Tensorflow 2 （CNN）

tensorflow卷积神经网络模型

第一步、构造并训练模型

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

mnist = input_data.read_data_sets(‘MNIST_data’, one_hot=True) #MNIST数据集所在路径

#输入
x = tf.placeholder(tf.float32, [None, 784])
#输出
y_ = tf.placeholder(tf.float32, [None, 10])

#权重
def weight_variable(shape):
initial = tf.truncated_normal(shape,stddev = 0.1)
return tf.Variable(initial)
#偏置
def bias_variable(shape):
initial = tf.constant(0.1,shape = shape)
return tf.Variable(initial)
#卷积
def conv2d(x,W):
return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = ‘SAME’)
#池化
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, 7764])
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(5000):
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, ‘MNIST_CNN/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}))

第二步、测试模型

from PIL import Image, ImageFilter
import tensorflow as tf
import matplotlib.pyplot as plt

def imageprepare():
im = Image.open(‘5.png’) #读取的图片所在路径，注意是28*28像素
plt.imshow(im) #显示需要识别的图片
plt.show()
im = im.convert(‘L’)
tv = list(im.getdata())
tva = [(255-x)*1.0/255.0 for x in tv]
return tva

result=imageprepare()
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’)