Tensorflow 模型恢复（恢复训练似乎从头开始）

Question

我在保存模型后恢复训练时遇到问题。问题是我的损失从 6 减少到 3。这时我保存模型。当我恢复它并继续训练时，损失从 6 重新开始。看来恢复真的不行。我不明白，因为打印重量时，它们似乎已正确加载。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])

Answer 1

我有同样的问题，在我的例子中，模型被正确恢复，但损失一次又一次地开始非常高，问题是我的批量恢复不是随机的。我有三个 classes，A、B 和 C。我的数据以这种方式输入，然后是 A，然后是 B，然后是 C。我不知道这是否是你的问题，但你必须确保每一批你给你的模型有你所有的 classes 在里面，所以在你的情况下，批次必须有 batch_size/num_classes 输入每个 class。我更改了它，一切都运行良好:)

检查您是否正确喂食模型。

Answer 2

我的问题是标签中的代码错误，它们在两个运行之间变化。所以现在可以了。感谢您的帮助

Tensorflow 模型恢复（恢复训练似乎从头开始）

Tensorflow model restoration (resume training seems starting from scratch)

restore

save

tensorflow

resuming-training