character_rnn.py 文件源码

def get_training_tensors(self, learning_rate = 0.001, grad_clip = 5):
        #-----------------------------------------------------------------------
        # Build a loss function
        #-----------------------------------------------------------------------
        with tf.name_scope('targets-encode'):
            y_one_hot  = tf.one_hot(self.targets, self.n_classes)
            y_reshaped = tf.reshape(y_one_hot, self.logits.get_shape())

        with tf.name_scope('loss'):
            loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits,
                                                           labels=y_reshaped)
            loss = tf.reduce_mean(loss)
            tf.summary.scalar('loss', loss)

        #-----------------------------------------------------------------------
        # Build the optimizer
        #-----------------------------------------------------------------------
        with tf.name_scope('optimizer'):
            tvars     = tf.trainable_variables()
            grads, _  = tf.clip_by_global_norm(tf.gradients(loss, tvars),
                                               grad_clip)
            train_op  = tf.train.AdamOptimizer(learning_rate)
            optimizer = train_op.apply_gradients(zip(grads, tvars))

        return loss, optimizer