tensorflow- MNIST机器学习入门

实现回归模型

使用TensorFlow之前，首先导入它：

import tensorflow as tf

#x不是一个特定的值，而是一个占位符placeholder，我们在#TensorFlow运行计算时输入这个值。我们希望能够输入任意数量的#MNIST图像，每一张图展平成784维的向量。

x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,W) + b)  #模型表示
y_ = tf.placeholder("float", [None,10])

cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# 在运行计算之前，我们需要添加一个操作来初始化我们创建的变量
init = tf.initialize_all_variables()
# 现在我们可以在一个Session里面启动我们的模型，并初始化变量：
sess = tf.Session()
sess.run(init)
# 开始训练模型，1000次
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})

以上是斯坦福官网教程。今天自己跟着做了一遍，具体代码如下：

>>> import tensorflow as tf
>>> x = tf.placeholder("float",[None, 784])
>>> w = tf.Variable(tf.zeros([784,10]))
>>> b = tf.Variable(tf.zeros([10]))
>>> y = tf.nn.softmax(tf.matmul(x,w)+ b)
>>> y_= tf.placeholder("float",[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()
>>> 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})
0.9103
>>>

可以看到，效果真的达到了91%，而且速度快的超出了我的想象，几乎一下子就出来，收到了惊吓。

不过之前因为下载不到数据，一直没有实现，下载数据的代码如下：

input_data.py

# 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.

# ==============================================================================

"""Functions for downloading and reading MNIST data."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import gzip

import os

import numpy

from six.moves import urllib

from six.moves import xrange  # pylint: disable=redefined-builtin

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.request.urlretrieve(SOURCE_URL + filename, filepath)

statinfo = os.stat(filepath)

print('Successfully 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:

ra
df5f
ise 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