使用训练的mnist识别自己写的数字

用我上次写的mnist1024层全连接层模型，训练了20000次，在测试集上的正确率为99.3%，这次打算写一个直接使用训练模型进行图片识别的python程序，第一步是自己手动构建个测试矩阵测试，因为第一次尝试这种东西所以遇到了好多问题。。。，现附上代码吧，测试了几下没问题

# -*- coding:gbk -*-
import tensorflow as tf
import numpy

#添加x作为占位符
x=tf.placeholder("float", [1, 784])

#生成权重函数
def weight_variable(shape):
#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。这个函数产生正态分布，均值和标准差自己设定
#权重在初始化时应该加入少量的噪声来打破对称性以及避免0梯度
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,dtype=tf.float32)

#生成偏置函数
#由于我们使用的是ReLU神经元，因此比较好的做法是用一个较小的正数来初始化偏置项，以避免神经元节点输出恒为0的问题（dead neurons）
def bias_variable(shape):
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial,dtype=tf.float32)

#卷积函数
#卷积使用1步长，0边距的模板，池化用2x2的模板
def conv2d(x, W):
#x：待卷积的矩阵具有[batch, in_height, in_width, in_channels]这样的shape
#w：卷积核具有[filter_height, filter_width, in_channels, out_channels]这样的shape
#strides：卷积时在图像每一维的步长，这是一个一维的向量，长度4
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#池化函数
#和卷积基本相同
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

#卷积在每个5x5的patch中算出32个特征。
#卷积的权重张量形状是[5, 5, 1, 32]，前两个维度是patch的大小，
#接着是输出几个单位，和输出的几个维度
W_conv1=weight_variable([5, 5, 1, 32])
b_conv1=bias_variable([32])

#shape:[batch, in_height, in_width, in_channels]
x_image=tf.reshape(x, [-1,28,28,1])

#卷积+偏置，然后给relu激活函数，最后激活函数返回值池化
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #output size 28*28*32
h_pool1=max_pool_2x2(h_conv1)  #output size 14*14*32

#第二层卷积，池化
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) #output size 14*14*64
h_pool2=max_pool_2x2(h_conv2) #output size 7*7*64

#全连接层1
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)

#全连接层3
W_fc2=weight_variable([1024, 10])
b_fc2=bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

prediction=tf.argmax(y_conv,1)

#初始化数据读取模型
sess = tf.InteractiveSession()
saver=tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess,"my_net/save_net.ckpt")
imgmatrix=numpy.array([
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,
]).reshape(1,784)

result=sess.run(prediction,feed_dict={x:imgmatrix})
print("The reslut is",result)

输出为7，不错不错

后面写图片转矩阵自动测试，待更新

遇到的问题
1.!!!!!!!!!不能同tf.zeros生成数据放到哪里，反正各种有问题
2.!!!!!!!!!numpy.array(784)后还需要.reshape(1,784)
3.去除所有训练阶段的代码，精简优化

更新：自动识别文件夹下的图片，tif格式灰度图28*28像素请自己去photoshop画

代码

# -*- coding:gbk -*-
import tensorflow as tf
import numpy
from PIL import Image

#添加x作为占位符
x=tf.placeholder("float", [1, 784])

#生成权重函数
def weight_variable(shape):
#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。这个函数产生正态分布，均值和标准差自己设定
#权重在初始化时应该加入少量的噪声来打破对称性以及避免0梯度
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,dtype=tf.float32)

#生成偏置函数
#由于我们使用的是ReLU神经元，因此比较好的做法是用一个较小的正数来初始化偏置项，以避免神经元节点输出恒为0的问题（dead neurons）
def bias_variable(shape):
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial,dtype=tf.float32)

#卷积函数
#卷积使用1步长，0边距的模板，池化用2x2的模板
def conv2d(x, W):
#x：待卷积的矩阵具有[batch, in_height, in_width, in_channels]这样的shape
#w：卷积核具有[filter_height, filter_width, in_channels, out_channels]这样的shape
#strides：卷积时在图像每一维的步长，这是一个一维的向量，长度4
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#池化函数
#和卷积基本相同
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

#卷积在每个5x5的patch中算出32个特征。
#卷积的权重张量形状是[5, 5, 1, 32]，前两个维度是patch的大小，
#接着是输出几个单位，和输出的几个维度
W_conv1=weight_variable([5, 5, 1, 32])
b_conv1=bias_variable([32])

#shape:[batch, in_height, in_width, in_channels]
x_image=tf.reshape(x, [-1,28,28,1])

#卷积+偏置，然后给relu激活函数，最后激活函数返回值池化
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #output size 28*28*32
h_pool1=max_pool_2x2(h_conv1)  #output size 14*14*32

#第二层卷积，池化
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) #output size 14*14*64
h_pool2=max_pool_2x2(h_conv2) #output size 7*7*64

#全连接层1
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)

#全连接层3
W_fc2=weight_variable([1024, 10])
b_fc2=bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

prediction=tf.argmax(y_conv,1)

#初始化数据读取模型
sess = tf.InteractiveSession()
saver=tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess,"my_net/save_net.ckpt")
img=Image.open('.//image//1.tif')
imgmatrix=numpy.array(img).reshape(1,784)

result=sess.run(prediction,feed_dict={x:imgmatrix})
print("The reslut is",result)

除了识别不出9其他没什么问题

再次更新！！！批量识别加识别不出9问题解决

上次写的识别器识别不出9，然后去网上搜索了一下了解了mnist训练集的构成，mnist数据集里的数字图片都是经过尺寸归一化以及数字居中处理的，所以如果你写的数字太偏向某一边的话会产生问题，除非你自己也对数字进行归一化居中处理，暂时不知道他的处理方式暂时不弄，后面弄了再更新吧，附上批量处理程序

训练了三个模型一个是2000次迭代产生的模型，一个是10000次迭代产生的模型，一个是30000次迭代产生的模型，准确度经过测试确实是一个比一个高，最后一个基本可以无差错识别我写的数字

# -*- coding:gbk -*-
import tensorflow as tf
import numpy
from PIL import Image

#添加x作为占位符
x=tf.placeholder("float", [1, 784])

#生成权重函数
def weight_variable(shape):
#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。这个函数产生正态分布，均值和标准差自己设定
#权重在初始化时应该加入少量的噪声来打破对称性以及避免0梯度
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial,dtype=tf.float32)

#生成偏置函数
#由于我们使用的是ReLU神经元，因此比较好的做法是用一个较小的正数来初始化偏置项，以避免神经元节点输出恒为0的问题（dead neurons）
def bias_variable(shape):
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial,dtype=tf.float32)

#卷积函数
#卷积使用1步长，0边距的模板，池化用2x2的模板
def conv2d(x, W):
#x：待卷积的矩阵具有[batch, in_height, in_width, in_channels]这样的shape
#w：卷积核具有[filter_height, filter_width, in_channels, out_channels]这样的shape
#strides：卷积时在图像每一维的步长，这是一个一维的向量，长度4
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#池化函数
#和卷积基本相同
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

#卷积在每个5x5的patch中算出32个特征。
#卷积的权重张量形状是[5, 5, 1, 32]，前两个维度是patch的大小，
#接着是输出几个单位，和输出的几个维度
W_conv1=weight_variable([5, 5, 1, 32])
b_conv1=bias_variable([32])

#shape:[batch, in_height, in_width, in_channels]
x_image=tf.reshape(x, [-1,28,28,1])

#卷积+偏置，然后给relu激活函数，最后激活函数返回值池化
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #output size 28*28*32
h_pool1=max_pool_2x2(h_conv1)  #output size 14*14*32

#第二层卷积，池化
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) #output size 14*14*64
h_pool2=max_pool_2x2(h_conv2) #output size 7*7*64

#全连接层1
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)

#全连接层3
W_fc2=weight_variable([1024, 10])
b_fc2=bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)
prediction=tf.argmax(y_conv,1)

#初始化数据读取模型
sess = tf.InteractiveSession()
saver=tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess,"30000/save_net.ckpt")

imgmatrix=[]
for i in range(10):
img=Image.open('.//image//'+str(i)+'.tif')
imgmatrix.append(numpy.array(img).reshape(1,784))

for i in range(10):
result=sess.run(prediction,feed_dict={x:imgmatrix[i]})
print(i,"The reslut is",result)