1000字范文 > 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）

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）

时间：2019-01-21 10:44:27

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./anaconda/pkgs/main/conda config --add channels https://mirrors./anaconda/pkgs/free/conda config --add channels https://mirrors./anaconda/cloud/conda-forge/conda config --add channels https://mirrors./anaconda/cloud/msys2/conda config --add channels https://mirrors./anaconda/cloud/bioconda/conda config --add channels https://mirrors./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。

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 directorytarget_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)breakhist = 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resizeimg = 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 = 0for batch in datagen.flow(x, batch_size=1):plt.figure(i)imgplot = plt.imshow(image.array_to_img(batch[0]))i += 1if i % 4 == 0:breakplt.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'))pile(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 osimport shutil # 复制文件from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropoutfrom keras.optimizers import RMSpropfrom keras.preprocessing.image import ImageDataGeneratorimport matplotlib.pyplot as pltfrom 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_dirfnames = ['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_dirfnames = ['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_dirfnames = ['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_dirfnames = ['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_dirfnames = ['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_dirfnames = ['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())pile(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 directorytarget_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)breakhist = 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resizeimg = 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 = 0for batch in datagen.flow(x, batch_size=1):plt.figure(i)imgplot = plt.imshow(image.array_to_img(batch[0]))i += 1if i % 4 == 0:breakplt.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'))pile(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()