nasm.py 文件源码

def build_encoder(self):
    """Inference Network. q(h|X)"""
    with tf.variable_scope("encoder"):
      q_cell = tf.nn.rnn_cell.LSTMCell(self.embed_dim, self.vocab_size)
      a_cell = tf.nn.rnn_cell.LSTMCell(self.embed_dim, self.vocab_size)

      l1 = tf.nn.relu(tf.nn.rnn_cell.linear(tf.expand_dims(self.x, 0), self.embed_dim, bias=True, scope="l1"))
      l2 = tf.nn.relu(tf.nn.rnn_cell.linear(l1, self.embed_dim, bias=True, scope="l2"))

      self.mu = tf.nn.rnn_cell.linear(l2, self.h_dim, bias=True, scope="mu")
      self.log_sigma_sq = tf.nn.rnn_cell.linear(l2, self.h_dim, bias=True, scope="log_sigma_sq")

      eps = tf.random_normal((1, self.h_dim), 0, 1, dtype=tf.float32)
      sigma = tf.sqrt(tf.exp(self.log_sigma_sq))

      _ = tf.histogram_summary("mu", self.mu)
      _ = tf.histogram_summary("sigma", sigma)

      self.h = self.mu + sigma * eps