tensorflow识别自己手写数字

# coding: UTF-8 
import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 
import input_data 
''''' 
得到数据 
''' 
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") 
# 获取交互式的方式 
sess = tf.InteractiveSession() 
# 初始化变量 
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])) 
''''' 
生成权重函数，其中shape是数据的形状 
''' 
def weight_variable(shape): 
  initial = tf.truncated_normal(shape, stddev=0.1) 
  return tf.Variable(initial) 
''''' 
生成偏执项 其中shape是数据形状 
''' 
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")) 
 
# 保存网络训练的参数 
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})

# 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 

# 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')