KMIX], name="mixparam")
  out_sigma = tf.placeholder(dtype=tf.float32, shape=[None,KMIX], name="mixparam")
  out_mu = tf.placeholder(dtype=tf.float32, shape=[None,KMIX*OUTPUTDIM], name="mixparam")
  splits = tf.split(1, 2 + OUTPUTDIM, output)
  out_pi = splits[0]
  out_sigma = splits[1]
  out_mu = tf.pack(splits[2:], axis=2)
  out_mu = tf.transpose(out_mu, [1,0,2])
  # use softmax to normalize pi into prob distribution
  max_pi = tf.reduce_max(out_pi, 1, keep_dims=True)
  out_pi = tf.sub(out_pi, max_pi)
  out_pi = tf.exp(out_pi)
  normalize_pi = tf.inv(tf.reduce_sum(out_pi, 1, keep_dims=True))
  out_pi = tf.mul(normalize_pi, out_pi)
  # use exponential to make sure sigma is positive
  out_sigma = tf.exp(out_sigma)
  return out_pi, out_sigma, out_mu