tensorflow MNIST机器学习入门

男娘i 2022-05-28 02:08 160阅读 0赞

# tensorflow MNIST机器学习入门 #

tensorflow入门学习[官方网站][Link 1]

## MNIST数据集 ##

MNIST数据集的官网是Yann LeCun’s website。在这里，我们提供了一份python源代码用于自动下载和安装这个数据集。你可以下载这份代码，然后用下面的代码导入到你的项目里面，也可以直接复制粘贴到你的代码文件里面。

import input_data
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

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下载下来的数据集被分成两部分：60000行的训练数据集（mnist.train）和10000行的测试数据集（mnist.test）。

每一张图片包含28像素X28像素。我们可以用一个数字数组来表示这张图片：

我们把这个数组展开成一个向量，长度是 28x28 = 784

因此，在MNIST训练数据集中，mnist.train.images 是一个形状为 \[60000, 784\] 的张量，第一个维度数字用来索引图片，第二个维度数字用来索引每张图片中的像素点。在此张量里的每一个元素，都表示某张图片里的某个像素的强度值，值介于0和1之间。

相对应的MNIST数据集的标签是介于0到9的数字，用来描述给定图片里表示的数字.因此， mnist.train.labels 是一个 \[60000, 10\] 的数字矩阵。

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## Softmax回归 ##

softmax模型可以用来给不同的对象分配概率。

分两步

*  我们对图片像素值进行加权求和。如果这个像素具有很强的证据说明这张图片不属于该类，那么相应的权值为负数，相反如果这个像素拥有有利的证据支持这张图片属于这个类，那么权值是正数。
 *  将各像素点权值累加归一化处理，是和为1，满足概率分布。

## 训练模型 ##

使用成本函数是“交叉熵”（cross-entropy）。

在这里，我们要求TensorFlow用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵。

## 评估模型 ##

计算模型正确率。

## code ##

#!/usr/bin/env python
    # coding=utf-8
    # File Name: start.py
    # Author: Fang Pin
    # Mail: fangpin1993@hotmail.com
    # Created Time: 一 6/27 20:33:46 2016
    
    import input_data
    import tensorflow as tf
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    
    # soft回归模型
    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)
    
    
    
    # 训练模型
    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})
    
    # 评估模型
    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})

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# 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 tensorflow.python.platform
    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/'
    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)[0]
    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, one_hot=False, dtype=tf.float32):
        """Construct a DataSet. one_hot arg is used only if fake_data is true. `dtype` can be either `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into `[0, 1]`. """
        dtype = tf.as_dtype(dtype).base_dtype
        if dtype not in (tf.uint8, tf.float32):
          raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
                          dtype)
        if fake_data:
          self._num_examples = 10000
          self.one_hot = one_hot
        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])
          if dtype == tf.float32:
            # 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] * 784
          if self.one_hot:
            fake_label = [1] + [0] * 9
          else:
            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, dtype=tf.float32):
      class DataSets(object):
        pass
      data_sets = DataSets()
      if fake_data:
        def fake():
          return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
        data_sets.train = fake()
        data_sets.validation = fake()
        data_sets.test = fake()
        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, dtype=dtype)
      data_sets.validation = DataSet(validation_images, validation_labels,
                                     dtype=dtype)
      data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
      return data_sets

[Link 1]: http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html