MNIST 例程源码分析 TensorFlow 从入门到精通

按照上节步骤， TensorFlow 默认安装在 /usr/lib/python/site-packages/tensorflow/ （也有可能是 /usr/local/lib……）下，查看目录结构：

 # tree -d -L 3 /usr/lib/python2.7/site-packages/tensorflow/
 /usr/lib/python2.7/site-packages/tensorflow/
 ├── contrib
 │   ├── bayesflow
 │   │   └── python
 │   ├── cmake
 │   ├── copy_graph
 │   │   └── python
 │   ├── crf
 │   │   └── python
 │   ├── cudnn_rnn
 │   │   ├── ops
 │   │   └── python
 │   ├── distributions
 │   │   └── python
 │   ├── factorization
 │   │   └── python
 │   ├── ffmpeg
 │   │   └── ops
 │   ├── framework
 │   │   └── python
 │   ├── graph_editor
 │   ├── grid_rnn
 │   │   └── python
 │   ├── layers
 │   │   ├── ops
 │   │   └── python
 │   ├── learn
 │   │   └── python
 │   ├── linear_optimizer
 │   │   ├── ops
 │   │   └── python
 │   ├── lookup
 │   ├── losses
 │   │   └── python
 │   ├── metrics
 │   │   ├── ops
 │   │   └── python
 │   ├── opt
 │   │   └── python
 │   ├── quantization
 │   │   ├── kernels
 │   │   ├── ops
 │   │   └── python
 │   ├── rnn
 │   │   └── python
 │   ├── session_bundle
 │   ├── slim
 │   │   └── python
 │   ├── tensorboard
 │   │   └── plugins
 │   ├── tensor_forest
 │   │   ├── client
 │   │   ├── data
 │   │   ├── hybrid
 │   │   └── python
 │   ├── testing
 │   │   └── python
 │   ├── training
 │   │   └── python
 │   └── util
 ├── core
 │   ├── example
 │   ├── framework
 │   ├── lib
 │   │   └── core
 │   ├── protobuf
 │   └── util
 ├── examples
 │   └── tutorials
 │       └── mnist
 ├── include
 │   ├── Eigen
 │   │   └── src
 │   ├── external
 │   │   └── eigen_archive
 │   ├── google
 │   │   └── protobuf
 │   ├── tensorflow
 │   │   └── core
 │   ├── third_party
 │   │   └── eigen3
 │   └── unsupported
 │       └── Eigen
 ├── models
 │   ├── embedding
 │   ├── image
 │   │   ├── alexnet
 │   │   ├── cifar10
 │   │   ├── imagenet
 │   │   └── mnist
 │   └── rnn
 │       ├── ptb
 │       └── translate
 ├── python
 │   ├── client
 │   ├── debug
 │   │   └── cli
 │   ├── framework
 │   ├── lib
 │   │   ├── core
 │   │   └── io
 │   ├── ops
 │   ├── platform
 │   ├── saved_model
 │   ├── summary
 │   │   └── impl
 │   ├── training
 │   ├── user_ops
 │   └── util
 │       └── protobuf
 ├── tensorboard
 │   ├── backend
 │   ├── dist
 │   ├── lib
 │   │   ├── css
 │   │   └── python
 │   └── plugins
 │       └── projector
 └── tools
     └── pip_package

 119 directories

上节运行 MNIST 例程的命令为：

# python -m tensorflow.models.image.mnist.convolutional
对应文件为 /usr/lib/python2.7/site-packages/tensorflow/models/image/mnist/convolutional.py

打开例程源码：

 # Copyright 2015 Google Inc. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================

 """Simple, end-to-end, LeNet-5-like convolutional MNIST model example.

 This should achieve a test error of 0.7%. Please keep this model as simple and
 linear as possible, it is meant as a tutorial for simple convolutional models.
 Run with --self_test on the command line to execute a short self-test.
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import gzip
 import os
 import sys
 import time

 import numpy
 from six.moves import urllib
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf

 # 数据源
 SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
 # 工作目录，存放下载的数据
 WORK_DIRECTORY = 'data'
 # MNIST 数据集特征：
 #     图像尺寸 28x28
 IMAGE_SIZE = 28
 #     黑白图像
 NUM_CHANNELS = 1
 #     像素值0~255
 PIXEL_DEPTH = 255
 #     标签分10个类别
 NUM_LABELS = 10
 #     验证集共 5000 个样本
 VALIDATION_SIZE = 5000
 # 随机数种子，可设为 None 表示真的随机
 SEED = 66478
 # 批处理大小为64
 BATCH_SIZE = 64
 # 数据全集一共过10遍网络
 NUM_EPOCHS = 10
 # 验证集批处理大小也是64
 EVAL_BATCH_SIZE = 64
 # 验证时间间隔，每训练100个批处理，做一次评估
 EVAL_FREQUENCY = 100  

 tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")
 FLAGS = tf.app.flags.FLAGS

 # 如果下载过了数据，就不再重复下载
 def maybe_download(filename):
   """Download the data from Yann's website, unless it's already here."""
   if not tf.gfile.Exists(WORK_DIRECTORY):
     tf.gfile.MakeDirs(WORK_DIRECTORY)
   filepath = os.path.join(WORK_DIRECTORY, filename)
   if not tf.gfile.Exists(filepath):
     filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
     with tf.gfile.GFile(filepath) as f:
       size = f.Size()
     print('Successfully downloaded', filename, size, 'bytes.')
   return filepath

 # 抽取数据，变为 4维张量[图像索引，y, x, c]
 # 去均值、做归一化，范围变到[-0.5, 0.5]
 def extract_data(filename, num_images):
   """Extract the images into a 4D tensor [image index, y, x, channels].

   Values are rescaled from [0, 255] down to [-0.5, 0.5].
   """
   print('Extracting', filename)
   with gzip.open(filename) as bytestream:
     bytestream.read(16)
     buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)
     data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
     data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH
     data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)
 return data

 # 抽取图像标签
 def extract_labels(filename, num_images):
   """Extract the labels into a vector of int64 label IDs."""
   print('Extracting', filename)
   with gzip.open(filename) as bytestream:
     bytestream.read(8)
     buf = bytestream.read(1 * num_images)
     labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)
   return labels

 # 假数据，用于功能自测
 def fake_data(num_images):
   """Generate a fake dataset that matches the dimensions of MNIST."""
   data = numpy.ndarray(
       shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
       dtype=numpy.float32)
   labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)
   for image in xrange(num_images):
     label = image % 2
     data[image, :, :, 0] = label - 0.5
     labels[image] = label
   return data, labels
 # 计算分类错误率
 def error_rate(predictions, labels):
   """Return the error rate based on dense predictions and sparse labels."""
   return 100.0 - (
       100.0 *
       numpy.sum(numpy.argmax(predictions, 1) == labels) /
 predictions.shape[0])

 # 主函数
 def main(argv=None):  # pylint: disable=unused-argument
   if FLAGS.self_test:
     print('Running self-test.')
     train_data, train_labels = fake_data(256)
     validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)
     test_data, test_labels = fake_data(EVAL_BATCH_SIZE)
     num_epochs = 1
   else:
     # 下载数据
     train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
     train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
     test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
     test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')

     # 载入数据到numpy
     train_data = extract_data(train_data_filename, 60000)
     train_labels = extract_labels(train_labels_filename, 60000)
     test_data = extract_data(test_data_filename, 10000)
     test_labels = extract_labels(test_labels_filename, 10000)

     # 产生评测集
     validation_data = train_data[:VALIDATION_SIZE, ...]
     validation_labels = train_labels[:VALIDATION_SIZE]
     train_data = train_data[VALIDATION_SIZE:, ...]
     train_labels = train_labels[VALIDATION_SIZE:]
     num_epochs = NUM_EPOCHS
   train_size = train_labels.shape[0]

 # 训练样本和标签将从这里送入网络。
 # 每训练迭代步，占位符节点将被送入一个批处理数据
 # 训练数据节点
   train_data_node = tf.placeholder(
       tf.float32,
 shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
 # 训练标签节点
   train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
 # 评测数据节点
   eval_data = tf.placeholder(
       tf.float32,
       shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

 # 下面这些变量是网络的可训练权值
 # conv1 权值维度为 32 x channels x 5 x 5, 32 为特征图数目
   conv1_weights = tf.Variable(
       tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
                           stddev=0.1,
                           seed=SEED))
 # conv1 偏置
   conv1_biases = tf.Variable(tf.zeros([32]))
 # conv2 权值维度为 64 x 32 x 5 x 5
   conv2_weights = tf.Variable(
       tf.truncated_normal([5, 5, 32, 64],
                           stddev=0.1,
                           seed=SEED))
   conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))
 # 全连接层 fc1 权值，神经元数目为512
   fc1_weights = tf.Variable(  # fully connected, depth 512.
       tf.truncated_normal(
           [IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
           stddev=0.1,
           seed=SEED))
   fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))
 # fc2 权值，维度与标签类数目一致
   fc2_weights = tf.Variable(
       tf.truncated_normal([512, NUM_LABELS],
                           stddev=0.1,
                           seed=SEED))
   fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))

 # 两个网络：训练网络和评测网络
 # 它们共享权值

 # 实现 LeNet-5 模型，该函数输入为数据，输出为fc2的响应
 # 第二个参数区分训练网络还是评测网络
   def model(data, train=False):
 """The Model definition."""
 # 二维卷积，使用“不变形”补零（即输出特征图与输入尺寸一致）。
     conv = tf.nn.conv2d(data,
                         conv1_weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')
     # 加偏置、过激活函数一块完成
 relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
     # 最大值下采样
     pool = tf.nn.max_pool(relu,
                           ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1],
                           padding='SAME')
     # 第二个卷积层
     conv = tf.nn.conv2d(pool,
                         conv2_weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')
     relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
     pool = tf.nn.max_pool(relu,
                           ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1],
                           padding='SAME')
 # 特征图变形为2维矩阵，便于送入全连接层
     pool_shape = pool.get_shape().as_list()
     reshape = tf.reshape(
         pool,
         [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
 # 全连接层，注意“+”运算自动广播偏置
     hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
 # 训练阶段，增加 50% dropout；而评测阶段无需该操作
     if train:
       hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
     return tf.matmul(hidden, fc2_weights) + fc2_biases

   # Training computation: logits + cross-entropy loss.
   # 训练阶段计算： 对数+交叉熵 损失函数
   logits = model(train_data_node, True)
   loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
       logits, train_labels_node))

   # 全连接层参数进行 L2 正则化
   regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
                   tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
   # 将正则项加入损失函数
   loss += 5e-4 * regularizers

   # 优化器： 设置一个变量，每个批处理递增，控制学习速率衰减
   batch = tf.Variable(0)
   # 指数衰减
   learning_rate = tf.train.exponential_decay(
       0.01,                # 基本学习速率
       batch * BATCH_SIZE,  # 当前批处理在数据全集中的位置
       train_size,          # Decay step.
       0.95,                # Decay rate.
       staircase=True)
   # Use simple momentum for the optimization.
   optimizer = tf.train.MomentumOptimizer(learning_rate,
                                          0.9).minimize(loss,
                                                        global_step=batch)

   # 用softmax 计算训练批处理的预测概率
   train_prediction = tf.nn.softmax(logits)

   # 用 softmax 计算评测批处理的预测概率
   eval_prediction = tf.nn.softmax(model(eval_data))

   # Small utility function to evaluate a dataset by feeding batches of data to
   # {eval_data} and pulling the results from {eval_predictions}.
   # Saves memory and enables this to run on smaller GPUs.
   def eval_in_batches(data, sess):
     """Get all predictions for a dataset by running it in small batches."""
     size = data.shape[0]
     if size < EVAL_BATCH_SIZE:
       raise ValueError("batch size for evals larger than dataset: %d" % size)
     predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)
     for begin in xrange(0, size, EVAL_BATCH_SIZE):
       end = begin + EVAL_BATCH_SIZE
       if end <= size:
         predictions[begin:end, :] = sess.run(
             eval_prediction,
             feed_dict={eval_data: data[begin:end, ...]})
       else:
         batch_predictions = sess.run(
             eval_prediction,
             feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
         predictions[begin:, :] = batch_predictions[begin - size:, :]
     return predictions

   # Create a local session to run the training.
   start_time = time.time()
   with tf.Session() as sess:
     # Run all the initializers to prepare the trainable parameters.
     tf.initialize_all_variables().run()
     print('Initialized!')
     # Loop through training steps.
     for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
       # Compute the offset of the current minibatch in the data.
       # Note that we could use better randomization across epochs.
       offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
       batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
       batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
       # This dictionary maps the batch data (as a numpy array) to the
       # node in the graph it should be fed to.
       feed_dict = {train_data_node: batch_data,
                    train_labels_node: batch_labels}
       # Run the graph and fetch some of the nodes.
       _, l, lr, predictions = sess.run(
           [optimizer, loss, learning_rate, train_prediction],
           feed_dict=feed_dict)
       if step % EVAL_FREQUENCY == 0:
         elapsed_time = time.time() - start_time
         start_time = time.time()
         print('Step %d (epoch %.2f), %.1f ms' %
               (step, float(step) * BATCH_SIZE / train_size,
                1000 * elapsed_time / EVAL_FREQUENCY))
         print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
         print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
         print('Validation error: %.1f%%' % error_rate(
             eval_in_batches(validation_data, sess), validation_labels))
         sys.stdout.flush()
     # Finally print the result!
     test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
     print('Test error: %.1f%%' % test_error)
     if FLAGS.self_test:
       print('test_error', test_error)
       assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
           test_error,)
 # 程序入口点
 if __name__ == '__main__':
   tf.app.run()

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