基础知识:
argparse: 用于在命令行运行python时,输入参数
例如proxy.py:
import argparse parser = argparse.ArgumentParser() parser.add_argument("square", help="display a square of a given number", type=int) args = parser.parse_args() print args.square**2
命令行下:
$ python prog.py 4 16 $ python prog.py four usage: prog.py [-h] square prog.py: error: argument square: invalid int value: 'four'
解释:
parser.add_argument("square", help="display a square of a given number", type=int)
# 插入参数square,并且要求该参数为int类型
# $ python prog.py 4
# 16
os和sys: os模块负责程序与操作系统的交互，提供了访问操作系统底层的接口;sys模块负责程序与python解释器的交互，提供了一系列的函数和变量，用于操控python的运行时环境。
<os 常用方法> os.remove(‘path/filename’) 删除文件 os.rename(oldname, newname) 重命名文件 os.walk() 生成目录树下的所有文件名 os.chdir('dirname') 改变目录 os.mkdir/makedirs('dirname')创建目录/多层目录 os.rmdir/removedirs('dirname') 删除目录/多层目录 os.listdir('dirname') 列出指定目录的文件 os.getcwd() 取得当前工作目录 os.chmod() 改变目录权限 os.path.basename(‘path/filename’) 去掉目录路径，返回文件名 os.path.dirname(‘path/filename’) 去掉文件名，返回目录路径 os.path.join(path1[,path2[,...]]) 将分离的各部分组合成一个路径名 os.path.split('path') 返回( dirname(), basename())元组 os.path.splitext() 返回 (filename, extension) 元组 os.path.getatimectimemtime 分别返回最近访问、创建、修改时间 os.path.getsize() 返回文件大小 os.path.exists() 是否存在 os.path.isabs() 是否为绝对路径 os.path.isdir() 是否为目录 os.path.isfile() 是否为文件
<sys 常用方法> sys.argv 命令行参数List，第一个元素是程序本身路径 sys.modules.keys() 返回所有已经导入的模块列表 sys.exc_info() 获取当前正在处理的异常类,exc_type、exc_value、exc_traceback当前处理的异常详细信息 sys.exit(n) 退出程序，正常退出时exit(0) sys.hexversion 获取Python解释程序的版本值，16进制格式如：0x020403F0 sys.version 获取Python解释程序的版本信息 sys.maxint 最大的Int值 sys.maxunicode 最大的Unicode值 sys.modules 返回系统导入的模块字段，key是模块名，value是模块 sys.path 返回模块的搜索路径，初始化时使用PYTHONPATH环境变量的值 sys.platform 返回操作系统平台名称 sys.stdout 标准输出 sys.stdin 标准输入 sys.stderr 错误输出 sys.exc_clear() 用来清除当前线程所出现的当前的或最近的错误信息 sys.exec_prefix 返回平台独立的python文件安装的位置 sys.byteorder 本地字节规则的指示器，big-endian平台的值是'big',little-endian平台的值是'little' sys.copyright 记录python版权相关的东西 sys.api_version 解释器的C的API版本 sys.stdin,sys.stdout,sys.stderr
fully_connected_feed源码分析
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.examples.tutorials.mnist import mnist from six.moves import xrange # pylint: disable=missing-docstring import argparse import os import sys import time # Basic model parameters as external flags. FLAGS = None def placeholder_inputs(batch_size): """ 定义两个placeholder,同时自定义placeholder的shape Args: batch_size: 分别定义images_placeholder和labels_placeholder的shape Returns: images_placeholder: Images placeholder. labels_placeholder: Labels placeholder. """ # Note that the shapes of the placeholders match the shapes of the full # image and label tensors, except the first dimension is now batch_size # rather than the full size of the train or test data sets. images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, mnist.IMAGE_PIXELS)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) return images_placeholder, labels_placeholder def fill_feed_dict(data_set, images_pl, labels_pl): """ 将data_set放入images_pl和labels_pl的,然后再放入feed_dict中用于动态训练 Args: data_set: 来自于input_data.read_data_sets()的数据集 images_pl: 来自于laceholder_inputs()的images placeholder labels_pl: 来自于laceholder_inputs()的labels placeholder Returns: feed_dict: 向feed_dict中传入值images_feed和labels_feed """ # Create the feed_dict for the placeholders filled with the next # `batch size` examples. images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size, FLAGS.fake_data) feed_dict = { images_pl: images_feed, labels_pl: labels_feed, } return feed_dict def do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_set): """Runs one evaluation against the full epoch of data. Args: sess: The session in which the model has been trained. eval_correct: The Tensor that returns the number of correct predictions. images_placeholder: The images placeholder. labels_placeholder: The labels placeholder. data_set: The set of images and labels to evaluate, from input_data.read_data_sets(). """ # And run one epoch of eval. true_count = 0 # Counts the number of correct predictions. steps_per_epoch = data_set.num_examples // FLAGS.batch_size num_examples = steps_per_epoch * FLAGS.batch_size for step in xrange(steps_per_epoch): feed_dict = fill_feed_dict(data_set, images_placeholder, labels_placeholder) true_count += sess.run(eval_correct, feed_dict=feed_dict) precision = float(true_count) / num_examples print('Num examples: %d Num correct: %d Precision @ 1: %0.04f' % (num_examples, true_count, precision)) def run_training(): """Train MNIST for a number of steps.""" # Get the sets of images and labels for training, validation, and # test on MNIST. data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data) # Tell TensorFlow that the model will be built into the default Graph. with tf.Graph().as_default(): # Generate placeholders for the images and labels. images_placeholder, labels_placeholder = placeholder_inputs( FLAGS.batch_size) # Build a Graph that computes predictions from the inference model. logits = mnist.inference(images_placeholder, FLAGS.hidden1, FLAGS.hidden2) # Add to the Graph the Ops for loss calculation. loss = mnist.loss(logits, labels_placeholder) # Add to the Graph the Ops that calculate and apply gradients. train_op = mnist.training(loss, FLAGS.learning_rate) # Add the Op to compare the logits to the labels during evaluation. eval_correct = mnist.evaluation(logits, labels_placeholder) # Build the summary Tensor based on the TF collection of Summaries. summary = tf.summary.merge_all() # Add the variable initializer Op. init = tf.global_variables_initializer() # Create a saver for writing training checkpoints. saver = tf.train.Saver() # Create a session for running Ops on the Graph. sess = tf.Session() # Instantiate a SummaryWriter to output summaries and the Graph. summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph) # And then after everything is built: # Run the Op to initialize the variables. sess.run(init) # Start the training loop. for step in xrange(FLAGS.max_steps): start_time = time.time() # Fill a feed dictionary with the actual set of images and labels # for this particular training step. feed_dict = fill_feed_dict(data_sets.train, images_placeholder, labels_placeholder) # Run one step of the model. The return values are the activations # from the `train_op` (which is discarded) and the `loss` Op. To # inspect the values of your Ops or variables, you may include them # in the list passed to sess.run() and the value tensors will be # returned in the tuple from the call. _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict) duration = time.time() - start_time # Write the summaries and print an overview fairly often. if step % 100 == 0: # Print status to stdout. print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration)) # Update the events file. summary_str = sess.run(summary, feed_dict=feed_dict) summary_writer.add_summary(summary_str, step) summary_writer.flush() # Save a checkpoint and evaluate the model periodically. if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps: checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt') saver.save(sess, checkpoint_file, global_step=step) # Evaluate against the training set. print('Training Data Eval:') do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.train) # Evaluate against the validation set. print('Validation Data Eval:') do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.validation) # Evaluate against the test set. print('Test Data Eval:') do_eval(sess, eval_correct, images_placeholder, labels_placeholder, data_sets.test) def main(_): if tf.gfile.Exists(FLAGS.log_dir): tf.gfile.DeleteRecursively(FLAGS.log_dir) tf.gfile.MakeDirs(FLAGS.log_dir) run_training() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--learning_rate', type=float, default=0.01, help='Initial learning rate.' ) parser.add_argument( '--max_steps', type=int, default=2000, help='Number of steps to run trainer.' ) parser.add_argument( '--hidden1', type=int, default=128, help='Number of units in hidden layer 1.' ) parser.add_argument( '--hidden2', type=int, default=32, help='Number of units in hidden layer 2.' ) parser.add_argument( '--batch_size', type=int, default=100, help='Batch size. Must divide evenly into the dataset sizes.' ) parser.add_argument( '--input_data_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'), 'tensorflow/mnist/input_data'), help='Directory to put the input data.' ) parser.add_argument( '--log_dir', type=str, default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'), 'tensorflow/mnist/logs/fully_connected_feed'), help='Directory to put the log data.' ) parser.add_argument( '--fake_data', default=False, help='If true, uses fake data for unit testing.', action='store_true' ) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)