目录
一：数据集准备
从官网下载比较麻烦，可根据以下链接，从百度网盘获取数据集
https://pan.baidu.com/s/13hw4LK8ihR6-6-8mpjLKDA
密码：dmp4
猫狗图像识别数据集 如下
二：读取自己的数据集
-
import os
-
import cv2
-
import numpy
as np
-
from tqdm
import tqdm
# 进度条
-
from random
import shuffle
# 随机打乱
-
-
IMAGE_SIZE =
50
-
-
-
def
label_img(
img_name):
-
label_name = img_name.split(
'.')[
0]
-
# print(label_name)
-
if label_name ==
'cat':
-
return [
1,
0]
-
elif label_name ==
'dog':
-
return [
0,
1]
-
return []
-
-
-
def
create_train_data(
dir_path):
-
train_data = []
-
# 遍历目录获取每一张图片的路径
-
for img_path
in tqdm(os.listdir(dir_path)):
-
# print(img_path)
-
label = label_img(img_path)
-
if
len(label) !=
0:
-
img_path = os.path.join(dir_path, img_path)
-
# 单通道
-
img_mat = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
-
# print(img_mat, type(img_mat), img_mat.shape)
-
if img_mat
is
not
None:
-
img = cv2.resize(img_mat, (IMAGE_SIZE, IMAGE_SIZE))
-
# cv2.imshow("resize", img)
-
# cv2.waitKey(0)
-
# cv2.destoryAllWindows()
-
train_data.append([np.array(img), np.array(label)])
-
shuffle(train_data)
-
np.save(
'mark_catDog_TrainData.npy', train_data)
-
-
-
create_train_data(
"./train/train")
等待加载........
直至加载完成100%
三：搭建网络 训练模型
1 导入库
-
import tflearn
-
import numpy
as np
-
from tflearn.layers.estimator
import regression
-
from tflearn.layers.conv
import conv_2d, max_pool_2d
-
from tflearn.layers.core
import input_data, fully_connected, dropout
2 搭建网络
-
# 搭建网络 卷积 激活 池化 全连
-
# 输入层
-
conv_input = input_data([
None, IMAGE_SIZE, IMAGE_SIZE,
1], name=
'input')
-
# 第一层卷积
-
conv1 = conv_2d(conv_input,
32,
5, activation=
'relu')
-
conv1 = max_pool_2d(conv1,
2)
-
# 第二层网络
-
conv2 = conv_2d(conv1,
64,
5, activation=
'relu')
-
conv2 = max_pool_2d(conv2,
2)
-
# 第三层网络
-
conv3 = conv_2d(conv2,
1024,
5, activation=
'relu')
-
conv3 = max_pool_2d(conv3,
2)
-
# 全连层1
-
fully_layer1 = fully_connected(conv3,
1024, activation=
'relu')
-
# 防止过拟合
-
fully_layer1 = dropout(fully_layer1,
0.6)
-
# 全连层2
-
fully_layer2 = fully_connected(fully_layer1,
2, activation=
'softmax')
-
# 设置损失函数和优化器
-
model_net = regression(fully_layer2, optimizer=
'adam', learning_rate=
0.0005, loss=
'categorical_crossentropy',
-
name=
'model_net')
3 数据集划分
-
# 创建模型
-
model = tflearn.DNN(model_net, tensorboard_dir=
'log')
-
-
# 加载数据npy文件
-
train_data = np.load(
'mark_catDog_TrainData.npy', allow_pickle=
True)
-
print(train_data,
len(train_data),
type(train_data))
-
# 数据划分
-
train = train_data[:-
30]
-
test = train_data[-
30:]
-
-
X = np.array([i[
0]
for i
in train]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
Y = [i[
1]
for i
in train]
-
x_test = np.array([i[
0]
for i
in test]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
y_test = [i[
1]
for i
in test]
4 模型训练及保存
-
# 模型训练
-
model.fit({
'input': X},
-
{
'model_net': Y},
-
n_epoch=
2,
-
validation_set=({
'input': x_test}, {
'model_net': y_test}),
-
snapshot_step=
10,
-
show_metric=
True,
-
run_id=
"model_class"
-
)
-
-
# 模型保存
-
model.save(
"model/cat-dog.model")
训练时间较长，需要耐心等待
直至加载完毕，将模型保存
训练模型 完整源码：
-
import tflearn
-
import numpy
as np
-
from tflearn.layers.estimator
import regression
-
from tflearn.layers.conv
import conv_2d, max_pool_2d
-
from tflearn.layers.core
import input_data, fully_connected, dropout
-
-
IMAGE_SIZE =
50
-
-
# 搭建网络 卷积 激活 池化 全连
-
# 输入层
-
conv_input = input_data([
None, IMAGE_SIZE, IMAGE_SIZE,
1], name=
'input')
-
# 第一层卷积
-
conv1 = conv_2d(conv_input,
32,
5, activation=
'relu')
-
conv1 = max_pool_2d(conv1,
2)
-
# 第二层网络
-
conv2 = conv_2d(conv1,
64,
5, activation=
'relu')
-
conv2 = max_pool_2d(conv2,
2)
-
# 第三层网络
-
conv3 = conv_2d(conv2,
1024,
5, activation=
'relu')
-
conv3 = max_pool_2d(conv3,
2)
-
# 全连层1
-
fully_layer1 = fully_connected(conv3,
1024, activation=
'relu')
-
# 防止过拟合
-
fully_layer1 = dropout(fully_layer1,
0.6)
-
# 全连层2
-
fully_layer2 = fully_connected(fully_layer1,
2, activation=
'softmax')
-
# 设置损失函数和优化器
-
model_net = regression(fully_layer2, optimizer=
'adam', learning_rate=
0.0001, loss=
'categorical_crossentropy',
-
name=
'model_net')
-
-
# 创建模型
-
model = tflearn.DNN(model_net, tensorboard_dir=
'log')
-
-
# 加载数据npy文件
-
train_data = np.load(
'mark_catDog_TrainData.npy', allow_pickle=
True)
-
print(train_data,
len(train_data),
type(train_data))
-
# 数据划分
-
train = train_data[:-
30]
-
test = train_data[-
30:]
-
-
X = np.array([i[
0]
for i
in train]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
Y = [i[
1]
for i
in train]
-
x_test = np.array([i[
0]
for i
in test]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
y_test = [i[
1]
for i
in test]
-
-
# 模型训练
-
model.fit({
'input': X},
-
{
'model_net': Y},
-
n_epoch=
2,
-
validation_set=({
'input': x_test}, {
'model_net': y_test}),
-
snapshot_step=
10,
-
show_metric=
True,
-
run_id=
"model_class"
-
)
-
-
# 模型保存
-
model.save(
"model/cat-dog.model")
四：猫狗图像识别
模型保存后，可以使用模型测试，看看猫狗识别的准确率如何
可以从数据集中下载图片至工程进行测试：
测试1
-
# 将检测的图片变成np array
-
def
classify(
img_path):
-
# 灰度读取
-
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
-
if img
is
not
None:
-
img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
-
data = img.reshape(-
1, IMAGE_SIZE, IMAGE_SIZE,
1)
-
return data
-
-
-
predict = model.predict(classify(
'dog.11392.jpg'))[
0]
-
print(predict)
-
max_index = np.argmax(predict)
-
if max_index ==
0:
-
print(
"猫")
-
elif max_index ==
1:
-
print(
"狗")
测试结果如下，识别为狗，识别正确
测试2
-
predict = model.predict(classify(
'cat.299.jpg'))[
0]
-
print(predict)
-
max_index = np.argmax(predict)
-
if max_index ==
0:
-
print(
"猫")
-
elif max_index ==
1:
-
print(
"狗")
测试结果如下，错将猫识别为狗，识别错误
经过多次测试，识别的准确率并不高，模型训练的不够好，需要调参优化，再多花时间训练
-
# 设置损失函数和优化器
-
model_net = regression(fully_layer2, optimizer=
'adam', learning_rate=
0.0005, loss=
'categorical_crossentropy',
-
name=
'model_net')
深度学习，一般学习率 learning_rate是设置为0.0001，
若是对这个猫狗识别比较感兴趣的，可以自己调参优化，多搭建几层网络，如果训练出比较好的模型可以告诉我一下，嘻嘻(可以理解为博主很懒)
如果对于使用模型不够熟悉的，可以参考以下源码(图片自行准备，图片名称记得修改)
-
import tflearn
-
import numpy
as np
-
import cv2
-
from tflearn.layers.estimator
import regression
-
from tflearn.layers.conv
import conv_2d, max_pool_2d
-
from tflearn.layers.core
import input_data, fully_connected, dropout
-
-
IMAGE_SIZE =
50
-
-
# 搭建网络 卷积 激活 池化 全连
-
# 输入层
-
conv_input = input_data([
None, IMAGE_SIZE, IMAGE_SIZE,
1], name=
'input')
-
# 第一层卷积
-
conv1 = conv_2d(conv_input,
32,
5, activation=
'relu')
-
conv1 = max_pool_2d(conv1,
2)
-
# 第二层网络
-
conv2 = conv_2d(conv1,
64,
5, activation=
'relu')
-
conv2 = max_pool_2d(conv2,
2)
-
# 第三层网络
-
conv3 = conv_2d(conv2,
1024,
5, activation=
'relu')
-
conv3 = max_pool_2d(conv3,
2)
-
# 全连层1
-
fully_layer1 = fully_connected(conv3,
1024, activation=
'relu')
-
# 防止过拟合
-
fully_layer1 = dropout(fully_layer1,
0.6)
-
# 全连层2
-
fully_layer2 = fully_connected(fully_layer1,
2, activation=
'softmax')
-
# 设置损失函数和优化器
-
model_net = regression(fully_layer2, optimizer=
'adam', learning_rate=
0.0005, loss=
'categorical_crossentropy',
-
name=
'model_net')
-
-
# 加载数据npy文件
-
train_data = np.load(
'mark_catDog_TrainData.npy', allow_pickle=
True)
-
print(train_data,
len(train_data),
type(train_data))
-
# 数据划分
-
train = train_data[:-
30]
-
test = train_data[-
30:]
-
-
X = np.array([i[
0]
for i
in train]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
Y = [i[
1]
for i
in train]
-
x_test = np.array([i[
0]
for i
in test]).reshape((-
1, IMAGE_SIZE, IMAGE_SIZE,
1))
-
y_test = [i[
1]
for i
in test]
-
-
# 创建模型
-
model = tflearn.DNN(model_net, tensorboard_dir=
'log')
-
# 加载模型
-
model.load(
"./model/cat-dog.model")
-
-
-
# 将检测的图片变成np array
-
def
classify(
img_path):
-
# 灰度读取
-
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
-
if img
is
not
None:
-
img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
-
data = img.reshape(-
1, IMAGE_SIZE, IMAGE_SIZE,
1)
-
return data
-
-
-
predict = model.predict(classify(
'cat.299.jpg'))[
0]
-
print(predict)
-
max_index = np.argmax(predict)
-
if max_index ==
0:
-
print(
"猫")
-
elif max_index ==
1:
-
print(
"狗")
转载：https://blog.csdn.net/m0_56051805/article/details/128420677
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