我在保存模型后恢复训练时遇到了问题.

问题是我的损失从例如6到3减少.这时我保存了模型.

当我恢复它并继续训练时,损失从6开始重新开始.

似乎恢复并不真正起作用.

我不明白,因为打印重量,似乎它们正确加载.

我使用ADAM优化器.提前致谢.

这里：

    batch_size = self.batch_size 
    num_classes = self.num_classes

    n_hidden = 50 #700 
    n_layers = 1 #3
    truncated_backprop = self.seq_len 
    dropout = 0.3 
    learning_rate = 0.001
    epochs = 200

    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [batch_size, truncated_backprop], name='x')
        y = tf.placeholder(tf.int32, [batch_size, truncated_backprop], name='y')

    with tf.name_scope('weights'):
        W = tf.Variable(np.random.rand(n_hidden, num_classes), dtype=tf.float32)
        b = tf.Variable(np.random.rand(1, num_classes), dtype=tf.float32)

    inputs_series = tf.split(x, truncated_backprop, 1)
    labels_series = tf.unstack(y, axis=1)

    with tf.name_scope('LSTM'):
        cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, state_is_tuple=True)
        cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=dropout)
        cell = tf.contrib.rnn.MultiRNNCell([cell] * n_layers)

    states_series, current_state = tf.contrib.rnn.static_rnn(cell, inputs_series, \
        dtype=tf.float32)

    logits_series = [tf.matmul(state, W) + b for state in states_series]
    prediction_series = [tf.nn.softmax(logits) for logits in logits_series]

    losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels) \
        for logits, labels, in zip(logits_series, labels_series)]
    total_loss = tf.reduce_mean(losses)

    train_step = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)

    tf.summary.scalar('total_loss', total_loss)
    summary_op = tf.summary.merge_all()

    loss_list = []
    writer = tf.summary.FileWriter('tf_logs', graph=tf.get_default_graph())

    all_saver = tf.train.Saver()

    with tf.Session() as sess:
        #sess.run(tf.global_variables_initializer())
        tf.reset_default_graph()
        saver = tf.train.import_meta_graph('./models/tf_models/rnn_model.meta')
        saver.restore(sess, './models/tf_models/rnn_model')

        for epoch_idx in range(epochs):
            xx, yy = next(self.get_batch)
            batch_count = len(self.D.chars) // batch_size // truncated_backprop

            for batch_idx in range(batch_count):
                batchX, batchY = next(self.get_batch)

                summ, _total_loss, _train_step, _current_state, _prediction_series = sess.run(\
                    [summary_op, total_loss, train_step, current_state, prediction_series],
                    feed_dict = {
                        x : batchX,
                        y : batchY
                    })

                loss_list.append(_total_loss)
                writer.add_summary(summ, epoch_idx * batch_count + batch_idx)
                if batch_idx % 5 == 0:
                    print('Step', batch_idx, 'Batch_loss', _total_loss)

                if batch_idx % 50 == 0:
                    all_saver.save(sess, 'models/tf_models/rnn_model')

            if epoch_idx % 5 == 0:
                print('Epoch', epoch_idx, 'Last_loss', loss_list[-1])

最佳答案 我有同样的问题,在我的情况下,模型正在被正确恢复,但损失一次又一次地开始很高,问题是我的批量撤销不是随机的.我有三个班级,A,B和C.我的数据是以这种方式喂A,然后是B,然后是C.我不知道这是不是你的问题,但你必须确保你给模型的每一批都有所有类都在其中,因此在您的情况下,批处理必须每个类都有batch_size / num_classes输入.我改变它,一切都很完美:)

看看你是否正确喂养你的模型.