源码解析之tensorflow构建多层前馈神经网络

这是TensorFlow的测试用例之一，本文将逐行予以解析。

本例使用手写数字识别MNIST，MNIST是机器学习领域的一个经典问题。指的是让机器查看一系列大小为28x28像素的手写数字灰度图像，并判断这些图像代表0-9中的哪一个数字。

• 准备数据

# 获取参数
FLAGS, b = parser.parse_known_args()

# load data
data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)

其中，各种参数的配置如下：

parser = argparse.ArgumentParser() # argparse是python3.*中处理参数的标准库

# 导入具体的参数
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'
)

• 构建图

TensorFlow是通过构建图来运行操作，对应于多层前馈神经网络，需要构筑2个隐藏层，mnist.inference就是这样的函数，其hidden_layer1是 tf.nn.relu(tf.matmul(images, weights) + biases)，用激活函数relu来运算 wx+b, hidden_layer2类似

def inference(images, hidden1_units, hidden2_units):
  """Build the MNIST model up to where it may be used for inference.

  Args:
    images: Images placeholder, from inputs().
    hidden1_units: Size of the first hidden layer.
    hidden2_units: Size of the second hidden layer.

  Returns:
    softmax_linear: Output tensor with the computed logits.
  """
  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
  # Hidden 2
  with tf.name_scope('hidden2'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, hidden2_units],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden2_units]),
                         name='biases')
    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases
  return logits

 

# coding=utf-8

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data, mnist
import argparse
import os
import time


def fill_feed_dict(data_set, images_pl, labels_pl):
    """Fills the feed_dict for training the given step.

  A feed_dict takes the form of:
  feed_dict = {
      <placeholder>: <tensor of values to be passed for placeholder>,
      ....
  }

  Args:
    data_set: The set of images and labels, from input_data.read_data_sets()
    images_pl: The images placeholder, from placeholder_inputs().
    labels_pl: The labels placeholder, from placeholder_inputs().

  Returns:
    feed_dict: The feed dictionary mapping from placeholders to values.
  """
    # 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 range(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 placeholder_inputs(batch_size):
    """Generate placeholder variables to represent the input tensors.

  These placeholders are used as inputs by the rest of the model building
  code and will be fed from the downloaded data in the .run() loop, below.

  Args:
    batch_size: The batch size will be baked into both placeholders.

  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


FLAGS, b = parser.parse_known_args()
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 range(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)