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输入图像描述

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}))

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：（

Answer 1

要检查的一些事项：

你的训练损失是否会下降？
训练数据集的准确度是否高？
您是否对验证数据集（预留的训练集的一部分？）获得高准确度？
您对目标数据集的准确度是否高？

如果你的训练损失很低（1.），那么你就不会学习，需要尝试不同的超参数，例如学习率。

如果你有高（2.）和低（3.），你就过度拟合，需要训练的时间更长，或者有更高的正则化惩罚。如果您有高（3.）和低（4.），则您的训练集不能代表您的实际训练集。需要使您的训练集更具代表性，或者至少更难，例如，通过添加扭曲

Tensor Flow Mninst外部图像预测不起作用

1 个答案: