tensorflow識別自己手寫數字，tensorflow識別手寫

tensorflow識別自己手寫數字，tensorflow識別手寫

tensorflow作為google開源的項目，現在趕超了caffe，好像成為最受歡迎的深度學習架構。確實在編寫的時候更能感受到代碼的真實存在，這點和caffe不同，caffe通過編寫設定檔進行網路的產生。環境tensorflow是0.10的版本，注意其他版本有的語句會有錯誤，這是tensorflow版本之間的相容問題。

還需要安裝PIL：pip install Pillow

圖片的格式： 

– 映像標準化，可安裝在20×20像素的框內，同時保留其長寬比。
– 圖片都集中在一個28×28的映像中。
– 像素以列為主進行排序。像素值0到255，0表示背景（白色），255表示前景（黑色）。

建立一個.png的檔案，背景是白色的，手寫的字型是黑色的，

下面是資料測試的代碼，一個兩層的卷積神經網，然後用save進行模型的儲存。

# coding: UTF-8 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import input_data ''''' 得到資料 ''' mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  training = mnist.train.images trainlable = mnist.train.labels testing = mnist.test.images testlabel = mnist.test.labels  print ("MNIST loaded") # 擷取互動方式 sess = tf.InteractiveSession() # 初始設定變數 x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) ''''' 產生權重函數，其中shape是資料的形狀 ''' def weight_variable(shape):   initial = tf.truncated_normal(shape, stddev=0.1)   return tf.Variable(initial) ''''' 產生偏執項 其中shape是資料形狀 ''' def bias_variable(shape):   initial = tf.constant(0.1, shape=shape)   return tf.Variable(initial)  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')  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)  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)   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)  keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  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)  cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  # 儲存網路訓練的參數 saver = tf.train.Saver() sess.run(tf.initialize_all_variables()) for i in range(8000):  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})  save_path = saver.save(sess, "model_mnist.ckpt") print("Model saved in life:", save_path)  print "test accuracy %g"%accuracy.eval(feed_dict={   x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})

其中input_data.py如下代碼，是進行mnist資料集的下載的：代碼是由mnist資料集提供的官方下載的版本。

# 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 

然後進行代碼的測試：

# import modules import sys import tensorflow as tf from PIL import Image, ImageFilter   def predictint(imvalue):   """   This function returns the predicted integer.   The imput is the pixel values from the imageprepare() function.   """    # Define the model (same as when creating the model file)   x = tf.placeholder(tf.float32, [None, 784])   W = tf.Variable(tf.zeros([784, 10]))   b = tf.Variable(tf.zeros([10]))    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)    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')    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)    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)    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)    keep_prob = tf.placeholder(tf.float32)   h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)    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)    init_op = tf.initialize_all_variables()   saver = tf.train.Saver()    """   Load the model_mnist.ckpt file   file is stored in the same directory as this python script is started   Use the model to predict the integer. Integer is returend as list.   Based on the documentatoin at   https://www.tensorflow.org/versions/master/how_tos/variables/index.html   """   with tf.Session() as sess:     sess.run(init_op)     saver.restore(sess, "model_mnist.ckpt")     # print ("Model restored.")      prediction = tf.argmax(y_conv, 1)     return prediction.eval(feed_dict={x: [imvalue], keep_prob: 1.0}, session=sess)   def imageprepare(argv):   """   This function returns the pixel values.   The imput is a png file location.   """   im = Image.open(argv).convert('L')   width = float(im.size[0])   height = float(im.size[1])   newImage = Image.new('L', (28, 28), (255)) # creates white canvas of 28x28 pixels    if width > height: # check which dimension is bigger     # Width is bigger. Width becomes 20 pixels.     nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width     if (nheight == 0): # rare case but minimum is 1 pixel       nheigth = 1       # resize and sharpen     img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)     wtop = int(round(((28 - nheight) / 2), 0)) # caculate horizontal pozition     newImage.paste(img, (4, wtop)) # paste resized image on white canvas   else:     # Height is bigger. Heigth becomes 20 pixels.     nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height     if (nwidth == 0): # rare case but minimum is 1 pixel       nwidth = 1       # resize and sharpen     img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)     wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition     newImage.paste(img, (wleft, 4)) # paste resized image on white canvas    # newImage.save("sample.png")    tv = list(newImage.getdata()) # get pixel values    # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.   tva = [(255 - x) * 1.0 / 255.0 for x in tv]   return tva   # print(tva)   def main(argv):   """   Main function.   """   imvalue = imageprepare(argv)   predint = predictint(imvalue)   print (predint[0]) # first value in list   if __name__ == "__main__":   main('2.png') 

其中我用於測試的代碼如下：

可以將圖片另存到路徑下面，然後進行測試。

（1）載入我的手寫數位映像。
（2）將映像轉換為黑白（模式“L”）
（3）確定原始映像的尺寸是最大的
（4）調整映像的大小，使得最大尺寸（醚的高度及寬度）為20像素，並且以相同的比例最小化尺寸刻度。
（5）銳利化映像。這會極大地強化結果。
（6）把映像粘貼在28×28像素的白色畫布上。在最大的尺寸上從頂部或側面置中映像4個像素。最大尺寸始終是20個像素和4 + 20 + 4 = 28，最小尺寸被定位在28和縮放的映像的新的大小之間差的一半。
（7）擷取新的映像（畫布+置中的映像）的像素值。
（8）歸一化像素值到0和1之間的一個值（這也在TensorFlow MNIST教程中完成）。其中0是白色的，1是純黑色。從步驟7得到的像素值是與之相反的，其中255是白色的，0黑色，所以數值必須反轉。下述公式包括反轉和規格化（255-X）* 1.0 / 255.0