TensorFlow学习：MNIST

本部分学习自：极客学院

使用MNIST手写数字入门TensorFLow使用，按照教程码写代码并得到结果。

读取数据（此部分教程中预先给出）：

# 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_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport tensorflow.python.platformimport numpyfrom six.moves import urllibfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfSOURCE_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 filepathdef _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 datadef 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_hotdef 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 labelsclass 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

使用Softmax Regression模型：

#coding: utf-8import tensorflow as tfimport input_data   #读入数据集的py文件mnist = input_data.read_data_sets("Mnist_data/",one_hot=True)#实现回归模型y=softmax(Wx+b)x = tf.placeholder(tf.float32,[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 成本函数：交叉熵 H(y)=-sum(yi_ * log(yi)) y:预测分布 y_:实际分布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})

结果：

使用卷积神经网络：

#coding: utf-8import tensorflow as tfimport input_datamnist = input_data.read_data_sets('Mnist_data', one_hot = True)sess = tf.InteractiveSession()#占位符x = tf.placeholder("float", shape=[None, 784]) #shape可以自动捕捉因数据维度不一致导致的错误y_ = tf.placeholder("float", shape=[None, 10])#变量，机器学习中的模型参数一遍都用变量定义W = tf.Variable(tf.zeros([784, 10]))  #784个特征和10个输出b = tf.Variable(tf.zeros([10])) #10个分类#类别预测与损失函数y = tf.nn.softmax(tf.matmul(x,W) + b)#构建一个多层卷积网络#定义初始化的权重函数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)#定义卷积和池化函数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')#第一层卷积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)#第二层卷积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)#密集连接层W_fc1 = weight_variable([7 *7 *64, 1024])b_fc1 = bias_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)#dropoutkeep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)#输出层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)#训练和评估模型cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediciton = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediciton, "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})

结果（每迭代100次输出一次结果，这里只截取最后的部分）：