Question

Tensorflow发布了一个名为Image retraining的转学习教程，可以在这里找到： https://www.tensorflow.org/tutorials/image_retraining

他们正在做的是在初始v3上使用预先训练的模型，然后他们只更改最后一层（softmax回归层）并在新数据集上训练它。这是非常容易理解的，实际上是转学习中的常见做法。

我在我的数据集（这是一个小数据集）上尝试了他们的方法，我已经应用了所有建议，以便从数据扩充中获得更好的结果来改变步骤数但我没有通过任何方式修改他们的代码。我得到的准确性相对较差~70％。

我正在考虑在给定模型之上训练小型神经网络的可能性，即将最后一层从简单回归改为更复杂的网络。

以下是他们修改softmax图层的代码部分：

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，如果这不是一个好主意，我不是吗？我会在努力做一些无用的事情上浪费吗？其次，他们使用简单的MNIST教程作为最后一层的模型，说我会使用他们的专家MNIST教程（https://www.tensorflow.org/get_started/mnist/pros）我迷失了做什么或如何配置它？

有关我该怎么办的任何建议？

张量流中的图像重新训练，将简单的softmax层改变为多层CNN

0 个答案: