吴裕雄 python 神经网络——TensorFlow 卷积神经网络水果图片识别
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互联网集市收集整理的这篇技术教程文章主要介绍了吴裕雄 python 神经网络——TensorFlow 卷积神经网络水果图片识别，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含8114字，纯文字阅读大概需要12分钟。
内容图文
#-*- coding:utf-8 -*- import time import keras import skimage import numpy as np import tensorflow as tf import matplotlib.image as img from scipy import ndimage from skimage import color, data, transform %matplotlib inline
#设置文件目录 Training = r'F:\\data\\fruits-360\\Training' Test = r'F:\\data\\fruits-360\\Test'
import os
from natsort import natsorted
#获取每类水果中的第五张图像
def load_print_img(root):
print_img = []
print_label = []
#遍历水果种类目录
for i in range(len(os.listdir(root))):
child1 = os.listdir(root)[i]
child2 = os.listdir(os.path.join(root, child1))
#对第二层目录进行自然数排序，os.listder默认为str排序
child2 = natsorted(child2)
#取出每类的第五张图像
path = os.path.join(root, child1, child2[4])
if(path.endswith('.jpg')):
print_img.append(skimage.data.imread(path))
print_label.append(child1)
return print_img, print_label
print_img, print_label = load_print_img(Test)
print(np.shape(print_img))
print(np.shape(print_label))
print(print_label)
print(print_img)
import matplotlib.pyplot as plt
from IPython.core.pylabtools import figsize
#打印每类水果的第五张图像
def print_fruit(print_img, print_label, size) :
plt.figure(figsize(size, size))
for i in range(len(print_img)):
#图像输出格式为11行10列
plt.subplot(11, 10,(i+1))
#打印图像
plt.imshow(print_img[i])
#打印水果种类
plt.title(format(print_label[i]))
plt.axis('off')
plt.show()
print_img, print_label = load_print_img(Test)
#打印水果
print_fruit(print_img, print_label, 15)
import random
#随机获取水果种类
def get_random_fruits(root, n_classes):
#创建一个1到水果种类总数的list
fruits = []
for i in range(len(os.listdir(root))):
fruits.append(i)
#随机获取n_classes个随机不重复的水果种类
random_fruits = random.sample(fruits, n_classes)
return random_fruits
#获取随机抽取的10类水果的图像
def load(root, random_fruits):
#存放图像
image_data = []
#存放标签
image_label = []
#存放图像标签码
num_label = []
#遍历水果类型
for i in range(len(random_fruits)):
#第一层子目录（水果种类）
child1 = os.listdir(root)[i]
#第二层子目录（水果图像）
child2 = os.listdir(os.path.join(root, child1))
#对第二层目录进行自然数排序，os.listder默认为str排序
child2 = natsorted(child2)
#遍历水果图像
for j in range(len(child2)):
#结合第一二层子目录
path = os.path.join(root, child1, child2[j])
#只读取'.jpg'文件（文件后缀是否为'.jpg'）
if(path.endswith('.jpg')):
#把文件读取为图像存入image_data
image_data.append(skimage.data.imread(path))
#储存第一层子目录文件名（即水果名）
image_label.append(child1)
#把第一层子目录文件名的下标作为水果类型的编码
num_label.append(i)
#把水果类型编码转换为one_hot编码
num_label = keras.utils.to_categorical(num_label, len(random_fruits))
#print("图片数:{0}, 标签数:{1}".format(len(image_data), len(os.listdir(root))) #输出图片和标签数
return image_data, image_label, num_label
#裁剪图像
def crop(image_data):
crop_data = []
for i in image_data:
#把图像转换成32*32的格式
I_crop = skimage.transform.resize(i, (32, 32))
#把转换后的图像放入Icrop_data
crop_data.append(I_crop)
return crop_data
def fruits_type(random_fruits):
print('fruits_type:')
for i in random_fruits:
print(os.listdir(Training)[i])
#定义水果种类数
n_classes = 10
#batch_size = 256 #定义块的大小
#batch_num = int(np.array(crop_img).shape[0]/batch_size) #计算取块的次数
#申请四维占位符,数据类型为float32
x = tf.placeholder(tf.float32,[None, 32, 32, 3])
#申请二维占位符,数据累型为float32
y = tf.placeholder(tf.float32,[None, n_classes])
#申请一维占位符，数据类型为float32
keep_prob = tf.placeholder(tf.float32)
#epochs=2 #训练次数
#每个神经元保留的概率
dropout=0.75
#卷积核大小
k_size = 3
Weights = {
"conv_w1" : tf.Variable(tf.random_normal([k_size, k_size, 3, 64]), name = 'conv_w1'),
"conv_w2" : tf.Variable(tf.random_normal([k_size, k_size, 64, 128]), name = 'conv_w2'),
#"conv_w3" : tf.Variable(tf.random_normal([k_size, k_size, 256, 512]), name = 'conv_w3'), \
"den_w1" : tf.Variable(tf.random_normal([int(32*32/4/4*128), 1024]), name = 'dev_w1'),
"den_w2" : tf.Variable(tf.random_normal([1024, 512]), name = 'den_w2'),
"den_w3" : tf.Variable(tf.random_normal([512, n_classes]), name = 'den_w3')
}
bias = {
"conv_b1" : tf.Variable(tf.random_normal([64]), name = 'conv_b1'),
"conv_b2" : tf.Variable(tf.random_normal([128]), name = 'conv_b2'),
#"conv_b3" : tf.Variable(tf.random_normal([512]), name = 'conv_b3'), \
"den_b1" : tf.Variable(tf.random_normal([1024]), name = 'den_b1'),
"den_b2" : tf.Variable(tf.random_normal([512]), name = 'den_b2'),
"den_b3" : tf.Variable(tf.random_normal([n_classes]), name = 'den_b3')
}
def conv2d(x,W,b,stride=1):
x=tf.nn.conv2d(x,W,strides=[1,stride,stride,1],padding="SAME")
x=tf.nn.bias_add(x,b)
return tf.nn.relu(x)
def maxpool2d(x,stride=2):
return tf.nn.max_pool(x,ksize=[1,stride,stride,1],strides=[1,stride,stride,1],padding="SAME")
def conv_net(inputs, W, b, dropout):
## convolution layer 1
## 输入32*32*3的数据，输出16*16*64的数据
conv1 = conv2d(x, W["conv_w1"], b["conv_b1"])
conv1 = maxpool2d(conv1, 2)
tf.summary.histogram('ConvLayer1/Weights', W["conv_w1"])
tf.summary.histogram('ConvLayer1/bias', b["conv_b1"])
## convolution layer2
## 输入16*16*64的数据，输出8*8*128的数据
conv2 = conv2d(conv1, W["conv_w2"], b["conv_b2"])
conv2 = maxpool2d(conv2, 2)
tf.summary.histogram('ConvLayer2/Weights', W["conv_w2"])
tf.summary.histogram('ConvLayer2/bias', b["conv_b2"])
## convolution layer3
#conv3 = conv2d(conv2, W["conv_w3"], b["conv_b3"])
#conv3 = maxpool2d(conv3, 2)
#tf.summary.histogram('ConvLayer3/Weights', W["conv_w3"])
#tf.summary.histogram('ConvLayer3/bias', b["conv_b3"])
## flatten
## 把数据拉伸为长度为8*8*128的一维数据
flatten = tf.reshape(conv2,[-1, W["den_w1"].get_shape().as_list()[0]])
## dense layer1
## 输入8192*1的数据，输出1024*1的数据
den1 = tf.add(tf.matmul(flatten, W["den_w1"]), b["den_b1"])
den1 = tf.nn.relu(den1)
den1 = tf.nn.dropout(den1, dropout)
tf.summary.histogram('DenLayer1/Weights', W["den_w1"])
tf.summary.histogram('DenLayer1/bias', b["den_b1"])
## dense layer2
## 1024*1的数据，输出512*1的数据
den2 = tf.add(tf.matmul(den1, W["den_w2"]), b["den_b2"])
den2 = tf.nn.relu(den2)
den2 = tf.nn.dropout(den2, dropout)
tf.summary.histogram('DenLayer2/Weights', W["den_w2"])
tf.summary.histogram('DenLayer2/bias', b["den_b2"])
## out
## 512*1的数据，输出n_classes*1的数据
out = tf.add(tf.matmul(den2, W["den_w3"]), b["den_b3"])
tf.summary.histogram('DenLayer3/Weights', W["den_w3"])
tf.summary.histogram('DenLayer3/bias', b["den_b3"])
return out
pred=conv_net(x,Weights,bias,keep_prob)
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
tf.summary.histogram('loss', cost)
optimizer=tf.train.AdamOptimizer(0.01).minimize(cost)
correct_pred=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))
merged=tf.summary.merge_all()
def train_and_test(train_x, train_y, test_x, test_y, epochs, batch_size, times = 1) :
# 初始化全局变量
init=tf.global_variables_initializer()
start_time = time.time()
with tf.Session() as sess:
sess.run(init)
# 把需要可视化的参数写入可视化文件
writer=tf.summary.FileWriter('F:\\data\\fruits-360\\tensorboard\\Fruit_graph' + str(times), sess.graph)
for i in range(epochs):
batch_num = int(np.array(train_x).shape[0]/batch_size)
sum_cost = 0
sum_acc = 0
for j in range(batch_num):
batch_x = get_data(train_x, batch_size, j)
batch_y = get_data(train_y, batch_size, j)
sess.run(optimizer, feed_dict={x:batch_x,y:batch_y,keep_prob:0.75})
loss,acc = sess.run([cost,accuracy],feed_dict={x:batch_x,y:batch_y,keep_prob: 1.})
sum_cost += loss
sum_acc += acc
result=sess.run(merged,feed_dict={x:batch_x, y:batch_y, keep_prob:0.75})
writer.add_summary(result, i)
arg_cost = sum_cost/batch_num
arg_acc = sum_acc/batch_num
print("Epoch:", '%04d' % (i+1),"cost=", "{:.9f}".format(arg_cost),"Training accuracy","{:.5f}".format(arg_acc))
end_time = time.time()
print('Optimization Completed')
print('Testing Accuracy:',sess.run(accuracy,feed_dict={x:test_x, y:test_y,keep_prob: 1}))
print('Total processing time:',end_time - start_time)
for i in range(10):
random_fruits = get_random_fruits(Training, n_classes)
img_data, img_label, num_label = load(Training, random_fruits)
crop_img = crop(img_data)
test_data, test_label, test_num_label = load(Test, random_fruits)
crop_test = crop(test_data)
print("TIMES"+str(i+1))
fruits_type(random_fruits)
print("\n")
train_and_test(crop_img, num_label, crop_test, test_num_label, 20, 26, (i+1))
print("\n\n\n")
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