Tensor flow小案例——05手写数字识别2

from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 定义神经网络中的隐藏层
def add_layer(inputs, size_inputs, size_outputs, act_fun = None):
    Weight = tf.Variable(tf.random_normal([size_inputs, size_outputs]))
    biases = tf.Variable(tf.zeros([1, size_outputs]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weight) + biases
    if act_fun == None:
        output = Wx_plus_b
    else:
        output = act_fun(Wx_plus_b)
    return output

# 计算正确率
def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result

# 引入数据
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


# 定义输入变量
xs = tf.placeholder(tf.float32, [None, 784], name='x_in')
ys = tf.placeholder(tf.float32, [None, 10], name='y_in')

# 预测值
prediction = add_layer(xs, 784, 10, act_fun=tf.nn.softmax)

# 定义代价函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))

# 定义训练方法：梯度下降；  指定学习率：0.001（一定不要太大，否则会NAN）；  训练目的：最小化loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

#声明tf.train.Saver用于保存模型
saver = tf.train.Saver()

# 初始化全部变量
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

# 迭代5,000次
for i in range(5000):
    batch_xs, batch_ys = mnist.train.next_batch(50)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
    # sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    # 输出正确率
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images, mnist.test.labels))


#将模型保存为/tensorflow_google/model/model.ckpt
saver.save(sess, "./model/DigitalRrecognition/DigitalRrecognition.ckpt")

# 测试图片的路径
root1 = './Image/t1.png'
# 加载已经保存的模型，并通过已保存的模型中变量的值来计算加法
saver.restore(sess, "./model/DigitalRrecognition/DigitalRrecognition.ckpt")
# 将图片转为灰度图并规定大小为【28，28】
demo = Image.open(root1).convert('L').resize([28, 28])
# 将图片所表示的灰度矩阵形状改为【1，18*18】
demoArray = np.array(demo).reshape(1, 28 * 28)
# 将矩阵里的数化为0~1之间的数
demoArray = (255 - demoArray) / 255
# 输入矩阵，进行预测，得到结果
result = sess.run(prediction, feed_dict={xs: demoArray})
# 结果为一个矩阵，范围在0~1之间，第几个数最大则代表预测结果是多少
print(result)

sess.close()

t1.png:

输出结果：

0.0944
.

.
0.9091
0.9035
[[6.3137204e-12 5.0958712e-12 7.9769625e-06 3.3179897e-01 5.3250981e-16
6.6806221e-01 1.5467061e-09 1.1380750e-20 1.3088244e-04 1.4278804e-08]]

输出的矩阵中，从0开始数，第5个数6.6806221e-01最大，所以结果是5

但是总不能每次预测数字都要训练一个神经网络吧，我们只要把训练好的神经网络保存起来，需要调用的时候直接调用就好了。

上面的程序中


#将模型保存为/tensorflow_google/model/model.ckpt
saver.save(sess, "./model/DigitalRrecognition/DigitalRrecognition.ckpt")

这里就保存了训练好的神经网络，调用的时候需要声明神经网络中的变量。

调用实例如下：

from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 定义神经网络中的隐藏层
def add_layer(inputs, size_inputs, size_outputs, act_fun = None):
    Weight = tf.Variable(tf.random_normal([size_inputs, size_outputs]))
    biases = tf.Variable(tf.zeros([1, size_outputs]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weight) + biases
    if act_fun == None:
        output = Wx_plus_b
    else:
        output = act_fun(Wx_plus_b)
    return output

# 计算正确率
def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result

# 引入数据
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


# 定义输入变量
xs = tf.placeholder(tf.float32, [None, 784], name='x_in')
ys = tf.placeholder(tf.float32, [None, 10], name='y_in')

# 预测值
prediction = add_layer(xs, 784, 10, act_fun=tf.nn.softmax)

# 定义代价函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))

# 定义训练方法：梯度下降；  指定学习率：0.001（一定不要太大，否则会NAN）；  训练目的：最小化loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

#声明tf.train.Saver用于保存模型
saver = tf.train.Saver()

root = './Image/t1.png'

with tf.Session() as sess:
    #加载已经保存的模型，并通过已保存的模型中变量的值来计算加法
    saver.restore(sess, "./model/DigitalRrecognition/DigitalRrecognition.ckpt")
    demo = Image.open(root).convert('L').resize([28, 28])
    demoArray = np.array(demo).reshape(1, 28 * 28)
    demoArray = (255 - demoArray) / 255
    result = sess.run(prediction, feed_dict={xs: demoArray})
    print(result)

Tensor flow小案例——05手写数字识别2

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