NO.3：自学tensorflow之路------MNIST识别，神经网络拓展
引言
最近自学GRU神经网络，感觉真的不简单。为了能够快速跑完程序，给我的渣渣笔记本(GT650M)也安装了一个GPU版的tensorflow。顺便也更新了版本到了tensorflow-gpu 1.7。之前相关的程序代码依然兼容，可以运行。刚好遇到五一假期，一个人在实验室发霉，就顺便随手做了一下MNIST手写体数字的BP神经网络识别程序。做的比较简单，日后可能会扩充这一篇随笔，所以大概算是个草稿版。
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MNIST数据准备
MNIST手写体数字识别，在人工智能中的地位有点像’hello world‘在编程中的地位，算是一个入门程序。从这个程序中其实可以扩展出很多tensorflow的使用方法。然而由于最近犯春困，就简单写一下。准备数据可以使用Google已经提供好的input_data.py文件。这里也一并提供一下源代码。
# 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
下载保存后，将input_data.py文件放入工程目录中，然后新建工程文件，使用以下两行代码，就可以完成整个MNIST数据的准备。
import input_data mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
这样，就会自动下载好数据文件到工程目录下的‘/MNIST_data/’中。如果已经下载就会跳过下载，然后将train,valiation和test三个数据集保存在mnist变量之中。
神经网络的扩展
这一部分，以后慢慢填补，现在就用最简单的BP实现，BP的内容可以参考上一篇随笔。
损失函数
神经网络模型的效果以及优化的目标是通过损失函数来定义的。不同的优化目标就对应需要采用不同的损失函数。分类问题中，交叉熵是判断输出向量和期望的向量接近程度的一种指标。
摸了
优化算法
摸了
过拟合
摸了
滑动平均模型
摸了
模型保存
摸了
作业
完成手写体数字识别程序，并尽可能提高识别的准确率。
#-*- coding:utf-8 -*- #The MNIST database of handwritten digits #Author:Kai Z import tensorflow as tf import numpy as np import input_data #创建MNIST数据，存储于/MNIST_data目录下 #mnist.train mnist.test mnist = input_data.read_data_sets('MNIST_data/', one_hot=True) #神经网络超参数 input_node = 784 output_node = 10 hide_node = 100 batch_size = 100 learning_rate = 1e-3 training_steps = 5000 x = tf.placeholder(tf.float32,[None,input_node]) y = tf.placeholder(tf.float32,[None,output_node]) hidden_weight = tf.Variable(tf.random_normal([input_node,hide_node],stddev = 1,seed = 1)) hidden_bias = tf.Variable(tf.zeros([1,hide_node],tf.float32)) output_weight = tf.Variable(tf.random_normal([hide_node,output_node],stddev = 1,seed = 1)) output_bias = tf.Variable(tf.zeros([1,output_node],tf.float32)) h = tf.nn.tanh(tf.matmul(x,hidden_weight)+hidden_bias) y_pred = tf.nn.sigmoid(tf.matmul(h,output_weight)+output_bias) correct_predict = tf.equal(tf.argmax(y_pred,1),tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_predict,tf.float32)) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_pred,labels=tf.argmax(y,1)) cross_entropy_mean = tf.reduce_mean(cross_entropy) train_op = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_mean) init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) for i in range(training_steps): input_batch,output_batch = mnist.train.next_batch(batch_size) sess.run(train_op,feed_dict={x:input_batch,y:output_batch}) if i%100 == 0: right_rate = sess.run(accuracy,feed_dict = {x:mnist.validation.images,y:mnist.validation.labels}) print('训练%d次后，训练正确率为百分之%f'%(i,right_rate*100)) right_rate = sess.run(accuracy,feed_dict = {x:mnist.test.images,y:mnist.test.labels}) print('训练%d次后，测试正确率为百分之%f'%(i,right_rate*100))
最终，结果为，测试准确率达到了91%。仍然有改进的空间。
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