使用外部图像的Tensor Flow Mninst示例预测不起作用

Question

我是神经网络的新手。 我已经通过了TensorFlow mninst ML初学者

使用了tensorflow基本的mnist教程

并尝试使用外部图像进行预测

我更新了tensorflow提供的mnist示例

On top of that i have added few things :
1. Saving trained models locally
2. loading the saved models.
3. preprocessing the image into 28 * 28.

i have attached the image for reference

 1. while training the models, save it locally. So i can reuse it at any point of time.
 2. once after training, loading the models.
 3. creating an external image via gimp which contains any one values ranging from [0 - 9]
 4. using opencv to convert the image into 28 * 28 image and reversing the bit as well.
 5. Then trying to predict.

我能够训练模型并妥善保存。

我得到了不正确的预测。

在

下面找到我的代码

1. TrainSimple.py

   # Load MNIST Data
   from tensorflow.examples.tutorials.mnist import input_data
   mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
   
   from random import randint
   from scipy import misc
   
   # Start TensorFlow InteractiveSession
   import tensorflow as tf
   sess = tf.InteractiveSession()
   
   # Placeholders
   x = tf.placeholder(tf.float32, shape=[None, 784])
   y_ = tf.placeholder(tf.float32, shape=[None, 10])
   
   # Variables
   W = tf.Variable(tf.zeros([784,10]))
   b = tf.Variable(tf.zeros([10]))
   
   sess.run(tf.initialize_all_variables())
   
   # Predicted Class and Cost Function
   y = tf.nn.softmax(tf.matmul(x,W) + b)
   cross_entropy = -tf.reduce_sum(y_*tf.log(y))
   
   saver = tf.train.Saver()  # defaults to saving all variables
   
   # GradientDescentOptimizer
   train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
   
   
   # Train the Model
   
      for i in range(40000):
           if (i + 1) == 40000 :
               saver.save(sess, "/Users/xxxx/Desktop/TensorFlow/"+"/model.ckpt", global_step=i)
       batch = mnist.train.next_batch(50)
       train_step.run(feed_dict={x: batch[0], y_: batch[1]})
   
   # Evaluate the Model
   correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
   
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   
   print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
   

2. loadImageAndPredict.py

   from random import randint
   from scipy import misc
   import numpy as np
   import cv2
   
   def preProcess(invert_file):
       print "preprocessing the images" + invert_file
       image=cv2.imread(invert_file,0)
       ret,image_thresh = cv2.threshold(image,127,255,cv2.THRESH_BINARY)
       l,b=image.shape
   
       fr=0
       lr=0
       fc=0
       lc=0
   
       i=0
       while len(set(image_thresh[i,]))==1:
           i+=1
       fr=i
   
       i=0
       while len(set(image_thresh[-1+i,]))==1:
           i-=1
       lr=i+l
   
       j=0
       while len(set(image_thresh[0:,j]))==1:
           j+=1
       fc=j
   
       j=0
       while len(set(image_thresh[0:,-1+j]))==1:
           j-=1
       lc=j+b
   
       image_crop=image_thresh[fr:lr,fc:lc]
       image_padded= cv2.copyMakeBorder(image_crop,5,5,5,5,cv2.BORDER_CONSTANT,value=255)
       image_resized = cv2.resize(image_padded, (28, 28))
       image_resized = (255-image_resized)
       cv2.imwrite(invert_file, image_resized)
   
   import tensorflow as tf
   sess = tf.InteractiveSession()
   
   # Placeholders
   x = tf.placeholder(tf.float32, shape=[None, 784])
   y_ = tf.placeholder(tf.float32, shape=[None, 10])
   
   # # Variables
   W = tf.Variable(tf.zeros([784,10]))
   b = tf.Variable(tf.zeros([10]))
   
   
   
   # Predicted Class and Cost Function
   y = tf.nn.softmax(tf.matmul(x,W) + b)
   cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
   
   saver = tf.train.Saver()  # defaults to saving all variables - in this case w and b
   
   # Train the Model
   # GradientDescentOptimizer
   train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
   flag_1 = 0
   
   # create an an array where we can store 1 picture
   images = np.zeros((1,784))
   # and the correct values
   correct_vals = np.zeros((1,10))
   
   
   preProcess("4_white.png")
   
   gray = cv2.imread("4_white.png", 0)
   
   flatten = gray.flatten() / 255.0
   """
   we need to store the flatten image and generate
   the correct_vals array
   correct_val for a digit (9) would be
   [0,0,0,0,0,0,0,0,0,1]
   """
   images[0] = flatten
   # print images[0]
   print len(images[0])
   
   sess.run(tf.initialize_all_variables())
   
   ckpt = tf.train.get_checkpoint_state("/Users/xxxx/Desktop/TensorFlow")
   if ckpt and ckpt.model_checkpoint_path:
       saver.restore(sess, ckpt.model_checkpoint_path)
       my_classification = sess.run(tf.argmax(y, 1), feed_dict={x: [images[0]]})
       print 'Neural Network predicted', my_classification[0], "for your digit"
   

我不确定我做了什么错误。

认为简单模型可能不起作用我已使用此卷积代码进行预测。

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/mnist/convolutional.py

即便如此也无法正确预测：（