学习笔记TF063:TensorFlow Debugger

TensorFlow Debugger(tfdbg)，TensorFlow专用调试器。用断点、计算机图形化展现实时数据流，可视化运行TensorFlow图形内部结构、状态。有助训练推理调试模型错误。https://www.tensorflow.org/programmers_guide/debugger 。

常见错误类型，非数字(nan)、无限值(inf)。tfdbg命令行界面(command line interface,CLI)。

Debugger示例。错误运行MNIST训练，通过TensorFlow Debugger找到出错地方，改正。https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/debug_mnist.py 。

先直接执行。

python -m tensorflow.python.debug.examples.debug_mnist

准确率第一次训练上千，后面保持较低水平。
TensorFlow Debugger，在每次调用run()前后基于终端用户界面(UI)，控制执行、检查图内部状态。

from tensorflow.python import debug as tf_debugsess = tr.debug.LocalCLIDebugWrapperSession(sess)sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)

张量值注册过滤器has_inf_on_nan，判断图中间张量是否有nan、inf值。
开启调试模式(debug)。

python -m tensorflow.python.debug.examples.debug_mnist -debugpython debug_mnist.py --debug=True

运行开始UI(run-start UI)，在tfdbg>后输入交互式命令，run()进入运行结束后UI(run-end UI)。连续运行10次

tfdbg>run -t 10

找出图形第一个nan或inf值

tfdbg> run -f has_inf_or_nan

第一行灰底字表示tfdbg在调用run()后立即停止，生成指定过滤器has_inf_or_nan中间张量。第4次调用run()，36个中间张量包含inf或nan值。首次出现在cross_entropy/Log:0。单击图中cross_entropy/Log:0,单击下划线node_info菜单项，看节点输入张量，是否有0值。

tfdbg>pt softmax/Softmax:0

用ni命令-t标志追溯
ni -t cross_entropy/Log
问题代码

diff = -(y_ * tf.log(y))

修改，对tf.log输入值裁剪

diff = y_ * tf.log(tf.clip_by_value(y, 1e-8, 1.0))from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport sysimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datafrom tensorflow.python import debug as tf_debugIMAGE_SIZE = 28HIDDEN_SIZE = 500NUM_LABELS = 10RAND_SEED = 42def main(_):  # Import data  mnist = input_data.read_data_sets(FLAGS.data_dir,                                    one_hot=True,                                    fake_data=FLAGS.fake_data)  def feed_dict(train):    if train or FLAGS.fake_data:      xs, ys = mnist.train.next_batch(FLAGS.train_batch_size,                                      fake_data=FLAGS.fake_data)    else:      xs, ys = mnist.test.images, mnist.test.labels    return {x: xs, y_: ys}  sess = tf.InteractiveSession()  # Create the MNIST neural network graph.  # Input placeholders.  with tf.name_scope("input"):    x = tf.placeholder(        tf.float32, [None, IMAGE_SIZE * IMAGE_SIZE], name="x-input")    y_ = tf.placeholder(tf.float32, [None, NUM_LABELS], name="y-input")  def weight_variable(shape):    """Create a weight variable with appropriate initialization."""    initial = tf.truncated_normal(shape, stddev=0.1, seed=RAND_SEED)    return tf.Variable(initial)  def bias_variable(shape):    """Create a bias variable with appropriate initialization."""    initial = tf.constant(0.1, shape=shape)    return tf.Variable(initial)  def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):    """Reusable code for making a simple neural net layer."""    # Adding a name scope ensures logical grouping of the layers in the graph.    with tf.name_scope(layer_name):      # This Variable will hold the state of the weights for the layer      with tf.name_scope("weights"):        weights = weight_variable([input_dim, output_dim])      with tf.name_scope("biases"):        biases = bias_variable([output_dim])      with tf.name_scope("Wx_plus_b"):        preactivate = tf.matmul(input_tensor, weights) + biases      activations = act(preactivate)      return activations  hidden = nn_layer(x, IMAGE_SIZE**2, HIDDEN_SIZE, "hidden")  logits = nn_layer(hidden, HIDDEN_SIZE, NUM_LABELS, "output", tf.identity)  y = tf.nn.softmax(logits)  with tf.name_scope("cross_entropy"):    # The following line is the culprit of the bad numerical values that appear    # during training of this graph. Log of zero gives inf, which is first seen    # in the intermediate tensor "cross_entropy/Log:0" during the 4th run()    # call. A multiplication of the inf values with zeros leads to nans,    # which is first in "cross_entropy/mul:0".    #    # You can use the built-in, numerically-stable implementation to fix this    # issue:    #   diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=logits)    diff = -(y_ * tf.log(y))    with tf.name_scope("total"):      cross_entropy = tf.reduce_mean(diff)  with tf.name_scope("train"):    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(        cross_entropy)  with tf.name_scope("accuracy"):    with tf.name_scope("correct_prediction"):      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    with tf.name_scope("accuracy"):      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  sess.run(tf.global_variables_initializer())  if FLAGS.debug:    sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type=FLAGS.ui_type)  # Add this point, sess is a debug wrapper around the actual Session if  # FLAGS.debug is true. In that case, calling run() will launch the CLI.  for i in range(FLAGS.max_steps):    acc = sess.run(accuracy, feed_dict=feed_dict(False))    print("Accuracy at step %d: %s" % (i, acc))    sess.run(train_step, feed_dict=feed_dict(True))if __name__ == "__main__":  parser = argparse.ArgumentParser()  parser.register("type", "bool", lambda v: v.lower() == "true")  parser.add_argument(      "--max_steps",      type=int,      default=10,      help="Number of steps to run trainer.")  parser.add_argument(      "--train_batch_size",      type=int,      default=100,      help="Batch size used during training.")  parser.add_argument(      "--learning_rate",      type=float,      default=0.025,      help="Initial learning rate.")  parser.add_argument(      "--data_dir",      type=str,      default="/tmp/mnist_data",      help="Directory for storing data")  parser.add_argument(      "--ui_type",      type=str,      default="curses",      help="Command-line user interface type (curses | readline)")  parser.add_argument(      "--fake_data",      type="bool",      nargs="?",      const=True,      default=False,      help="Use fake MNIST data for unit testing")  parser.add_argument(      "--debug",      type="bool",      nargs="?",      const=True,      default=False,      help="Use debugger to track down bad values during training")  FLAGS, unparsed = parser.parse_known_args()  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

远程调试。tfdbg offline_analyzer。设置本地、远程机器能访问共享目录。debug_utils.watch_graph函数设置运行时参数选项。运行session.run()，中间张量、运行时图像转储到共享目录。本地终端用tfdbg offline_analyzer加载、检查共享目录数据。

python -m tensorflow.python.debug.cli.offline_analyzer --dump_dir=/home/somebody/tfdbg_dumps_1

源码

from tensorflow.python.debug.lib import debug_utils# 构建图，生成session对象，省略run_options = tf.RunOptions()      debug_utils.watch_graph(          run_options,          sess.graph,         # 共享目录位置          # 多个客户端执行run，应用多个不同共享目录          debug_urls=["file://home/somebody/tfdbg_dumps_1"])session.run(fetches, feed_dict=feeds, options=run_options)

或用会话包装器函数DumpingDebugWrapperSession在共享目录产生训练累积文件。

from tensorflow.python.debug.lib import debug_utilssess = tf_debug.DumpingDebugWrapperSession(sess, "/home/somebody/tfdbg_dumps_1", watch_fn=my_watch_fn)

参考资料：
《TensorFlow技术解析与实战》

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