大学整数集z手写体怎么写(大小写字母怎么写手写体)

import tensorflow as tf

import tensorflow.examples.tutorials.mnist.input_data as input_data

import os

import sys

mnist_path = "E:/xx/dataset/MNIST_data"

mnist = input_data.read_data_sets(mnist_path, one_hot=True)

x = tf.placeholder("float", shape=)

y_ = tf.placeholder("float", shape=)

W = tf.Variable(tf.zeros())

b = tf.Variable(tf.zeros())

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)

#权重初始化

def conv2d(x, W):

return tf.nn.conv2d(x, W, strides=, padding='SAME')

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize=,

strides=, padding='SAME')

#第一层卷积

W_conv1 = weight_variable()

b_conv1 = bias_variable()

x_image = tf.reshape(x, )

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

h_pool1 = max_pool_2x2(h_conv1)

#d第二层卷积

W_conv2 = weight_variable()

b_conv2 = bias_variable()

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)

#全连接层

W_fc1 = weight_variable()

b_fc1 = bias_variable()

h_pool2_flat = tf.reshape(h_pool2, )

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)

#输出层

W_fc2 = weight_variable()

b_fc2 = bias_variable()

#训练和评估模型

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.AdagradOptimizer(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"))

ckpt_dir = "./ckpt_dir"

if not os.path.exists(ckpt_dir):

os.makedirs(ckpt_dir)

#标志变量不参与到训练中

global_step = tf.Variable(0, name='global_step', trainable=False)

saver = tf.train.Saver()

if sys.argv.__len__()>1 and int(sys.argv)>0:

end=int(sys.argv)

else:

end=10000

with tf.Session() as sess:

ckpt = tf.train.get_checkpoint_state(ckpt_dir)

if ckpt and ckpt.model_checkpoint_path:

print(ckpt.model_checkpoint_path)

saver.restore(sess, ckpt.model_checkpoint_path) # restore all variables

else:

tf.global_variables_initializer().run()

start = global_step.eval() # get last global_step

print("Start from:", start)

for i in range(start, end):

batch = mnist.train.next_batch(100)

if i%10 == 0:

train_accuracy = accuracy.eval(session=sess,feed_dict={

x:batch, y_: batch, keep_prob: 1.0})

print ("step %d, training accuracy %g"%(i, train_accuracy))

train_step.run(session=sess,feed_dict={x: batch, y_: batch, keep_prob: 0.5})

global_step.assign(i).eval() #i更新global_step.

saver.save(sess, ckpt_dir + "/model.ckpt", global_step=global_step)

print ("test accuracy %g"%accuracy.eval(session=sess,feed_dict={

import glob

import tensorflow as tf

import os

import numpy as np

import sys

from PIL import Image#pillow(PIL)

x = tf.placeholder("float", shape=)

y_ = tf.placeholder("float", shape=)

W = tf.Variable(tf.zeros())

b = tf.Variable(tf.zeros())

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)

#权重初始化

def conv2d(x, W):

return tf.nn.conv2d(x, W, strides=, padding='SAME')

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize=,

strides=, padding='SAME')

#第一层卷积

W_conv1 = weight_variable()

b_conv1 = bias_variable()

x_image = tf.reshape(x, )

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

h_pool1 = max_pool_2x2(h_conv1)

#d第二层卷积

W_conv2 = weight_variable()

b_conv2 = bias_variable()

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

h_pool2 = max_pool_2x2(h_conv2)

#全连接层

W_fc1 = weight_variable()

b_fc1 = bias_variable()

h_pool2_flat = tf.reshape(h_pool2, )

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)

#输出层

W_fc2 = weight_variable()

b_fc2 = bias_variable()

#训练和评估模型

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

ckpt_dir = "./ckpt_dir"

saver = tf.train.Saver()

with tf.Session() as sess:

ckpt = tf.train.get_checkpoint_state(ckpt_dir)

if ckpt and ckpt.model_checkpoint_path:

print(ckpt.model_checkpoint_path)

saver.restore(sess, ckpt.model_checkpoint_path) # restore all variables

else:

raise FileNotFoundError("未找到模型")#raise 引发异常

# image_path="./test_images/0_0.bmp"

# # image_path="/home/mnist/mnist03/test_data/"+sys.argv+".png"

# #image_path=sys.argv

# print(image_path)

# img = Image.open(image_path).convert('L')#灰度图(L)

# img_shape = np.reshape(img, 784)

# real_x = np.array()# 0-255 uint8 8位无符号整数，取值： 如果采用1-大数变成小数

# y = sess.run(y_conv, feed_dict={x: real_x,keep_prob: 1.0}) #y类似一个二维表，因为只有一张图片所以只有一行，y包含10个值，

#

# print('Predict digit', np.argmax(y))#找出最大的值

# 测试集图片

images_dir = 'test_images'

images_path = glob.glob(images_dir + "/*")

for image_path in images_path:

img = Image.open(image_path).convert('L')#灰度图(L)

img_shape = np.reshape(img, 784)

real_x = np.array()# 0-255 uint8 8位无符号整数，取值： 如果采用1-大数变成小数

y = sess.run(y_conv, feed_dict={x: real_x,keep_prob: 1.0}) #y类似一个二维表，因为只有一张图片所以只有一行，y包含10个值，

imagePath:test_images0_0.bmp,Predict digit:0

imagePath:test_images1_0.bmp,Predict digit:3

imagePath:test_images2_0.bmp,Predict digit:2

imagePath:test_images3_0.bmp,Predict digit:8

imagePath:test_images4_0.bmp,Predict digit:4

imagePath:test_images5_0.bmp,Predict digit:5