TensorFlow 从入门到精通（二）：MNIST 例程源码分析

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()