tensorflow入门实践例子—MNIST手写数字识别

MNIST是手写数字图像数据集，是一个用于图像识别基础数据集。
这里用tensorflow实现MNIST手写数据集，用两种方法：一种是普通的BP神经网络；另一种是卷积神经网络。
这里的Python版本是2.7，tensorflow版本是1.0。

1、BP神经网络

读取MNIST数据集文件mnist_input.py

"""Functions for downloading and reading MNIST data."""
from __future__ import print_function
import gzip
import os
import urllib

import numpy

SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'


def maybe_download(filename, work_directory):
"""Download the data from Yann's website, unless it's already here."""
if not os.path.exists(work_directory):
    os.mkdir(work_directory)
  filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
    filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath)
    statinfo = os.stat(filepath)
    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
return filepath


def _read32(bytestream):
  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)


def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
  print('Extracting', filename)
with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
          (magic, filename))
    num_images = _read32(bytestream)
    rows = _read32(bytestream)
    cols = _read32(bytestream)
    buf = bytestream.read(rows * cols * num_images)
    data = numpy.frombuffer(buf, dtype=numpy.uint8)
    data = data.reshape(num_images, rows, cols, 1)
return data


def dense_to_one_hot(labels_dense, num_classes=10):
"""Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot


def extract_labels(filename, one_hot=False):
"""Extract the labels into a 1D uint8 numpy array [index]."""
  print('Extracting', filename)
with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
if magic != 2049:
raise ValueError(
'Invalid magic number %d in MNIST label file: %s' %
          (magic, filename))
    num_items = _read32(bytestream)
    buf = bytestream.read(num_items)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels)
return labels


class DataSet(object):

def __init__(self, images, labels, fake_data=False):
if fake_data:
      self._num_examples = 10000
else:
assert images.shape[0] == labels.shape[0], (
"images.shape: %s labels.shape: %s" % (images.shape,
                                                 labels.shape))
      self._num_examples = images.shape[0]

# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
assert images.shape[3] == 1
      images = images.reshape(images.shape[0],
                              images.shape[1] * images.shape[2])
# Convert from [0, 255] -> [0.0, 1.0].
      images = images.astype(numpy.float32)
      images = numpy.multiply(images, 1.0 / 255.0)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0

@property
def images(self):
return self._images

@property
def labels(self):
return self._labels

@property
def num_examples(self):
return self._num_examples

@property
def epochs_completed(self):
return self._epochs_completed

def next_batch(self, batch_size, fake_data=False):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
      fake_image = [1.0 for _ in xrange(784)]
      fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)]
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
      self._epochs_completed += 1
# Shuffle the data
      perm = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
# Start next epoch
      start = 0
      self._index_in_epoch = batch_size
assert batch_size <= self._num_examples
    end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]


def read_data_sets(train_dir, fake_data=False, one_hot=False):
class DataSets(object):
pass
  data_sets = DataSets()

if fake_data:
    data_sets.train = DataSet([], [], fake_data=True)
    data_sets.validation = DataSet([], [], fake_data=True)
    data_sets.test = DataSet([], [], fake_data=True)
return data_sets

  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
  VALIDATION_SIZE = 5000

  local_file = maybe_download(TRAIN_IMAGES, train_dir)
  train_images = extract_images(local_file)

  local_file = maybe_download(TRAIN_LABELS, train_dir)
  train_labels = extract_labels(local_file, one_hot=one_hot)

  local_file = maybe_download(TEST_IMAGES, train_dir)
  test_images = extract_images(local_file)

  local_file = maybe_download(TEST_LABELS, train_dir)
  test_labels = extract_labels(local_file, one_hot=one_hot)

  validation_images = train_images[:VALIDATION_SIZE]
  validation_labels = train_labels[:VALIDATION_SIZE]
  train_images = train_images[VALIDATION_SIZE:]
  train_labels = train_labels[VALIDATION_SIZE:]

  data_sets.train = DataSet(train_images, train_labels)
  data_sets.validation = DataSet(validation_images, validation_labels)
  data_sets.test = DataSet(test_images, test_labels)

return data_sets

BP神经网络实现

# simple MNIST
import tensorflow as tf
import input_data
mnist = input_data.read_data_sets('MNIST', one_hot=True)

# input data
x = tf.placeholder(tf.float32, [None, 784])
# W:weights b:biases
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10])) 
# predictions
y = tf.nn.softmax(tf.matmul(x, W)+b)
# label
y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# init = tf.initialize_all_variables()
init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)

for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x:batch_xs, y_:batch_ys})

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

print (sess.run(accuracy, feed_dict = {x:mnist.test.images, y_:mnist.test.labels}))

执行结果：

2、卷积神经网络

卷积神经网络实现

# load tensorflow model
import tensorflow as tf
# load get data file
import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
sess = tf.InteractiveSession()
# x: input data y_: label
x = tf.placeholder("float", shape = [None, 784])
y_ = tf.placeholder("float", shape = [None, 10])

# convolutional layer
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev = 0.1)
return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape = shape)
return tf.Variable(initial)

def conv2d(x, W):
return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME')

def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')

# conv1
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1, 28, 28, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# conv2
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# flat
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = weight_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# oupout layer
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv =  tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# train and evaluate
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess.run(tf.initialize_all_variables())

for i in range(20000):
  batch = mnist.train.next_batch(50)
if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.0})
print ("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict = {x:batch[0], y_:batch[1], keep_prob:0.5})
print ("test accuracy %g"%accuracy.eval(feed_dict = {x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0}))

执行结果：