[DL學習筆記]從人工神經網路到捲積神經網路_3_使用tensorflow搭建CNN來分類not_MNIST數據(有一些問題)

3：用tensorflow搭個神經網路出來

        為什麼用tensorflow呢，應為谷歌是親爹啊，雖然有些人說caffe更適合圖像啊mxnet效率更高等等，但爸爸就是爸爸，Android都能那麼火，一個道理嘛。其實這些個框架一通百通，就是語法不一樣了些。從tensorflow開始吧。

        關於tf的安裝詳見另一篇博文，此處tensorflow的學習基本來自Udacity中google的深度學習課程。

1:tensorflow的計算圖

在tensorflow中編寫代碼可以分成兩個部分，首先是要定義一個計算的流程，或者叫計算圖，然後再建立一個任務，讓tensorflow調用系統資源去運算這個東西，舉個慄子：

import tensorflow as tf #導入tensorflow庫
matrix1=tf.constant([[3.,3.]])#創建常量節點
matrix2=tf.constant([2.],[2.])
product=tf.matmul(matrix1,matrix2)#創建矩陣乘法節點

上邊並沒有運算具體的值，而只是一個運算圖。

真正的計算要用到session：

sess=tf.Session()#啟動預設圖
#運行這裡會有一堆運行信息出來
result = sess.run(product)#調用sess的run方法來執行矩陣乘法節點的操作，product代表了矩陣乘法這個節點的輸出
print result
sess.close()#完成任務後關閉會話

這就是tf的基礎運行方式，對於變數，使用Variable方法定義：

W1=tf.Variable(tf.zeros((2,2)), name=”weights”)
sess.run(tf.initialize_all_variables())#變數需要預先初始化
print sess.run(W1)

另一個慄子：

state = tf.Variable(0,name=”counter”)
new_value=tf.add(state, tf.constant(1))#對state加1
update=tf.assign(state,new_value)#將自增後的值重新賦值給state
with tf.Session() as sess: #使用with可以省去close()操作，還可以處理一些操作出現的異常(也可以用try)
    sess.run(tf.initialize_all_variables())
    print(sess.run(state))#輸出計數器值
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

為毛谷歌爸爸這麼蛋疼呢，直接算不好嗎？其實一點也不蛋疼，這麼設計，同樣一套計算圖，就可以扔給不同的設備或者分散式的設備去運算了：

with tf.Session() as sess:
    with tf.device(“/gpu:1”):
    …

另一個好處就是python的運算效率較低，所以設計成使用python編寫運算圖，之後再使用python之外的運算器(比如底層的C++)去計算。

2:使用tensorflow搭建一個捲積神經網路

這裡會詳解Google發佈在udacity中使用CNN分類not_MINIST數據代碼，這些代碼包含在了tensorflow源代碼中的examples中

https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/udacity

<1>:準備數據(notMINIST)

代碼的第一部分是載入數據：

# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
# Config the matplotlib backend as plotting inline in IPython
%matplotlib inline

url = 'http://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None

def download_progress_hook(count, blockSize, totalSize):
  """A hook to report the progress of a download. This is mostly intended for users with
  slow internet connections. Reports every 5% change in download progress.
  """
  global last_percent_reported
  percent = int(count * blockSize * 100 / totalSize)

  if last_percent_reported != percent:
    if percent % 5 == 0:
      sys.stdout.write("%s%%" % percent)
      sys.stdout.flush()
    else:
      sys.stdout.write(".")
      sys.stdout.flush()
      
    last_percent_reported = percent
        
def maybe_download(filename, expected_bytes, force=False):
  """Download a file if not present, and make sure it's the right size."""
  if force or not os.path.exists(filename):
    print('Attempting to download:', filename) 
    filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)
    print('\nDownload Complete!')
  statinfo = os.stat(filename)
  if statinfo.st_size == expected_bytes:
    print('Found and verified', filename)
  else:
    raise Exception(
      'Failed to verify ' + filename + '. Can you get to it with a browser?')
  return filename

train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)
test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)

上邊的代碼是下載所需要的數據集壓縮包，下一步是解壓

num_classes = 10
np.random.seed(133)

def maybe_extract(filename, force=False):
  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz
  if os.path.isdir(root) and not force:
    # You may override by setting force=True.
    print('%s already present - Skipping extraction of %s.' % (root, filename))
  else:
    print('Extracting data for %s. This may take a while. Please wait.' % root)
    tar = tarfile.open(filename)
    sys.stdout.flush()
    tar.extractall()
    tar.close()
  data_folders = [
    os.path.join(root, d) for d in sorted(os.listdir(root))
    if os.path.isdir(os.path.join(root, d))]
  if len(data_folders) != num_classes:
    raise Exception(
      'Expected %d folders, one per class. Found %d instead.' % (
        num_classes, len(data_folders)))
  print(data_folders)
  return data_folders
  
train_folders = maybe_extract(train_filename)
test_folders = maybe_extract(test_filename)

解壓後可以查看一下代碼文件所在的文件夾中會有兩個文件夾not_MNIST_large和not_MNIST_small，large用來訓練，small用來驗證，每個文件夾中都有10個文件夾，分別保存了A到J的圖像(28*28)，這些圖像就是數據集，標簽就是A到J，當然之前下載的壓縮文件也在。下一步是將這些數據轉換成python中更容易處理的pickle格式，為了確保記憶體裝得下，我們把每一個類別分別轉換成一個獨立的pickle文件，同時也對數據進行去均值和歸一化，在這個過程中可能會有一些文件是不可讀的，跳過即可，無所謂：

image_size = 28  # Pixel width and height.
pixel_depth = 255.0  # Number of levels per pixel.

def load_letter(folder, min_num_images):
  """Load the data for a single letter label."""
  image_files = os.listdir(folder)
  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
                         dtype=np.float32)
  print(folder)
  num_images = 0
  for image in image_files:
    image_file = os.path.join(folder, image)#文件路徑拼接
    try:
      image_data = (ndimage.imread(image_file).astype(float) - 
                    pixel_depth / 2) / pixel_depth #去均值和歸一化
      if image_data.shape != (image_size, image_size):
        raise Exception('Unexpected image shape: %s' % str(image_data.shape))
      dataset[num_images, :, :] = image_data
      num_images = num_images + 1
    except IOError as e:
      print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
    
  dataset = dataset[0:num_images, :, :]
  if num_images < min_num_images:
    raise Exception('Many fewer images than expected: %d < %d' %
                    (num_images, min_num_images))
    
  print('Full dataset tensor:', dataset.shape)
  print('Mean:', np.mean(dataset))
  print('Standard deviation:', np.std(dataset))
  return dataset
        
def maybe_pickle(data_folders, min_num_images_per_class, force=False):
  dataset_names = []
  for folder in data_folders:#本例中就是not_MNIST_large/A, not_MNIST_large/B等等
    set_filename = folder + '.pickle'#folders是A到J，設定文件名
    dataset_names.append(set_filename)#往dataset_names後邊添加set_filename
    if os.path.exists(set_filename) and not force:
      # You may override by setting force=True.
      print('%s already present - Skipping pickling.' % set_filename)
    else:
      print('Pickling %s.' % set_filename)
      dataset = load_letter(folder, min_num_images_per_class)
      try:
        with open(set_filename, 'wb') as f:
          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
      except Exception as e:
        print('Unable to save data to', set_filename, ':', e)
  
  return dataset_names

train_datasets = maybe_pickle(train_folders, 45000)
test_datasets = maybe_pickle(test_folders, 1800)

上邊的代碼就是把數據壓縮到了一個pickle文件中去了，這樣生成的數據文件可以在後續的程式中繼續使用，這也就是沒有直接採集圖像數據的原因之一，下一步是將這些pickle文件中的數據進行合併和分類，生成一個擁有訓練集、測試集合驗證集的文件，訓練數據的量取決於記憶體，如果非要使用超出記憶體的量的數據必須就分開運算了。

def make_arrays(nb_rows, img_size):#在merge_dagasets方法中把數據轉換成圖片個數*imgsize*imgsize(28)，同時建一個標簽向量，大小為nb_rows
  if nb_rows:
    dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)
    labels = np.ndarray(nb_rows, dtype=np.int32)
  else:
    dataset, labels = None, None
  return dataset, labels

def merge_datasets(pickle_files, train_size, valid_size=0):
  num_classes = len(pickle_files)
  valid_dataset, valid_labels = make_arrays(valid_size, image_size)
  train_dataset, train_labels = make_arrays(train_size, image_size)
  vsize_per_class = valid_size // num_classes
  tsize_per_class = train_size // num_classes
    
  start_v, start_t = 0, 0
  end_v, end_t = vsize_per_class, tsize_per_class
  end_l = vsize_per_class+tsize_per_class
  for label, pickle_file in enumerate(pickle_files):#將分佈在10個pickle文件中的數據合併成一個張量     
    try:
      with open(pickle_file, 'rb') as f:
        letter_set = pickle.load(f)
        # 將讀取到的pickle文件中的數據打亂
        np.random.shuffle(letter_set)
        if valid_dataset is not None:
          valid_letter = letter_set[:vsize_per_class, :, :]
          valid_dataset[start_v:end_v, :, :] = valid_letter
          valid_labels[start_v:end_v] = label
          start_v += vsize_per_class
          end_v += vsize_per_class
                    
        train_letter = letter_set[vsize_per_class:end_l, :, :]
        train_dataset[start_t:end_t, :, :] = train_letter
        train_labels[start_t:end_t] = label
        start_t += tsize_per_class
        end_t += tsize_per_class
    except Exception as e:
      print('Unable to process data from', pickle_file, ':', e)
      raise
    
  return valid_dataset, valid_labels, train_dataset, train_labels
            
            
train_size = 200000
valid_size = 10000
test_size = 10000

valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(
  train_datasets, train_size, valid_size)
_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)

print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_dataset.shape, valid_labels.shape)
print('Testing:', test_dataset.shape, test_labels.shape)

最後將數據再次打亂保存後，就得到了最後的pickle文件。

def randomize(dataset, labels):
  permutation = np.random.permutation(labels.shape[0])
  shuffled_dataset = dataset[permutation,:,:]
  shuffled_labels = labels[permutation]
  return shuffled_dataset, shuffled_labels
train_dataset, train_labels = randomize(train_dataset, train_labels)
test_dataset, test_labels = randomize(test_dataset, test_labels)
valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)

pickle_file = 'notMNIST.pickle'
try:
  f = open(pickle_file, 'wb')
  save = {#存到一個dictionary中去
    'train_dataset': train_dataset,#num*28*28
    'train_labels': train_labels,#num*10
    'valid_dataset': valid_dataset,#…
    'valid_labels': valid_labels,
    'test_dataset': test_dataset,
    'test_labels': test_labels,
    }
  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
  f.close()
except Exception as e:
  print('Unable to save data to', pickle_file, ':', e)
  raise
statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)

上面是一些預操作，然後我們讀取這個pickle文件，得到捲積神經網路要使用的數據文件：

pickle_file = 'notMNIST.pickle'

with open(pickle_file, 'rb') as f:
  save = pickle.load(f)
  train_dataset = save['train_dataset']
  train_labels = save['train_labels']
  valid_dataset = save['valid_dataset']
  valid_labels = save['valid_labels']
  test_dataset = save['test_dataset']
  test_labels = save['test_labels']
  del save  # hint to help gc free up memory
  print('Training set', train_dataset.shape, train_labels.shape)
  print('Validation set', valid_dataset.shape, valid_labels.shape)
  print('Test set', test_dataset.shape, test_labels.shape)

運行後：

Training set (200000, 28, 28) (200000,)

Validation set (10000, 28, 28) (10000,)

Test set (18724, 28, 28) (18724,)

可見訓練集、驗證集和測試集的原始格式。如果要將數據用到一個人工神經網路中，就要把每個圖像數據都轉換成一個長×寬維的向量，而在捲積神經網路中我們需要將圖片數據轉換成長×寬×深度的樣子，同時將labels轉換成one-hot encodings格式，於是：

image_size = 28
num_labels = 10
num_channels = 1 # grayscale，如果要使用RGB格式數據就是3了

import numpy as np

def reformat(dataset, labels):
  dataset = dataset.reshape(
    (-1, image_size, image_size, num_channels)).astype(np.float32)
#-1表示我懶得計算該填什麼數字，由python通過a和其他的值3推測出來(這句話來自知乎，感覺好精辟啊)
  labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32) 
#這句寫的很迷，腫麽解釋。。。
#labels[:,None]相當於把shape為(20w,)的labels轉換成shape為(20w,1)的數組，從[1,2,3,4...]到[[1],[2],[3],…]
#np.arange是生成了一個[0,1,2,3...]的(10,)的數組
#判斷一個(10,)是否等於一個(10,1)的數組，或者說判斷一個列向量是否等於一個行向量，可理解為矩陣乘法了，定義乘法規則為一樣就是true，不一樣就是false，那麼這個判斷式的結果就是一個20w*10的數組。
  return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)

運行結果為：

Training set (200000, 28, 28, 1) (200000, 10)

Validation set (10000, 28, 28, 1) (10000, 10)

Test set (10000, 28, 28, 1) (10000, 10)

下一步我們先定義一個用來檢測預測精度的方法：

def accuracy(predictions, labels):
  return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
          / predictions.shape[0])#註意這裡的argmax方法返回的是數組的索引值。

<2>:Draw a graph

前邊說過了，tensorflow中進行運算，首先需要構建一個運算圖，在這裡將建立一個擁有兩個捲積層和一個全連接層的捲積神經網路，算這個東西需要很土豪的顯卡，所以限制了一下深度和全捲積層的節點。

batch_size = 16 #SGD每次選取的圖片個數
patch_size = 5 #捲積視窗大小
depth = 16 #捲積深度，就是特征圖的個數
num_hidden = 64 #全連接層隱層大小

graph = tf.Graph()

with graph.as_default():

  # Input data.4
  tf_train_dataset = tf.placeholder(
    tf.float32, shape=(batch_size, image_size, image_size, num_channels))#每次選出batch_size個圖片參與運算
  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)
  
  # Variables.
  layer1_weights = tf.Variable(tf.truncated_normal(
      [patch_size, patch_size, num_channels, depth], stddev=0.1))#隨機初始化第一卷積層權重參數，depth*num_channels張特征圖，滑動視窗大小為5*5
  layer1_biases = tf.Variable(tf.zeros([depth]))#第一卷積層bias項初始化為0
  layer2_weights = tf.Variable(tf.truncated_normal(#隨機初始化第二捲積層權重參數，depth*depth張特征圖，滑動視窗5*5
      [patch_size, patch_size, depth, depth], stddev=0.1))
  layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))#第二捲積層bias項初始化為0
  layer3_weights = tf.Variable(tf.truncated_normal(
      [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))#全連接層第一層，//4是因為後邊定義模型的時候定義stride為2，
    #所以兩次捲積後的數據就是7*7*16*16了？？？？？？？
  layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))
  layer4_weights = tf.Variable(tf.truncated_normal(#全連接層第二層
      [num_hidden, num_labels], stddev=0.1))
  layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))
  
  # Model.
  def model(data):
    conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')#這裡1,2,2,1是stride，依次對應到data的格式中去
    #same padding是補0的那種padding模式，比較便於運算，所以基本上都用這種的。
    hidden = tf.nn.relu(conv + layer1_biases)
    conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')
    hidden = tf.nn.relu(conv + layer2_biases)
    shape = hidden.get_shape().as_list()
    reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])
    hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)
    return tf.matmul(hidden, layer4_weights) + layer4_biases
  
  # Training computation.
  logits = model(tf_train_dataset)
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
    
  # Optimizer.
  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)#梯度下降
  
  # Predictions for the training, validation, and test data.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
  test_prediction = tf.nn.softmax(model(tf_test_dataset))

這裡看程式的話感覺好像是計算了，尤其是最後幾句話，其實並沒有計算的，下一步才是使用session來計算。

num_steps = 1001
#batch_size=16
with tf.Session(graph=graph) as session:
  #tf.global_variables_initializer().run()#for old version of tf0
  session.run(tf.initialize_all_variables())
  print('Initialized')
  for step in range(num_steps):
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)#這句是防止迭代次數過多超出數據集範圍，就通過取餘數改變取batch的偏置
    batch_data = train_dataset[offset:(offset + batch_size), :, :, :]
    batch_labels = train_labels[offset:(offset + batch_size), :]
    feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
    
    _, l, predictions = session.run(
      [optimizer, loss, train_prediction], feed_dict=feed_dict)
    if (step % 50 == 0):
      print('Minibatch loss at step %d: %f' % (step, l))
      print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))
      print('Validation accuracy: %.1f%%' % accuracy(
        valid_prediction.eval(), valid_labels))
  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

以上就是實用捲積神經網路簡單的區分not_MNIST數據的程式，池化層我還沒加上，還有dropout防止過擬合也沒有添加，待續。先看rfcn吧。