nasm.py 文件源码

def build_model(self):
    self.q = tf.placeholder(tf.float32, [self.reader.vocab_size], name="question")
    self.a = tf.placeholder(tf.float32, [self.reader.vocab_size], name="answer")

    self.build_encoder()
    self.build_decoder()

    # Kullback Leibler divergence
    self.e_loss = -0.5 * tf.reduce_sum(1 + self.log_sigma_sq - tf.square(self.mu) - tf.exp(self.log_sigma_sq))

    # Log likelihood
    self.g_loss = tf.reduce_sum(tf.log(self.p_x_i))

    self.loss = tf.reduce_mean(self.e_loss + self.g_loss)
    self.optim = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(-self.loss)

    _ = tf.scalar_summary("encoder loss", self.e_loss)
    _ = tf.scalar_summary("decoder loss", self.g_loss)
    _ = tf.scalar_summary("loss", self.loss)