2018/8.30mnist数据集模型训练及测试

参考博客链接：https://blog.csdn.net/qq_38269418/article/details/78991649

https://www.cnblogs.com/houjun/p/9016837.html

1.下载mnisit数据集，下载完成后不要解压，

input_data.read_data_sets函数读取的就是压缩文件

2.训练模型，代码如下

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

mnist = input_data.read_data_sets('F:\MNIST_data', one_hot=True) #MNIST数据集所在路径

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

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


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)

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

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

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

h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

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)

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)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

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.AdamOptimizer(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"))

saver = tf.train.Saver() #定义saver

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        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))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    saver.save(sess, 'C:/Users/81286/Desktop/SAVE1/model.ckpt') #模型储存位置

    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

3.制作测试图片，要求像素28*28的灰度图，图片格式为png

。目前还不会图像处理，采用将mnist数据集一维数组转变为二维图片的方法获得测试图片，代码如下

from tensorflow.examples.tutorials.mnist import input_data
import scipy.misc
import os

# 读取MNIST数据集。如果不存在会事先下载。
mnist = input_data.read_data_sets('F:\MNIST_data', one_hot=True)

# 我们把原始图片保存在MNIST_data/raw/文件夹下
# 如果没有这个文件夹会自动创建
save_dir = 'F:/MNIST_data/raw'
if os.path.exists(save_dir) is False:
    os.makedirs(save_dir)

# 保存前20张图片
for i in range(20):
    # 请注意，mnist.train.images[i, :]就表示第i张图片（序号从0开始）
    image_array = mnist.train.images[i, :]
    # TensorFlow中的MNIST图片是一个784维的向量，我们重新把它还原为28x28维的图像。
    image_array = image_array.reshape(28, 28)
    # 保存文件的格式为 mnist_train_0.jpg, mnist_train_1.jpg, ... ,mnist_train_19.jpg
    filename = save_dir + 'mnist_train_%d.png' % i
    # 将image_array保存为图片
    # 先用scipy.misc.toimage转换为图像，再调用save直接保存。
    scipy.misc.toimage(image_array, cmin=0.0, cmax=1.0).save(filename)

print('Please check: %s ' % save_dir)

4，将图片输入训练好的模型，测试识别准确度，代码如下：

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

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)

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

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

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

h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

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)

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)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

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.AdamOptimizer(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"))

saver = tf.train.Saver()


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver.restore(sess, "C:/Users/81286/Desktop/SAVE1/model.ckpt") #使用模型，参数和之前的代码保持一致

    prediction=tf.argmax(y_conv,1)
    predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)
    print(h_conv2)
    print('识别结果:')
    print(predint[0])

5.识别结果：

6.有些图片识别度很低，模型整体识别度在80%左右，没有其他大佬们输的那么高。以后再找原因吧

2018/8.30mnist数据集模型训练及测试

猜你喜欢