keras分类之二分类实例(Cat and dog)

更新时间：2020年07月09日 09:32:06 作者：mr_liyonghong

这篇文章主要介绍了keras分类之二分类实例(Cat and dog)，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

1. 数据准备

在文件夹下分别建立训练目录train，验证目录validation，测试目录test，每个目录下建立dogs和cats两个目录，在dogs和cats目录下分别放入拍摄的狗和猫的图片，图片的大小可以不一样。

2. 数据读取

# 存储数据集的目录
base_dir = 'E:/python learn/dog_and_cat/data/'
 
# 训练、验证数据集的目录
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
 
# 猫训练图片所在目录
train_cats_dir = os.path.join(train_dir, 'cats')
 
# 狗训练图片所在目录
train_dogs_dir = os.path.join(train_dir, 'dogs')
 
# 猫验证图片所在目录
validation_cats_dir = os.path.join(validation_dir, 'cats')
 
# 狗验证数据集所在目录
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
 
print('total training cat images:', len(os.listdir(train_cats_dir))) 
print('total training dog images:', len(os.listdir(train_dogs_dir))) 
print('total validation cat images:', len(os.listdir(validation_cats_dir))) 
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))

3. 模型建立

# 搭建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu',
         input_shape=(150, 150, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
 
print(model.summary())
 
model.compile(loss='binary_crossentropy',
       optimizer=RMSprop(lr=1e-4),
       metrics=['acc'])

4. 模型训练

train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
 
train_generator = train_datagen.flow_from_directory(
  train_dir, # target directory
  target_size=(150, 150), # resize图片
  batch_size=20,
  class_mode='binary'
)
 
validation_generator = test_datagen.flow_from_directory(
  validation_dir,
  target_size=(150, 150),
  batch_size=20,
  class_mode='binary'
)
 
for data_batch, labels_batch in train_generator:
  print('data batch shape:', data_batch.shape)
  print('labels batch shape:', labels_batch.shape)
  break
 
hist = model.fit_generator(
  train_generator,
  steps_per_epoch=100,
  epochs=10,
  validation_data=validation_generator,
  validation_steps=50
)
 
model.save('cats_and_dogs_small_1.h5')

5. 模型评估

acc = hist.history['acc']
val_acc = hist.history['val_acc']
loss = hist.history['loss']
val_loss = hist.history['val_loss']
 
epochs = range(len(acc))
 
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
 
plt.legend()
plt.figure()
 
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.legend()
plt.show()

6. 预测

imagename = 'E:/python learn/dog_and_cat/data/validation/dogs/dog.2026.jpg'
test_image = image.load_img(imagename, target_size = (150, 150))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = model.predict(test_image)
 
if result[0][0] == 1:
  prediction ='dog'
else:
  prediction ='cat'
  
print(prediction)

代码在spyder下运行正常，一般情况下，可以将文件分为两个部分，一部分为Train.py，包含深度学习模型建立、训练和模型的存储，另一部分Predict.py，包含模型的读取，评价和预测

补充知识：keras 猫狗大战自搭网络以及vgg16应用

导入模块

import os
import numpy as np
import tensorflow as tf
import random
import seaborn as sns
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input,BatchNormalization
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import RMSprop, Adam, SGD
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16, preprocess_input
 
from sklearn.model_selection import train_test_split

加载数据集

def read_and_process_image(data_dir,width=64, height=64, channels=3, preprocess=False):
  train_images= [data_dir + i for i in os.listdir(data_dir)]
  
  random.shuffle(train_images)
  
  def read_image(file_path, preprocess):
    img = image.load_img(file_path, target_size=(height, width))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    # if preprocess:
      # x = preprocess_input(x)
    return x
  
  def prep_data(images, proprocess):
    count = len(images)
    data = np.ndarray((count, height, width, channels), dtype = np.float32)
    
    for i, image_file in enumerate(images):
      image = read_image(image_file, preprocess)
      data[i] = image
    
    return data
  
  def read_labels(file_path):
    labels = []
    for i in file_path:
      label = 1 if 'dog' in i else 0
      labels.append(label)
    
    return labels
  
  X = prep_data(train_images, preprocess)
  labels = read_labels(train_images)
  
  assert X.shape[0] == len(labels)
  print("Train shape: {}".format(X.shape))
  return X, labels

读取数据集

# 读取图片
WIDTH = 150
HEIGHT = 150
CHANNELS = 3
X, y = read_and_process_image('D:\\Python_Project\\train\\',width=WIDTH, height=HEIGHT, channels=CHANNELS)

查看数据集信息

# 统计y
sns.countplot(y)
 
# 显示图片
def show_cats_and_dogs(X, idx):
  plt.figure(figsize=(10,5), frameon=True)
  img = X[idx,:,:,::-1]
  img = img/255
  plt.imshow(img)
  plt.show()
 
 
for idx in range(0,3):
  show_cats_and_dogs(X, idx)
 
train_X = X[0:17500,:,:,:]
train_y = y[0:17500]
test_X = X[17500:25000,:,:,:]
test_y = y[17500:25000]
train_X.shape
test_X.shape

自定义神经网络层数

input_layer = Input((WIDTH, HEIGHT, CHANNELS))
# 第一层
z = input_layer
z = Conv2D(64, (3,3))(z)
z = BatchNormalization()(z)
z = Activation('relu')(z)
z = MaxPooling2D(pool_size = (2,2))(z)
 
z = Conv2D(64, (3,3))(z)
z = BatchNormalization()(z)
z = Activation('relu')(z)
z = MaxPooling2D(pool_size = (2,2))(z)
 
z = Conv2D(128, (3,3))(z)
z = BatchNormalization()(z)
z = Activation('relu')(z)
z = MaxPooling2D(pool_size = (2,2))(z)
 
z = Conv2D(128, (3,3))(z)
z = BatchNormalization()(z)
z = Activation('relu')(z)
z = MaxPooling2D(pool_size = (2,2))(z)
 
z = Flatten()(z)
z = Dense(64)(z)
z = BatchNormalization()(z)
z = Activation('relu')(z)
z = Dropout(0.5)(z)
z = Dense(1)(z)
z = Activation('sigmoid')(z)
 
model = Model(input_layer, z)
 
model.compile(
  optimizer = keras.optimizers.RMSprop(),
  loss = keras.losses.binary_crossentropy,
  metrics = [keras.metrics.binary_accuracy]
)
 
model.summary()

训练模型

history = model.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=10,batch_size=128,verbose=True)
score = model.evaluate(test_X, test_y, verbose=0)
print("Large CNN Error: %.2f%%" %(100-score[1]*100))

复用vgg16模型

def vgg16_model(input_shape= (HEIGHT,WIDTH,CHANNELS)):
  vgg16 = VGG16(include_top=False, weights='imagenet',input_shape=input_shape)
  
  for layer in vgg16.layers:
    layer.trainable = False
  last = vgg16.output
  # 后面加入自己的模型
  x = Flatten()(last)
  x = Dense(256, activation='relu')(x)
  x = Dropout(0.5)(x)
  x = Dense(256, activation='relu')(x)
  x = Dropout(0.5)(x)
  x = Dense(1, activation='sigmoid')(x)
  
  model = Model(inputs=vgg16.input, outputs=x)
  
  return model

编译模型

model_vgg16 = vgg16_model()
model_vgg16.summary()
model_vgg16.compile(loss='binary_crossentropy',optimizer = Adam(0.0001), metrics = ['accuracy'])

训练模型

# 训练模型
history = model_vgg16.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=5,batch_size=128,verbose=True)
score = model_vgg16.evaluate(test_X, test_y, verbose=0)
print("Large CNN Error: %.2f%%" %(100-score[1]*100))

以上这篇keras分类之二分类实例(Cat and dog)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。

您可能感兴趣的文章:

通过python检测字符串的字母
这篇文章主要介绍了通过python检测字符串的字母,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
2020-02-02
Pytorch中torch.unsqueeze()与torch.squeeze()函数详细解析
torch.squeeze()这个函数主要对数据的维度进行压缩,去掉维数为1的的维度,下面这篇文章主要给大家介绍了关于Pytorch中torch.unsqueeze()与torch.squeeze()函数详细的相关资料,需要的朋友可以参考下
2023-02-02
Python实现图像手绘效果的方法详解
这篇文章主要为大家详细介绍了如何利用Python语言实现图像手绘效果，文中的示例代码讲解详细，具有一定的借鉴价值，需要的可以参考一下
2022-09-09
OpenCV读取与写入图片的实现
这篇文章主要介绍了OpenCV读取与写入图片的实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
2020-10-10
使用Python实现正态分布、正态分布采样
今天小编就为大家分享一篇使用Python实现正态分布、正态分布采样，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
2019-11-11
以视频爬取实例讲解Python爬虫神器Beautiful Soup用法
这篇文章主要以视频爬取实例来讲解Python爬虫神器Beautiful Soup的用法,Beautiful Soup是一个为Python获取数据而设计的包,简洁而强大,需要的朋友可以参考下
2016-01-01
Python找出列表中出现次数最多的元素三种方式
本文通过三种方式给大家介绍Python找出列表中出现次数最多的元素，每种方式通过实例代码给大家介绍的非常详细，具有一定的参考借鉴价值，需要的朋友参考下
2020-02-02
详解python代码模块化
今天给大家带来的是关于Python的相关知识,文章围绕着python代码模块化展开,文中有非常详细的介绍及代码示例,需要的朋友可以参考下
2021-06-06
python中将字典形式的数据循环插入Excel
这篇文章主要介绍了python中将字典形式的数据循环插入Excel的方法,需要的朋友可以参考下
2018-01-01
一文详细介绍PyQt5 QPushButton() 的作用
通过本文的介绍,相信你已经对PyQt5中的QPushButton控件有了深入的了解,从基础介绍到常用属性和方法,再到应用场景和样式定制,本文为你提供了全面的指南,感兴趣的朋友跟随小编一起看看吧
2024-08-08

keras分类之二分类实例(Cat and dog)

相关文章

最新评论

大家感兴趣的内容

最近更新的内容

常用在线小工具