tensorflow+python flask进行手写识别_TensorFlow与Flask结合打造手写体数字识别（一）...

TensorFlow框架介绍：

TensorFlow是谷歌基于DistBelief(第一代人工智能学习系统)研发的第二代人工智能学习系统。

AlphaGo就是基于TensorFlow研发的。

TensorFlow支持CNN、RNN 和 LSTM算法。

TensorFlow 从1.0升级为2.0后做了一些改变，具体可查看tensorflow的官方文档:

MNIST数据集介绍：

MNIST数据集是一个存放手写数字(0-9)的数据库。

该数据集中有70000张训练图像(60000张训练图像和10000张的测试图像)

展现形式：

MNIST数据集模型搭建：

pycharm新建项目minist_testdemo (conda环境)

新建mnist文件夹

mnist文件夹下新建MNIST_data文件夹和data文件夹

将下载的四个包直接放入MNIST_data中(不用解压)

导入训练数据：

mnist文件夹下新建input_data.py

from __future__import absolute_import

from __future__import division

from __future__import print_function

import gzip

import os

import tempfile

import numpy

from six.movesimport urllib

from six.movesimport xrange

import tensorflow.compat.v1as tf

tf.disable_v2_behavior()

#导入mnist数据集

from tensorflow.keras.datasets.mnistimport load_data

定义训练模型：

mnist文件夹下新建model.py,定义线性模型和卷积模型

import tensorflow.compat.v1as tf

tf.disable_v2_behavior()

#定义线性模型

#Y=W*x+b

def regression(x):

#784*10的图像

W = tf.Variable(tf.zeros([784, 10]), name="W")

#维度为10

b = tf.Variable(tf.zeros([10]), name="b")

#y=wx+b

y = tf.nn.softmax(tf.matmul(x, W) + b)

return y, [W, b]

#定义卷积模型

#多重卷积

def convolutional(x, keep_prob):

#定义卷积层2*2

def conv2d(x, W):

return tf.nn.conv2d(x, W, [1, 1, 1, 1], padding='SAME')

#定义2*2池化层

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

#定义权重变量

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)

#定义卷积重，图像张量

#第一重

x_image = tf.reshape(x, [-1, 28, 28, 1])

W_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

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

h_pool1 = max_pool_2x2(h_conv1)

#第二重

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)

#full connection

#全连接层

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)

#防止过拟合

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

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

return y, [W_conv1, b_conv1, W_conv2, b_conv2, W_fc1, b_fc1, W_fc2, b_fc2]

使用线性模型进行训练：

mnist文件夹下新建regression.py

import os

import input_data

import model

import tensorflow.compat.v1as tf

tf.disable_v2_behavior()

data = input_data.load_data('MNIST_data')

# create model

# 引入参数

with tf.variable_scope("regression"):

x = tf.placeholder(tf.float32, [None, 784])

y, variables = model.regression(x)

# train

y_ = tf.placeholder("float", [None, 10])

# 交叉熵

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

# 训练步骤

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# 预测

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

# 计算准确率

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 保存参数

saver = tf.train.Saver(variables)

# 开始训练

with tf.Session()as sess:

# 将所有参数进行全局初始化放进来

sess.run(tf.global_variables_initializer())

# 训练1000次

for _in range(1000):

batch_xs, batch_ys = data.train.next_batch(100)

# feed_dict:往里放参数

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

# 打印准确度和测试数据集的images,labels

print((sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels})))

# 保存模型

path = saver.save(

sess, os.path.join(os.path.dirname(__file__), 'data', 'regression.ckpt'),

write_meta_graph=False, write_state=False)

#打印模型的保存路径

print("Saved:", path)

使用卷积模型进行训练

mnist文件夹下新建convolutional.py

import os

import model

import tensorflow.compat.v1as tf

import input_data

tf.disable_v2_behavior()

#导入数据集

data = input_data.load_data('MNIST_data')

#定义model

with tf.variable_scope("convolutional"):

x = tf.placeholder(tf.float32, [None, 784], name='x')

keep_prob = tf.placeholder(tf.float32)

y, variables = model.convolutional(x, keep_prob)

#train

#张量大小为[none,10]

y_ = tf.placeholder(tf.float32, [None, 10], name='y')

#交叉熵

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

#训练步长，使用随机梯度下降

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

#预测

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))

#准确率

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#保存参数

saver = tf.train.Saver(variables)

#进行训练

with tf.Session()as sess:

#回归参数

merged_summary_op = tf.summary.merge_all()

#

summay_writer = tf.summary.FileWriter('/tmp/mnist_log/1', sess.graph)

#把图加进来

summay_writer.add_graph(sess.graph)

sess.run(tf.global_variables_initializer())

#循环20000次

#训练数据

for iin range(20000):

batch = data.train.next_batch(50)

#每隔100次打印一次准确率

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))

sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob:0.5})

#测试数据

print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels, keep_prob:1.0}))

#保存所有参数，路径

path = saver.save(

sess, os.path.join(os.path.dirname(__file__), 'data', 'convalutional.ckpt'),

write_meta_graph=False, write_state=False)

print("Saved:", path)