美文网首页
图像分类实践及其优化

图像分类实践及其优化

作者: Byte猫 | 来源:发表于2019-06-11 15:10 被阅读0次

一、基础网络

#-*- coding: UTF-8 -*-
import numpy as np
from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# 加载MNIST数据集
from keras.datasets import mnist
(X_train, y_train),(X_test, y_test) = mnist.load_data()

# 数据集格式规范化(单通道图像)
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 标准化输入数据
X_train = X_train.astype('float32')/255.0
X_test = X_test.astype('float32')/255.0

# 对标签进行独热编码
n_classes = len(set(y_train))
y_train = to_categorical(y_train, n_classes)
y_test = to_categorical(y_test, n_classes)

# 定义网络结构
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(n_classes, activation='softmax'))

opt = Adam()
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# 设置网络超参数与回调函数
batch_size = 128
n_epochs = 11

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

# 训练模型
model.fit(X_train,
          y_train,
          batch_size=batch_size,
          epochs=n_epochs,
          validation_split=0.2,
          callbacks=callbacks,
          verbose=1
          )

# 保存参数
model.save_weights('result.model', overwrite=True)

# 在测试集上显示结果
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 模型预测
preds = model.predict(X_test)
n_examples = 10
for i in range(n_examples):
    ax = plt.subplot(2, n_examples, i+1)
    plt.imshow(X_test[i,:,:,0], cmap='gray')
    plt.title('label:{}\nPredicted: {}'.format(np.argmax(y_test[i]), np.argmax(preds[i])))
    plt.axis('off')
plt.show()

# 绘制10个错误分类的图像及其标签
plt.figure(figsize=(15,15))
j = 1
for i in range(len(y_test)):
    if(j==10):
        break
    label = np.argmax(y_test[i])
    pred = np.argmax(pred[i])
    if label != pred:
        ax = plt.subplot(2, n_examples, j+1)
        plt.imshow(X_test[i,:,:,0], cmap='gray')
        plt.title('label:{}\nPredicted: {}'.format(label, pred))
        plt.axis('off')
plt.show()

二、网络优化相关技术

1、层合并/池化技术

一种流行的CNN优化技术是池化。池化是一种用智能方法减少可训练参数的方法。两个最常用的池化技术是平均池化和最大池化。
池化技术有两大好处，一方面限制了网络的复杂性，以防止过拟合出现；另一方面将大大减少训练时间和推理时间。

#-*- coding: UTF-8 -*-
import numpy as np
from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint,EarlyStopping

# 加载MNIST数据集
from keras.datasets import mnist
(X_train, y_train),(X_test, y_test) = mnist.load_data()

# 数据集格式规范化(单通道图像)
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 标准化输入数据
X_train = X_train.astype('float32')/255.0
X_test = X_test.astype('float32')/255.0

# 对标签进行独热编码
n_classes = len(set(y_train))
y_train = to_categorical(y_train, n_classes)
y_test = to_categorical(y_test, n_classes)

# 定义网络结构
model = Sequential()
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(128, kernel_size=(3,3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(256, kernel_size=(3,3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

opt = Adam()
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# 设置网络超参数与回调函数
batch_size = 128
n_epochs = 1

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

# 训练模型
model.fit(X_train,
          y_train,
          batch_size=batch_size,
          epochs=n_epochs,
          validation_split=0.2,
          callbacks=callbacks,
          verbose=1
          )

# 保存参数
model.save_weights('result.model', overwrite=True)

# 在测试集上显示结果
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

在该方案中增加了对已实现的CNN添加池化层，同时增加卷积层中滤波器数量。

2、批量归一化

CNN的另一个众所周知的优化技术是批量归一化。该技术使得每批数据的输入分布对网络的影响较小，因此模型可以更好地泛化和更快地训练网络。

#-*- coding: UTF-8 -*-
import numpy as np
from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# 加载MNIST数据集
from keras.datasets import mnist
(X_train, y_train),(X_test, y_test) = mnist.load_data()

# 数据集格式规范化(单通道图像)
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 标准化输入数据
X_train = X_train.astype('float32')/255.0
X_test = X_test.astype('float32')/255.0

# 对标签进行独热编码
n_classes = len(set(y_train))
y_train = to_categorical(y_train, n_classes)
y_test = to_categorical(y_test, n_classes)

# 定义网络结构
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape, padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

opt = Adam()
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# 设置网络超参数与回调函数
batch_size = 128
n_epochs = 10

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

# 训练模型
model.fit(X_train,
          y_train,
          batch_size=batch_size,
          epochs=n_epochs,
          validation_split=0.2,
          callbacks=callbacks,
          verbose=1
          )

# 保存参数
model.save_weights('result.model', overwrite=True)

# 在测试集上显示结果
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

通常，具有批量归一化的模型在多个周期之后在验证精度上领先于没有批量归一化的模型，而且收敛更快（训练周期少）。

3、步长

当数据集在像素级别上含有较少的粒度信息时，可以尝试用更大的值作为步长。通过增加步长，卷积层在每个轴上跳过更多的输入变量，因此可以加速收敛，而不会有太大的性能损失。

#-*- coding: UTF-8 -*-
import numpy as np
from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# 加载MNIST数据集
from keras.datasets import mnist
(X_train, y_train),(X_test, y_test) = mnist.load_data()

# 数据集格式规范化(单通道图像)
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 标准化输入数据
X_train = X_train.astype('float32')/255.0
X_test = X_test.astype('float32')/255.0

# 对标签进行独热编码
n_classes = len(set(y_train))
y_train = to_categorical(y_train, n_classes)
y_test = to_categorical(y_test, n_classes)

# 定义网络结构
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape, padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2)))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

opt = Adam()
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# 设置网络超参数与回调函数
batch_size = 128
n_epochs = 10

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

# 训练模型
model.fit(X_train,
          y_train,
          batch_size=batch_size,
          epochs=n_epochs,
          validation_split=0.2,
          callbacks=callbacks,
          verbose=1
          )

# 保存参数
model.save_weights('result.model', overwrite=True)

# 在测试集上显示结果
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

4、初始化权值

对CNN来说，权重和偏置的初始化是非常重要的。通过选择正确的初始化可以加速网络的收敛。

#-*- coding: UTF-8 -*-
import numpy as np
from matplotlib import pyplot as plt

from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# 加载MNIST数据集
from keras.datasets import mnist
(X_train, y_train),(X_test, y_test) = mnist.load_data()

# 数据集格式规范化(单通道图像)
img_rows, img_cols = X_train[0].shape[0], X_train[0].shape[1]
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# 标准化输入数据
X_train = X_train.astype('float32')/255.0
X_test = X_test.astype('float32')/255.0

# 对标签进行独热编码
n_classes = len(set(y_train))
y_train = to_categorical(y_train, n_classes)
y_test = to_categorical(y_test, n_classes)

# 定义网络结构
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=input_shape, padding='same', kernel_initializer='glorot_uniform'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2), kernel_initializer='glorot_uniform'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2), kernel_initializer='glorot_uniform'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', strides=(2,2), kernel_initializer='glorot_uniform'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation='softmax'))

opt = Adam()
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# 设置网络超参数与回调函数
batch_size = 128
n_epochs = 10

callbacks = [EarlyStopping(monitor='val_acc', patience=5)]

# 训练模型
model.fit(X_train,
          y_train,
          batch_size=batch_size,
          epochs=n_epochs,
          validation_split=0.2,
          callbacks=callbacks,
          verbose=1
          )

# 保存参数
# model.save_weights('result.model', overwrite=True)

# 在测试集上显示结果
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

对于二维卷积层，Glorot均匀分布权重（也称Xavier均匀初始化）通常用作默认值。
了解更多初始化

相关文章

  • 图像分类实践及其优化

    一、基础网络 二、网络优化相关技术 1、层合并/池化技术 一种流行的CNN优化技术是池化。池化是一种用智能方法减少...

  • 图像分类实践

    启动Jupyter docker run -d -p 8888:8888 tensorflow/tensorflo...

  • Plant Seedlings Classification

    本教程作为一个植物幼苗分类的实践教程，有助于对图像分类整个实践过程有个清晰明了认识，加强研究实际图像问题的能力...

  • 计算机视觉(CV)

    质：工业级，企业级，设计。量：高效，优化。 核心功能：匹配定位，测量，图像检测，分类，条码识别 鲁棒图像算法 图像...

  • Paddlepaddle:一般任务最佳实践

    图像分类 1.优化器使用Adam opt = fluid.optimizer.Adam(learning_rate...

  • 图像分类

    图像分类入门 -图像分类的概念 背景与意义 所谓图像分类问题，就是已有固定的分类标签集合，然后对于输入的图像，从分...

  • python计算机视觉深度学习3图像分类基础

    什么是图像分类？ 图像分类的核心任务是从预定义的一类图像中为图像分配标签。分析输入图像并返回标签对图像进行分类。标...

  • python计算机视觉深度学习工具3图像分类基础

    什么是图像分类？ 图像分类的核心任务是从预定义的一类图像中为图像分配标签。分析输入图像并返回标签对图像进行分类。标...

  • 算法

    图像算法：图片特征提取图片质量评价图像分类打标签消重等技术研发 推荐算法：海量用户画像的构建及优化维护和改进文本挖...

  • 有效加载大位图

    注意：有几个库遵循加载图像的最佳实践。 您可以在应用中使用这些库以最优化的方式加载图像。 我们建议使用Glide库...

网友评论

      本文标题：图像分类实践及其优化

      本文链接：https://www.haomeiwen.com/subject/xruffctx.html

      延伸阅读

      深度阅读

      • 您也可以注册成为美文阅读网的作者，发表您的原创作品、分享您的心情！

      栏目导航

      热点阅读

      关于我们|服务条款|联系我们|图像分类实践及其优化|投稿指南|网站地图|RSS订阅|排版工具|手机版

      提供经典美文摘抄,优美散文欣赏,现代诗歌精选,短篇小说,心情随笔,表白情书范文,故事会在线阅读欣赏

      Copyright © 2014-2023 Haomeiwen.com All Rights Reserved. 好美文阅读网 版权所有

      备案信息：桂公网安备 45052102000051号 · 桂ICP备13007215号-3

      本站所收录作品、热点评论等信息部分来源互联网，目的只是为了系统归纳学习和传递资讯

      所有作品版权归原创作者所有，与本站立场无关，如不慎侵犯了你的权益，请联系我们告知，我们将做删除处理！