张量流中的图像再训练，将简单的softmax层更改为多层CNN

Question

Tensorflow发布了一个用于迁移学习的教程，名为Image retraining，可以在以下位置找到：https：//www.tensorflow.org/tutorials/image_retraining

他们正在做的是在Inception v3上使用预先训练的模型，然后他们仅更改最后一层（softmax回归层）并将其训练到新数据集上。这是非常容易理解的，实际上是迁移学习中的常见做法。

我已经在我的数据集（这是一个很小的数据集）上尝试了他们的方法，并且我已经应用了所有建议以从数据扩充中获得更好的结果来更改步骤数，但是我没有以任何方式修改他们的代码。我得到的准确度相对较差〜70％。

我正在考虑在给定模型的顶部训练小型神经网络的可能性，即将最后一层从简单回归更改为更复杂的网络。

这是他们的代码部分，用于修改softmax层：

def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
  """Adds a new softmax and fully-connected layer for training.
  We need to retrain the top layer to identify our new classes, so this function
  adds the right operations to the graph, along with some variables to hold the
  weights, and then sets up all the gradients for the backward pass.
  The set up for the softmax and fully-connected layers is based on:
  https://tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
  Args:
    class_count: Integer of how many categories of things we're trying to
    recognize.
    final_tensor_name: Name string for the new final node that produces results.
    bottleneck_tensor: The output of the main CNN graph.
  Returns:
    The tensors for the training and cross entropy results, and tensors for the
    bottleneck input and ground truth input.
  """
  with tf.name_scope('input'):
    bottleneck_input = tf.placeholder_with_default(
        bottleneck_tensor, shape=[None, BOTTLENECK_TENSOR_SIZE],
        name='BottleneckInputPlaceholder')

    ground_truth_input = tf.placeholder(tf.float32,
                                        [None, class_count],
                                        name='GroundTruthInput')

  # Organizing the following ops as `final_training_ops` so they're easier
  # to see in TensorBoard
  layer_name = 'final_training_ops'
  with tf.name_scope(layer_name):
    with tf.name_scope('weights'):
      layer_weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001), name='final_weights')
      variable_summaries(layer_weights)
    with tf.name_scope('biases'):
      layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
      variable_summaries(layer_biases)
    with tf.name_scope('Wx_plus_b'):
      logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
      tf.summary.histogram('pre_activations', logits)

  final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
  tf.summary.histogram('activations', final_tensor)

  with tf.name_scope('cross_entropy'):
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
        labels=ground_truth_input, logits=logits)
    with tf.name_scope('total'):
      cross_entropy_mean = tf.reduce_mean(cross_entropy)
  tf.summary.scalar('cross_entropy', cross_entropy_mean)

  with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(
        cross_entropy_mean)

  return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
          final_tensor)


def add_evaluation_step(result_tensor, ground_truth_tensor):
  """Inserts the operations we need to evaluate the accuracy of our results.

  Args:
    result_tensor: The new final node that produces results.
    ground_truth_tensor: The node we feed ground truth data
    into.

  Returns:
    Tuple of (evaluation step, prediction).
  """
  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      prediction = tf.argmax(result_tensor, 1)
      correct_prediction = tf.equal(
          prediction, tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  return evaluation_step, prediction

但是，我面临两个主要问题。首先，这不是一个好主意吗？我会浪费我的精力去做一些无用的事情吗？其次，他们使用简单的MNIST教程作为最后一层的模型，说我会使用他们的Expert MNIST教程（https://www.tensorflow.org/get_started/mnist/pros）我不知道该怎么做或如何配置？

有什么建议我该怎么办？