基于Jupyter Notebook---卷积神经网络的图像分类（keras对猫狗图像数据集进行分类）
keras对猫狗图像数据集进行分类
- 一、安装Keras
- 二、keras对猫狗图像数据集进行分类
- 三、全部代码
- 四、结果显示
一、安装Keras
在windows下，首先添加中科大源
命令行中直接使用以下命令
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/ conda config --set show_channel_urls yes
Anaconda3如何安装keras，主要包括下面三步：
1.安装mingw libpython
2.安装theano
3.安装keras
若你还没有按装anaconda，赶紧下载安装吧！若已安装好，进入win菜单打开Anaconda prompt，如下图：
输入conda install mingw libpython回车，然后输入y回车，
输入conda install theano回车，然后输入y回车，
输入conda install keras回车，然后输入y回车。
二、keras对猫狗图像数据集进行分类
猫狗数据集下载链接：cats_and_dogs_small_train
导入keras所需用到的库函数，读取本地猫狗数据集，搭建模型。
import os
import shutil # 复制文件
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from keras.preprocessing import image
# 原始目录所在的路径
# 数据集未压缩
original_dataset_dir = 'cats_and_dogs_small_train'
# 存储较小数据集的目录
base_dir = 'cats_and_dogs_small_test'
os.mkdir(base_dir)
# 训练、验证、测试数据集的目录
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
# 猫训练图片所在目录
train_cats_dir = os.path.join(train_dir, 'cats')
os.mkdir(train_cats_dir)
# 狗训练图片所在目录
train_dogs_dir = os.path.join(train_dir, 'dogs')
os.mkdir(train_dogs_dir)
# 猫验证图片所在目录
validation_cats_dir = os.path.join(validation_dir, 'cats')
os.mkdir(validation_cats_dir)
# 狗验证数据集所在目录
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
os.mkdir(validation_dogs_dir)
# 猫测试数据集所在目录
test_cats_dir = os.path.join(test_dir, 'cats')
os.mkdir(test_cats_dir)
# 狗测试数据集所在目录
test_dogs_dir = os.path.join(test_dir, 'dogs')
os.mkdir(test_dogs_dir)
# 复制最开始的1000张猫图片到 train_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张猫图片到 validation_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张图片到 test_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制最开始的1000张狗图片到 train_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张狗图片到 validation_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张狗图片到 test_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
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)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))
# 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'])
数据应该在被送入我们的网络之前格式化为适当的预处理浮点张量。
目前，我们的数据作为JPEG文件位于驱动器上，因此将其放入网络的步骤大致如下：
*阅读图片文件。
*将JPEG内容解码为RBG像素网格。
*将这些转换为浮点张量。
*将像素值（0到255之间）重新缩放到[0,1]间隔（如您所知，神经网络更喜欢处理小输入值）。
这可能看起来有点令人生畏，但幸好Keras有实用工具自动处理这些步骤。
Keras有一个带有图像处理辅助工具的模块，位于keras.preprocessing.image。
特别是，它包含类ImageDataGenerator，它允许快速设置Python生成器，
可以自动将磁盘上的图像文件转换为批处理的预处理张量。
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=30,
validation_data=validation_generator,
validation_steps=50
)
model.save('cats_and_dogs_small_1.h5')
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()
‘’’
因为我们只有相对较少的训练样本（2000），过度拟合将是我们的头号问题。您已经了解了许多有助于缓解过度拟合的技术，
例如丢失和重量衰减（L2正规化）。我们现在将介绍一种新的，专门针对计算机视觉的，并且在使用深度学习模型处理图像时几乎普遍使用：数据增强。
###使用数据增强
＃过度拟合是由于样本太少而无法学习，导致我们无法训练能够推广到新数据的模型。
＃给定无限数据，我们的模型将暴露于手头数据分布的每个可能方面：我们永远不会过度拟合。
数据增强采用从现有训练样本生成更多训练数据的方法，通过数字“扩充”样本
随机转换的数量，产生可信的图像。目标是在训练时，我们的模型永远不会看到两次完全相同的图片。
这有助于模型暴露于数据的更多方面并更好地概括。
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)
# 查看数据增强的效果
frames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]
# 选择一张图片来做增强
img_path = fnames[3]
# 读取图片并进行resize
img = image.load_img(img_path, target_size=(150, 150))
# 转化为Numpy数组， shape(150, 150, 3)
x = image.img_to_array(img)
# reshape->(1, 150, 150, 3)
x = x.reshape(1, 150, 150, 3)
i = 0
for batch in datagen.flow(x, batch_size=1):
plt.figure(i)
imgplot = plt.imshow(image.array_to_img(batch[0]))
i += 1
if i % 4 == 0:
break
plt.show()
# 使用数据增强后的数据来训练一个新的网络
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(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=1e-4),
metrics=['acc'])
train_datagen = ImageDataGenerator(
rescale=1./ 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary'
)
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary'
)
hist = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=100,
validation_data=validation_generator,
validation_steps=50,
)
model.save('cats_and_dogs_small_2.h5')
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.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
三、全部代码
import os
import shutil # 复制文件
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from keras.preprocessing import image
# 原始目录所在的路径
# 数据集未压缩
original_dataset_dir = 'cats_and_dogs_small_train'
# 存储较小数据集的目录
base_dir = 'cats_and_dogs_small_test'
os.mkdir(base_dir)
# 训练、验证、测试数据集的目录
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
# 猫训练图片所在目录
train_cats_dir = os.path.join(train_dir, 'cats')
os.mkdir(train_cats_dir)
# 狗训练图片所在目录
train_dogs_dir = os.path.join(train_dir, 'dogs')
os.mkdir(train_dogs_dir)
# 猫验证图片所在目录
validation_cats_dir = os.path.join(validation_dir, 'cats')
os.mkdir(validation_cats_dir)
# 狗验证数据集所在目录
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
os.mkdir(validation_dogs_dir)
# 猫测试数据集所在目录
test_cats_dir = os.path.join(test_dir, 'cats')
os.mkdir(test_cats_dir)
# 狗测试数据集所在目录
test_dogs_dir = os.path.join(test_dir, 'dogs')
os.mkdir(test_dogs_dir)
# 复制最开始的1000张猫图片到 train_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张猫图片到 validation_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张图片到 test_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制最开始的1000张狗图片到 train_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张狗图片到 validation_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
# 复制接下来500张狗图片到 test_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
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)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))
# 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'])
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=30,
validation_data=validation_generator,
validation_steps=50
)
model.save('cats_and_dogs_small_1.h5')
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()
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest'
)
# 查看数据增强的效果
frames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]
# 选择一张图片来做增强
img_path = fnames[3]
# 读取图片并进行resize
img = image.load_img(img_path, target_size=(150, 150))
# 转化为Numpy数组， shape(150, 150, 3)
x = image.img_to_array(img)
# reshape->(1, 150, 150, 3)
x = x.reshape(1, 150, 150, 3)
i = 0
for batch in datagen.flow(x, batch_size=1):
plt.figure(i)
imgplot = plt.imshow(image.array_to_img(batch[0]))
i += 1
if i % 4 == 0:
break
plt.show()
# 使用数据增强后的数据来训练一个新的网络
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(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=1e-4),
metrics=['acc'])
train_datagen = ImageDataGenerator(
rescale=1./ 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary'
)
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary'
)
hist = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=100,
validation_data=validation_generator,
validation_steps=50,
)
model.save('cats_and_dogs_small_2.h5')
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.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
四、结果显示
在命令行会显示运行结果，如下图。
同时会在生产本地文件
