TensorFlow手写数字识别mnist example源码分析
TensorFlow 默认安装在 /usr/lib/Python/site-packages/tensorflow/
实例文件位于tensorflow/models/image/mnist/convolutional.py，为TensorFlow自带的example文件。
# Copyright 2015 The TensorFlow Authors. 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 argparse
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
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' # 数据源
WORK_DIRECTORY = 'data' # 工作目录，存放下载的数据
# MNIST 数据集特征：
# 图像尺寸 28x28
IMAGE_SIZE = 28
NUM_CHANNELS = 1 # 黑白图像
PIXEL_DEPTH = 255 # 像素值0~255
NUM_LABELS = 10 # 标签分10个类别
VALIDATION_SIZE = 5000 # 验证集大小
SEED = 66478 # 随机数种子，可设为 None 表示真的随机
BATCH_SIZE = 64 # 批处理大小为64
NUM_EPOCHS = 10 # 数据全集一共过10遍网络
EVAL_BATCH_SIZE = 64 # 验证集批处理大小也是64
# 验证时间间隔，每训练100个批处理，做一次评估
EVAL_FREQUENCY = 100 # Number of steps between evaluations.
FLAGS = None
def data_type():
"""Return the type of the activations, weights, and placeholder variables."""
if FLAGS.use_fp16:
return tf.float16
else:
return tf.float32
def maybe_download(filename):
"""如果下载过了数据，就不再重复下载"""
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
def extract_data(filename, num_images):
# 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]
print('Extracting', filename)
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
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, NUM_CHANNELS)
return data
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
def fake_data(num_images):
"""Generate a fake dataset that matches the dimensions of MNIST."""
"""生成一个匹配MNIST数据集大小的伪造数据集"""
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
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(_):
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 # 数据全集一共过10遍网络
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(
data_type(),
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(
data_type(),
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, dtype=data_type()))
# conv1 偏置
conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
# 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, dtype=data_type()))
# conv2 偏置
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
# 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,
dtype=data_type()))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
# 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,
dtype=data_type()))
fc2_biases = tf.Variable(tf.constant(
0.1, shape=[NUM_LABELS], dtype=data_type()))
# 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):
# 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，步长为1
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
# 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, dtype=data_type())
# Decay once per epoch, using an exponential schedule starting at 0.01.
# 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, # 衰减率
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):
"""通过运行在小批量数据，得到所有预测结果."""
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:
# 初始化操作准备参数
tf.global_variables_initializer().run()
print('Initialized!')
# 循环训练
for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
# 计算当前minibatch的offset
# 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}
# 运行优化器更新权重
sess.run(optimizer, feed_dict=feed_dict)
# print some extra information once reach the evaluation frequency
if step % EVAL_FREQUENCY == 0:
# fetch some extra nodes' data
l, lr, predictions = sess.run([loss, learning_rate, train_prediction],
feed_dict=feed_dict)
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,)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--use_fp16',
default=False,
help='Use half floats instead of full floats if True.',
action='store_true')
parser.add_argument(
'--self_test',
default=False,
action='store_true',
help='True if running a self test.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)