tensorflow中如何进行可视化和减轻过拟合

TensorFlow可视化界面与过拟合

最近因为一些需要所以做了一个关于TensorFlow如何使用tensorboard进行可视化以及如何减轻模型训练过程中的过拟合现象的小demo。这里就直接发出来供大家参考~~本文代码可以前往我的github进行查看。

TensorFlow可视化界面–tensorboard介绍

tensorflow提供了一个十分强大的功能–tensorboard可视化面板，我想这也是很多人选择使用tf的原因。有了它我们就可以很方便的查看模型训练过程中loss、accuracy、weight、biase、lr等等元素的变化过程。并且其会自动帮助我们绘制图表，大大简化我们训练模型的时间也有助于调参，也可以查看我们所设计的模型架构。而且只需要简单的几行代码就可以实现很炫酷的功能。首先给一个官网的README连接，可以先去看一下了解其用法和功能。TensorBoard大致界面如下图所示：

TensorBoard可视化示例–mnist

1，代码

我们使用官网上面的mnist示例来介绍tensorboard的基础用法，首先看一下其代码：

def train():
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data)
  sess = tf.InteractiveSession()

  with tf.name_scope('input'):
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')

  with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)

  def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

  def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

  def variable_summaries(var):
    with tf.name_scope('summaries'):
      mean = tf.reduce_mean(var)
      tf.summary.scalar('mean', mean)
      with tf.name_scope('stddev'):
        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
      tf.summary.scalar('stddev', stddev)
      tf.summary.scalar('max', tf.reduce_max(var))
      tf.summary.scalar('min', tf.reduce_min(var))
      tf.summary.histogram('histogram', var)

  def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    with tf.name_scope(layer_name):
      with tf.name_scope('weights'):
        weights = weight_variable([input_dim, output_dim])
        variable_summaries(weights)
      with tf.name_scope('biases'):
        biases = bias_variable([output_dim])
        variable_summaries(biases)
      with tf.name_scope('Wx_plus_b'):
        preactivate = tf.matmul(input_tensor, weights) + biases
        tf.summary.histogram('pre_activations', preactivate)
      activations = act(preactivate, name='activation')
      tf.summary.histogram('activations', activations)
      return activations

  hidden1 = nn_layer(x, 784, 500, 'layer1')

  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar('dropout_keep_probability', keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)

  y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

  with tf.name_scope('cross_entropy'):
    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
    with tf.name_scope('total'):
      cross_entropy = tf.reduce_mean(diff)
  tf.summary.scalar('cross_entropy', cross_entropy)

  with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
        cross_entropy)