本文参考Yann LeCun的LeNet5经典架构，稍加ps得到下面适用于本手写识别的cnn结构，构造一个两层卷积神经网络，神经网络的结构如下图所示：

　　输入-卷积-pooling-卷积-pooling-全连接层-Dropout-Softmax输出

　　

　　第一层卷积利用5*5的patch，32个卷积核，可以计算出32个特征。然后进行maxpooling。第二层卷积利用5*5的patch，64个卷积核，可以计算出64个特征。然后进行max pooling。卷积核的个数是我们自己设定，可以增加卷积核数目提高分类精度，但是那样会增加更大参数，提高计算成本。

　　这样输入是分辨率为28*28的图片。利用5*5的patch进行卷积。我们的卷积使用1步长（stride size），0填充模块（zero padded），这样得到的输出和输入是同一个大小。经过第一层卷积之后，卷积特征大小为28*28。然后通过ReLU函数激活。我们的pooling用简单传统的2x2大小的模板做max pooling，这样pooling后得到14*14大小的特征。经过第二层卷积后，卷积特征大小为14*14，然后通过ReLU函数激活，再经过pooling后得到特征大小为7*7。

　　现在，图片尺寸减小到7x7，我们加入一个有1024个神经元的全连接层，用于处理整个图片。我们把池化层输出的张量展开成一些向量，乘上权重矩阵，加上偏置，然后对其使用ReLU。

　　为了避免过拟合，在全连接层输出接上dropout层。Dropout层在训练时屏蔽一半的神经元。

1、输入数据

　　直接使用tensorflow中的模块，导入输入数据：

　　　　from tensorflow.examples.tutorials.mnist import input_data

　　　　mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

　　或者使用官方提供的input_data.py文件下载mnist数据

2、启动session

　　（1）交互方式启动session

　　　　sess = tf.InteractiveSession()

　　（2）一般方式启动session

　　　　sess = tf.Session()

　　ps: 使用交互方式不用提前构建计算图，而使用一般方式必须提前构建好计算图才能启动session

3、权重和偏置初始化

　　权重初始化的原则：应该加入少量的噪声来打破对称性并且要避免0梯度（初始化为0）

　　权重初始化一般选择均匀分布或是正态分布

　　定义权重初始化方法：

　　　def weight_variable(shape):

　　　　#截尾正态分布,stddev是正态分布的标准偏差

　　　　initial = tf.truncated_normal(shape=shape, stddev=0.1)

　　　　return tf.Variable(initial)

　　定义偏置初始化方法：

　　def bias_variable(shape):

　　　　initial = tf.constant(0.1, shape=shape)

　　　　return tf.Variable(initial)

4、定义卷积和池化方法

　　TensorFlow在卷积和Pooling上有很强的灵活性。我们怎么处理边界？步长应该设多大？在这个实例里，我们的卷积使用1步长（stride size），0填充模块（zero padded），保证输出和输入是同一个大小。我们的pooling用简单传统的2x2大小的模板做maxpooling。为了代码更简洁，我们把这部分抽象成一个函数。

　　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')

5、直接贴完整代码

 

from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf #加载数据集 mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #以交互式方式启动session #如果不使用交互式session，则在启动session前必须 # 构建整个计算图，才能启动该计算图 sess = tf.InteractiveSession() """构建计算图""" #通过占位符来为输入图像和目标输出类别创建节点 #shape参数是可选的，有了它tensorflow可以自动捕获维度不一致导致的错误 x = tf.placeholder("float", shape=[None, 784]) #原始输入 y_ = tf.placeholder("float", shape=[None, 10]) #目标值 #为了不在建立模型的时候反复做初始化操作， # 我们定义两个函数用于初始化 def weight_variable(shape): #截尾正态分布,stddev是正态分布的标准偏差 initial = tf.truncated_normal(shape=shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) #卷积核池化,步长为1,0边距 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""" #由一个卷积和一个最大池化组成。滤波器5x5中算出32个特征，是因为使用32个滤波器进行卷积 #卷积的权重张量形状是[5, 5, 1, 32],1是输入通道的个数，32是输出通道个数 W_conv1 = weight_variable([5, 5, 1, 32]) #每一个输出通道都有一个偏置量 b_conv1 = bias_variable([32]) #位了使用卷积，必须将输入转换成4维向量，2、3维表示图片的宽、高 #最后一维表示图片的颜色通道（因为是灰度图像所以通道数维1，RGB图像通道数为3） x_image = tf.reshape(x, [-1, 28, 28, 1]) #第一层的卷积结果,使用Relu作为激活函数 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)) #第一层卷积后的池化结果 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) """全连接层""" #图片尺寸减小到7*7，加入一个有1024个神经元的全连接层 W_fc1 = weight_variable([7*7*64, 1024]) b_fc1 = bias_variable([1024]) #将最后的池化层输出张量reshape成一维向量 h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) #全连接层的输出 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) """使用Dropout减少过拟合""" #使用placeholder占位符来表示神经元的输出在dropout中保持不变的概率 #在训练的过程中启用dropout，在测试过程中关闭dropout keep_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]) #模型预测输出 y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #交叉熵损失 cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) #模型训练,使用AdamOptimizer来做梯度最速下降 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #正确预测,得到True或False的List correct_prediction = tf.equal(tf.argmax(y_, 1), tf.argmax(y_conv, 1)) #将布尔值转化成浮点数，取平均值作为精确度 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #在session中先初始化变量才能在session中调用 sess.run(tf.initialize_all_variables()) #迭代优化模型 for i in range(20000): #每次取50个样本进行训练 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}) #模型中间不使用dropout 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})) 6、input_data.py文件 　　 注：python3中没有xrange，其range与python2中的xrange作用相同

#!/urs/bin/env python # -*- coding:utf-8 -*- # 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 import urllib 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 range(batch_size)], [ fake_label for _ in range(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