tensorflow 下CNN卷积神经网络实现

简介：卷积神经网络原理其实就是基于感受野，感受野讲的是只需识别某个图片的一个小区域就知道有某个东西。比如，在一个会议室里，要识别里面是否有投影仪，只需要看放投影仪的那部分小区域就知道这个会议室有投影仪。在卷积神经网络中也是通过卷积和池化来减少计算的复杂度，正式基于感受野的原理。

什么是卷积神经网络？我们以奠基之作Lenet-5为例子

上图中有输入层，卷积C1、C3、C5层，池化S2、S4层，全连接层、输出层

对于一张输入 32*32的黑白图片，经过5x5的filter(神经元)提取出 28x28 feature map。对于C1层，5x5的filter就有25个可训练参数，加上一个共享偏置，每个卷积核就有26个可训练参数，总共就有26*6=156个参数。有(5x5+1)x28x28x6=122304个连接，5*5神经元和1个bias都要和28x28相连

对于池化S2: filter是2x2的，采用的是加和乘一个参数加偏置，训练的参数有 (1+1)x6=12个参数，连接数(2×2+1)x6×14×14=5880个

由于C3的输入并非全部的S2输出，之后的参数和连接就不计算了。值得注意的是在S4(5x5)-->filter(5x5)-->C5类似于一个全连接，图中并没有标错

CNN源码可以下载

代码解释如下，其中将几乎每一步的解释都备注了在了代码。期间有不懂的可以使用print打印语句。欢迎评论。

# -*- coding: utf-8 -*-"""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.This should achieve a test error of 0.7%. Please keep this model as simple andlinear as possible, it is meant as a tutorial for simple convolutional models.Run with --self_test on the command line to execute a short self-test."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport sysimport timeimport numpyfrom six.moves import urllibfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfSOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'WORK_DIRECTORY = '/home/xzy/tf-input-data'IMAGE_SIZE = 28NUM_CHANNELS = 1PIXEL_DEPTH = 255NUM_LABELS = 10VALIDATION_SIZE = 500  # 自定义validate 数据集.SEED = 66478  # Set to None for random seed.BATCH_SIZE = 64NUM_EPOCHS = 10   # 自定义训练集上轮循10次EVAL_BATCH_SIZE = 64  # 定义每批次数据EVAL_FREQUENCY = 100  # 执行步长TRAIN_MAX = 5000  # 自定义训练数据大小tf.app.flags.DEFINE_boolean("self_test", False, "True if running a self test.")tf.app.flags.DEFINE_boolean('use_fp16', False, "Use half floats instead of full floats if True.")FLAGS = tf.app.flags.FLAGSdef data_type():  """返回激活函数、权重、placeholder变量的类型"""  if FLAGS.use_fp16:    return tf.float16  else:    return tf.float32def maybe_download(filename):  """如果数据不存在，则下载数据"""  if not tf.gfile.Exists(WORK_DIRECTORY):    tf.gfile.MakeDirs(WORK_DIRECTORY)  filepath = os.path.join(WORK_DIRECTORY, filename)  if not tf.gfile.Exists(filepath):    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)    with tf.gfile.GFile(filepath) as f:      size = f.size()    print('Successfully downloaded', filename, size, 'bytes.')  return filepathdef extract_data(filename, num_images):  """提取图像为4D tensor [image index, y, x, channels].  其值从[0, 255]重新调整为[-0.5, 0.5].  """  print('Extracting', filename)  with gzip.open(filename) as bytestream:    bytestream.read(16)    buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)    data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)    """    print('--------------------------------')    print('data1=', data)    print('--------------------------------')    """    data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH    """    print('################################')    print('data2=', data)    print('################################')    """    data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)    """    print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')    print('data3=', data)    print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')    """    return datadef extract_labels(filename, num_images):  """提取labels成一个int64 IDs的vector"""  print('Extracting', filename)  with gzip.open(filename) as bytestream:    bytestream.read(8)    buf = bytestream.read(1 * num_images)    labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)  return labelsdef fake_data(num_images):  """生成与MNIST的维度相匹配的假数据集"""  data = numpy.ndarray(      shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),      dtype=numpy.float32)  labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)  for image in xrange(num_images):    label = image % 2    data[image, :, :, 0] = label - 0.5    labels[image] = label  return data, labelsdef error_rate(predictions, labels):  """基于密集预测和稀疏标签返回错误率"""  return 100.0 - (100.0 * numpy.sum(numpy.argmax(predictions, 1) == labels) /                  predictions.shape[0])def main(argv=None):  # pylint: disable=unused-argument  if FLAGS.self_test:    print('Running self-test.')    train_data, train_labels = fake_data(256)    validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)    test_data, test_labels = fake_data(EVAL_BATCH_SIZE)    num_epochs = 1  else:    # 获取数据    train_data_filename = maybe_download('train-images-idx3-ubyte.gz')    train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')    test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')    test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')    # 提取数据并转成numpy的array.    train_data = extract_data(train_data_filename, 60000)    train_labels = extract_labels(train_labels_filename, 60000)    test_data = extract_data(test_data_filename, 1000)    test_labels = extract_labels(test_labels_filename, 1000)    # 产生一个验证集，并分片处理.VALIDATION_SIZE = 5000.    validation_data = train_data[:VALIDATION_SIZE, ...]  # 0-5000分片    validation_labels = train_labels[:VALIDATION_SIZE]    train_data = train_data[VALIDATION_SIZE:TRAIN_MAX+VALIDATION_SIZE, ...]    train_labels = train_labels[VALIDATION_SIZE:TRAIN_MAX+VALIDATION_SIZE]    num_epochs = NUM_EPOCHS   # NUM_EPOCHS = 10  train_size = train_labels.shape[0]  """ 训练样本和标签占位声明，每次训练都会喂一个批次的数据，  通过向Run()方法传递{feed_dict}参数"""  train_data_node = tf.placeholder(data_type(),                                   shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))  train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))  eval_data = tf.placeholder(data_type(),                             shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))  """持久化训练权重，通过{tf.initialize_all_variables().run()}执行操作"""  # 5x5 filter, depth 32.  conv1_weights = tf.Variable(tf.truncated_normal([5, 5, NUM_CHANNELS, 32],                                                  stddev=0.1, seed=SEED, dtype=data_type()))  conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))  conv2_weights = tf.Variable(tf.truncated_normal(      [5, 5, 32, 64], stddev=0.1, seed=SEED, dtype=data_type()))  conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))  # fully connected, depth 512.  fc1_weights = tf.Variable(tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],                            stddev=0.1, seed=SEED, dtype=data_type()))  fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))  fc2_weights = tf.Variable(tf.truncated_normal(      [512, NUM_LABELS], stddev=0.1, seed=SEED, dtype=data_type()))  fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS], dtype=data_type()))  """训练子图和评估子图共用一个模型，通过共享训练参数"""  def model(data, train=False):    """2维卷积, 使用 'SAME'处理边界 (feature map输出和输入有相同的维数       {strides} 是一个 4D array，他的形状为[image index, y, x, depth].       data=[bitchSize=64, 28, 28, channels=1]       conv1_weights=[5, 5, channels=1, filters=32]          print("---------initial data---------")    print('data=', data)    print('conv1_weights=', conv1_weights)    """    conv = tf.nn.conv2d(data, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')    relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))    """    最大池化    ksize：池化窗口的大小，取一个四维向量，一般是[1, height, width, 1]，    因为我们不想在batch和channels上做池化    """    pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')    """first conv-pool       conv=[64, 28, 28, filters=32]              pool=[64, 14, 14, channels=32]       conv2_weights=[5, 5, channels=32, filters=64]        print('---------first conv-pool---------')    print('pool=', pool)    print('conv=', conv)    print('conv2_weights=', conv2_weights)    """    conv = tf.nn.conv2d(pool, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')    relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))    pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')    """second conv-pool       conv=[64, 14, 14, filters=64]       pool=[64, 7, 7, channels=64]        print('----------second conv-pool---------')    print('conv=', conv)    print('pool=', pool)    """    # 重构feature map成2D矩阵，传给全链接网络    pool_shape = pool.get_shape().as_list()    reshape = tf.reshape(pool,                         [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])    """    print('-------reshape---------')    print('pool_shape=', pool_shape)    print('reshape=', reshape)    print('fc1_weights=',fc1_weights)    """    # 全链接网络层    hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)    # 训练数据时候，采用dropout随机丢弃50%的像素点    if train:      hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)    return tf.matmul(hidden, fc2_weights) + fc2_biases  """ 训练计算公式: logits + cross-entropy loss.      交叉熵：之前使用sigmoid{f(z) = 1/(1+\exp(-z))}函数计算神经元与真实值的欧式距离来判断偏差，      反向去修正权重和偏置，但函数接近1的时候，导数会非常小，学习的速度就会变得非常缓慢。解决这个缺点      就是使用交叉熵。参看http://blog.csdn.net/bixiwen_liu/article/details/52922008  """  logits = model(train_data_node, True)  loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(      logits=logits, labels=train_labels_node))  # 防止过拟合，对全链接网络的参数进行正则化，常见的是dropout.  regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +                  tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))  # 加入一个正则项.  loss += 5e-4 * regularizers  # 优化器: 每个批次都会增加，并且控制着学习率的衰变  batch = tf.Variable(0, dtype=data_type())  # 每次训练全部样本，学习率都会衰变，使用指数衰变  learning_rate = tf.train.exponential_decay(      0.01,                # 开始学习率      batch * BATCH_SIZE,  # 数据集大小=批次×每批次数据大小.      train_size,          # 训练多大更新学习率      0.95,                # 衰变率--单位时间内衰变的几率.      staircase=True)      # true代表训练train_size完更新一次，false代表每个样本都更新  # 使用momentum（动量--它模拟的是物体运动时的惯性，即更新的时候在一定程度上保留之前更新的方向） 优化器.  optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(loss, global_step=batch)  #  计算当前训练minibatch的预测概率.  train_prediction = tf.nn.softmax(logits)  #  计算测试和验证minibatch上的预测概率  eval_prediction = tf.nn.softmax(model(eval_data))  # 通过feeding多个批次的数据到{eval_data},并从{eval_predictions}获取结果并保存.  def eval_in_batches(data, sess):    """获取一个小批次数据集的所有预测概率."""    size = data.shape[0]    if size < EVAL_BATCH_SIZE:  # 数据集小于每批次数据EVAL_BATCH_SIZE=64      raise ValueError("batch size for evals larger than dataset: %d" % size)    predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)  # NUM_LABELS=10    for begin in xrange(0, size, EVAL_BATCH_SIZE):  # 每次增量为EVAL_BATCH_SIZE,一直到大于size      end = begin + EVAL_BATCH_SIZE      if end <= size:        predictions[begin:end, :] = sess.run(eval_prediction,                                             feed_dict={eval_data: data[begin:end, ...]})      else:        batch_predictions = sess.run(eval_prediction,                                     feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})        predictions[begin:, :] = batch_predictions[begin - size:, :]    return predictions  # 创建session开始训练数据  st_time = time.clock()  start_time = time.time()  with tf.Session() as sess:    # 初始化所有的训练参数    tf.initialize_all_variables().run()    print('Initialized!')    # 根据 steps进行循环.    # num_epochs = 10(非self_test)或 1(self_test)  BATCH_SIZE = 64 train_size=55000    for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):      # 计算当前 minibatch 在数据集里的偏移.      offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)      batch_data = train_data[offset:(offset + BATCH_SIZE), ...]      batch_labels = train_labels[offset:(offset + BATCH_SIZE)]      # 向数据字典里喂数据      feed_dict = {train_data_node: batch_data, train_labels_node: batch_labels}      # 取数据      _, l, lr, predictions = sess.run([optimizer, loss, learning_rate, train_prediction],                                       feed_dict=feed_dict)      if step % EVAL_FREQUENCY == 0:        elapsed_time = time.time() - start_time        start_time = time.time()        print('Step %d (epoch %.2f), %.1f ms' %              (step, float(step) * BATCH_SIZE / train_size,               1000 * elapsed_time / EVAL_FREQUENCY))        print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))        print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))        print('Validation error: %.1f%%' % error_rate(            eval_in_batches(validation_data, sess), validation_labels))        sys.stdout.flush()    # 打印最终的结果    test_error = error_rate(eval_in_batches(test_data, sess), test_labels)    print('Test error: %.1f%%' % test_error)    if FLAGS.self_test:      print('test_error', test_error)      assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (test_error,)  ed_time = time.clock()  print('******total train time:', ed_time - st_time)if __name__ == '__main__':  tf.app.run()

参考资料

lenet-5  http://blog.csdn.net/xuanyuansen/article/details/41800721

卷积神经网络  http://blog.csdn.net/u011453773/article/details/51597072