做g3云推广需要网站,吴江住房城乡建设局网站,书画展示网站源码,大型企业网站建设放假回家了#xff0c;感觉快坚持不下去了#xff0c;目前还没有找到关于无监督学习实现分类的课程#xff0c;普通数据当然肯定不会给你实现分类的啊 给些建议吧。
LeNet
通过共享卷积核#xff0c;减少网络参数。 一般只统计卷积计算层和全连接计算层#xff0c;其余操…放假回家了感觉快坚持不下去了目前还没有找到关于无监督学习实现分类的课程普通数据当然肯定不会给你实现分类的啊 给些建议吧。
LeNet
通过共享卷积核减少网络参数。 一般只统计卷积计算层和全连接计算层其余操作是卷积层的附属
LeNet有五层网络c1卷积c3卷积lenet没有BN操作没有D
Flatten拉直连续三层全连接网络前两层使用sigmod激活函数左后一层使用softmax。 class LeNer5(Model) def __init__(self): super.(LeNet5, eslf).__init__() # 6个5*5各卷积核激活函数sigmoid self.c1 Conv2D(filters6, kernel_size(5,5), activationsigmoid) #选择最大池化池化是2*2的尺寸步长为2 self.p1 MaxPool2D(poolsize(2, 2), strides2) self.c2 Conv2D(filters16, kernel_size(5, 5), activationsigmoid) self.p2 MaxPool2D(pool.size(2, 2), strides2) #拉直 self.flatten Flatten() # 全连接网络 self.f1 Dense(120, activationsigmod) self.f2 Dense(84, activationsigmoid) #使输出符合概率分布10个神经元 self.f3Dense(10, activationsoftmax)
AlexNet 使用Relu激活函数提升训练速度dropout缓解过拟合
8层第一层使用96个3*3个卷积核步长为1不适用填充使用BN操作实现特征标准化使用relu激活函数用3*3的池化核做最大池化步长为2 import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense
from tensorflow.keras import Modelnp.set_printoptions(thresholdnp.inf)cifar10 tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) cifar10.load_data()
x_train, x_test x_train / 255.0, x_test / 255.0class AlexNet8(Model):def __init__(self):super(AlexNet8, self).__init__()self.c1 Conv2D(filters96, kernel_size(3, 3))self.b1 BatchNormalization()self.a1 Activation(relu)self.p1 MaxPool2D(pool_size(3, 3), strides2)self.c2 Conv2D(filters256, kernel_size(3, 3))self.b2 BatchNormalization()self.a2 Activation(relu)self.p2 MaxPool2D(pool_size(3, 3), strides2)self.c3 Conv2D(filters384, kernel_size(3, 3), paddingsame,activationrelu)self.c4 Conv2D(filters384, kernel_size(3, 3), paddingsame,activationrelu)self.c5 Conv2D(filters256, kernel_size(3, 3), paddingsame,activationrelu)self.p3 MaxPool2D(pool_size(3, 3), strides2)self.flatten Flatten()self.f1 Dense(2048, activationrelu)self.d1 Dropout(0.5)self.f2 Dense(2048, activationrelu)self.d2 Dropout(0.5)self.f3 Dense(10, activationsoftmax)def call(self, x):x self.c1(x)x self.b1(x)x self.a1(x)x self.p1(x)x self.c2(x)x self.b2(x)x self.a2(x)x self.p2(x)x self.c3(x)x self.c4(x)x self.c5(x)x self.p3(x)x self.flatten(x)x self.f1(x)x self.d1(x)x self.f2(x)x self.d2(x)y self.f3(x)return ymodel AlexNet8()model.compile(optimizeradam,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsFalse),metrics[sparse_categorical_accuracy])checkpoint_save_path ./checkpoint/AlexNet8.ckpt
if os.path.exists(checkpoint_save_path .index):print(-------------load the model-----------------)model.load_weights(checkpoint_save_path)cp_callback tf.keras.callbacks.ModelCheckpoint(filepathcheckpoint_save_path,save_weights_onlyTrue,save_best_onlyTrue)history model.fit(x_train, y_train, batch_size32, epochs5, validation_data(x_test, y_test), validation_freq1,callbacks[cp_callback])
model.summary()# print(model.trainable_variables)
file open(./weights.txt, w)
for v in model.trainable_variables:file.write(str(v.name) \n)file.write(str(v.shape) \n)file.write(str(v.numpy()) \n)
file.close()############################################### show ################################################ 显示训练集和验证集的acc和loss曲线
acc history.history[sparse_categorical_accuracy]
val_acc history.history[val_sparse_categorical_accuracy]
loss history.history[loss]
val_loss history.history[val_loss]plt.subplot(1, 2, 1)
plt.plot(acc, labelTraining Accuracy)
plt.plot(val_acc, labelValidation Accuracy)
plt.title(Training and Validation Accuracy)
plt.legend()plt.subplot(1, 2, 2)
plt.plot(loss, labelTraining Loss)
plt.plot(val_loss, labelValidation Loss)
plt.title(Training and Validation Loss)
plt.legend()
plt.show()VGGNet
非常适合硬件加速。
import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense
from tensorflow.keras import Modelnp.set_printoptions(thresholdnp.inf)cifar10 tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) cifar10.load_data()
x_train, x_test x_train / 255.0, x_test / 255.0class VGG16(Model):def __init__(self):super(VGG16, self).__init__()self.c1 Conv2D(filters64, kernel_size(3, 3), paddingsame) # 卷积层1self.b1 BatchNormalization() # BN层1self.a1 Activation(relu) # 激活层1self.c2 Conv2D(filters64, kernel_size(3, 3), paddingsame, )self.b2 BatchNormalization() # BN层1self.a2 Activation(relu) # 激活层1self.p1 MaxPool2D(pool_size(2, 2), strides2, paddingsame)self.d1 Dropout(0.2) # dropout层self.c3 Conv2D(filters128, kernel_size(3, 3), paddingsame)self.b3 BatchNormalization() # BN层1self.a3 Activation(relu) # 激活层1self.c4 Conv2D(filters128, kernel_size(3, 3), paddingsame)self.b4 BatchNormalization() # BN层1self.a4 Activation(relu) # 激活层1self.p2 MaxPool2D(pool_size(2, 2), strides2, paddingsame)self.d2 Dropout(0.2) # dropout层self.c5 Conv2D(filters256, kernel_size(3, 3), paddingsame)self.b5 BatchNormalization() # BN层1self.a5 Activation(relu) # 激活层1self.c6 Conv2D(filters256, kernel_size(3, 3), paddingsame)self.b6 BatchNormalization() # BN层1self.a6 Activation(relu) # 激活层1self.c7 Conv2D(filters256, kernel_size(3, 3), paddingsame)self.b7 BatchNormalization()self.a7 Activation(relu)self.p3 MaxPool2D(pool_size(2, 2), strides2, paddingsame)self.d3 Dropout(0.2)self.c8 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b8 BatchNormalization() # BN层1self.a8 Activation(relu) # 激活层1self.c9 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b9 BatchNormalization() # BN层1self.a9 Activation(relu) # 激活层1self.c10 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b10 BatchNormalization()self.a10 Activation(relu)self.p4 MaxPool2D(pool_size(2, 2), strides2, paddingsame)self.d4 Dropout(0.2)self.c11 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b11 BatchNormalization() # BN层1self.a11 Activation(relu) # 激活层1self.c12 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b12 BatchNormalization() # BN层1self.a12 Activation(relu) # 激活层1self.c13 Conv2D(filters512, kernel_size(3, 3), paddingsame)self.b13 BatchNormalization()self.a13 Activation(relu)self.p5 MaxPool2D(pool_size(2, 2), strides2, paddingsame)self.d5 Dropout(0.2)self.flatten Flatten()self.f1 Dense(512, activationrelu)self.d6 Dropout(0.2)self.f2 Dense(512, activationrelu)self.d7 Dropout(0.2)self.f3 Dense(10, activationsoftmax)def call(self, x):x self.c1(x)x self.b1(x)x self.a1(x)x self.c2(x)x self.b2(x)x self.a2(x)x self.p1(x)x self.d1(x)x self.c3(x)x self.b3(x)x self.a3(x)x self.c4(x)x self.b4(x)x self.a4(x)x self.p2(x)x self.d2(x)x self.c5(x)x self.b5(x)x self.a5(x)x self.c6(x)x self.b6(x)x self.a6(x)x self.c7(x)x self.b7(x)x self.a7(x)x self.p3(x)x self.d3(x)x self.c8(x)x self.b8(x)x self.a8(x)x self.c9(x)x self.b9(x)x self.a9(x)x self.c10(x)x self.b10(x)x self.a10(x)x self.p4(x)x self.d4(x)x self.c11(x)x self.b11(x)x self.a11(x)x self.c12(x)x self.b12(x)x self.a12(x)x self.c13(x)x self.b13(x)x self.a13(x)x self.p5(x)x self.d5(x)x self.flatten(x)x self.f1(x)x self.d6(x)x self.f2(x)x self.d7(x)y self.f3(x)return ymodel VGG16()model.compile(optimizeradam,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsFalse),metrics[sparse_categorical_accuracy])checkpoint_save_path ./checkpoint/VGG16.ckpt
if os.path.exists(checkpoint_save_path .index):print(-------------load the model-----------------)model.load_weights(checkpoint_save_path)cp_callback tf.keras.callbacks.ModelCheckpoint(filepathcheckpoint_save_path,save_weights_onlyTrue,save_best_onlyTrue)history model.fit(x_train, y_train, batch_size32, epochs5, validation_data(x_test, y_test), validation_freq1,callbacks[cp_callback])
model.summary()# print(model.trainable_variables)
file open(./weights.txt, w)
for v in model.trainable_variables:file.write(str(v.name) \n)file.write(str(v.shape) \n)file.write(str(v.numpy()) \n)
file.close()############################################### show ################################################ 显示训练集和验证集的acc和loss曲线
acc history.history[sparse_categorical_accuracy]
val_acc history.history[val_sparse_categorical_accuracy]
loss history.history[loss]
val_loss history.history[val_loss]plt.subplot(1, 2, 1)
plt.plot(acc, labelTraining Accuracy)
plt.plot(val_acc, labelValidation Accuracy)
plt.title(Training and Validation Accuracy)
plt.legend()plt.subplot(1, 2, 2)
plt.plot(loss, labelTraining Loss)
plt.plot(val_loss, labelValidation Loss)
plt.title(Training and Validation Loss)
plt.legend()
plt.show()InceptionNet
引入Inception结构块在同一层网络内内采用不同的卷积核提升模型感知力使用批标准化缓解梯度消失 结构块分为4个分支分别经过*1卷积核输出到卷积连接器经过*1配合3*3输出到卷积连接器经过1*1配合5*5输出到卷积连接器经过3*3最大池化核配合1*1卷积核卷积连接器将各个特征数据按照深度方向进行拼接。
分支1采用16个1*1全零填充
分支2先将16*1*1进行降维采用BN 在进行16*3*3卷积核进行操作
分支3先降维在使用16个5*5卷积核
分支4先池化降维 CBN操作 通过1*1卷积核作用到每一个像素点通过设定少于输入特征图深度的1*1卷积核个数减少输出特征图的深度起到了降维的作用减少参数量和计算量。
使用concat堆叠在一起axis3表示堆叠方向 精简的inception网络结构网络共有10层第一层采用16个3*3卷积核。 基于cifar数据集是10分类。 将batch_size跳到1024,充分发挥GPU性能发挥70%到80%
import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense, \GlobalAveragePooling2D
from tensorflow.keras import Modelnp.set_printoptions(thresholdnp.inf)cifar10 tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) cifar10.load_data()
x_train, x_test x_train / 255.0, x_test / 255.0class ConvBNRelu(Model):def __init__(self, ch, kernelsz3, strides1, paddingsame):super(ConvBNRelu, self).__init__()self.model tf.keras.models.Sequential([Conv2D(ch, kernelsz, stridesstrides, paddingpadding),BatchNormalization(),Activation(relu)])def call(self, x):x self.model(x, trainingFalse) #在trainingFalse时BN通过整个训练集计算均值、方差去做批归一化trainingTrue时通过当前batch的均值、方差去做批归一化。推理时 trainingFalse效果好return xclass InceptionBlk(Model):def __init__(self, ch, strides1):super(InceptionBlk, self).__init__()self.ch chself.strides stridesself.c1 ConvBNRelu(ch, kernelsz1, stridesstrides)self.c2_1 ConvBNRelu(ch, kernelsz1, stridesstrides)self.c2_2 ConvBNRelu(ch, kernelsz3, strides1)self.c3_1 ConvBNRelu(ch, kernelsz1, stridesstrides)self.c3_2 ConvBNRelu(ch, kernelsz5, strides1)self.p4_1 MaxPool2D(3, strides1, paddingsame)self.c4_2 ConvBNRelu(ch, kernelsz1, stridesstrides)def call(self, x):x1 self.c1(x)x2_1 self.c2_1(x)x2_2 self.c2_2(x2_1)x3_1 self.c3_1(x)x3_2 self.c3_2(x3_1)x4_1 self.p4_1(x)x4_2 self.c4_2(x4_1)# concat along axischannelx tf.concat([x1, x2_2, x3_2, x4_2], axis3)return xclass Inception10(Model):def __init__(self, num_blocks, num_classes, init_ch16, **kwargs):super(Inception10, self).__init__(**kwargs)self.in_channels init_chself.out_channels init_chself.num_blocks num_blocksself.init_ch init_chself.c1 ConvBNRelu(init_ch)self.blocks tf.keras.models.Sequential()for block_id in range(num_blocks):for layer_id in range(2):if layer_id 0:block InceptionBlk(self.out_channels, strides2)else:block InceptionBlk(self.out_channels, strides1)self.blocks.add(block)# enlarger out_channels per blockself.out_channels * 2self.p1 GlobalAveragePooling2D()self.f1 Dense(num_classes, activationsoftmax)def call(self, x):x self.c1(x)x self.blocks(x)x self.p1(x)y self.f1(x)return ymodel Inception10(num_blocks2, num_classes10)model.compile(optimizeradam,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsFalse),metrics[sparse_categorical_accuracy])checkpoint_save_path ./checkpoint/Inception10.ckpt
if os.path.exists(checkpoint_save_path .index):print(-------------load the model-----------------)model.load_weights(checkpoint_save_path)cp_callback tf.keras.callbacks.ModelCheckpoint(filepathcheckpoint_save_path,save_weights_onlyTrue,save_best_onlyTrue)history model.fit(x_train, y_train, batch_size32, epochs5, validation_data(x_test, y_test), validation_freq1,callbacks[cp_callback])
model.summary()# print(model.trainable_variables)
file open(./weights.txt, w)
for v in model.trainable_variables:file.write(str(v.name) \n)file.write(str(v.shape) \n)file.write(str(v.numpy()) \n)
file.close()############################################### show ################################################ 显示训练集和验证集的acc和loss曲线
acc history.history[sparse_categorical_accuracy]
val_acc history.history[val_sparse_categorical_accuracy]
loss history.history[loss]
val_loss history.history[val_loss]plt.subplot(1, 2, 1)
plt.plot(acc, labelTraining Accuracy)
plt.plot(val_acc, labelValidation Accuracy)
plt.title(Training and Validation Accuracy)
plt.legend()plt.subplot(1, 2, 2)
plt.plot(loss, labelTraining Loss)
plt.plot(val_loss, labelValidation Loss)
plt.title(Training and Validation Loss)
plt.legend()
plt.show()ResNet
提出了层间残差跳连引入前方信息减少梯度消失是增加网络层数成为可能
通过提升网络层数有可能会使退化后面特征丢失了前面特征 此操作缓解了退化 Inception的是延深度方向叠加ResNet块中的是特征图对应元素值相加矩阵相加
ResNet块中有两种情况一种情况用图中实线表示 加速模型收敛使模型的batch_size调整到128
import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense
from tensorflow.keras import Modelnp.set_printoptions(thresholdnp.inf)cifar10 tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) cifar10.load_data()
x_train, x_test x_train / 255.0, x_test / 255.0class ResnetBlock(Model):def __init__(self, filters, strides1, residual_pathFalse):super(ResnetBlock, self).__init__()self.filters filtersself.strides stridesself.residual_path residual_pathself.c1 Conv2D(filters, (3, 3), stridesstrides, paddingsame, use_biasFalse)self.b1 BatchNormalization()self.a1 Activation(relu)self.c2 Conv2D(filters, (3, 3), strides1, paddingsame, use_biasFalse)self.b2 BatchNormalization()# residual_path为True时对输入进行下采样即用1x1的卷积核做卷积操作保证x能和F(x)维度相同顺利相加if residual_path:self.down_c1 Conv2D(filters, (1, 1), stridesstrides, paddingsame, use_biasFalse)self.down_b1 BatchNormalization()self.a2 Activation(relu)def call(self, inputs):residual inputs # residual等于输入值本身即residualx# 将输入通过卷积、BN层、激活层计算F(x)x self.c1(inputs)x self.b1(x)x self.a1(x)x self.c2(x)y self.b2(x)if self.residual_path:residual self.down_c1(inputs)residual self.down_b1(residual)out self.a2(y residual) # 最后输出的是两部分的和即F(x)x或F(x)Wx,再过激活函数return outclass ResNet18(Model):def __init__(self, block_list, initial_filters64): # block_list表示每个block有几个卷积层super(ResNet18, self).__init__()self.num_blocks len(block_list) # 共有几个blockself.block_list block_listself.out_filters initial_filtersself.c1 Conv2D(self.out_filters, (3, 3), strides1, paddingsame, use_biasFalse)self.b1 BatchNormalization()self.a1 Activation(relu)self.blocks tf.keras.models.Sequential()# 构建ResNet网络结构for block_id in range(len(block_list)): # 第几个resnet blockfor layer_id in range(block_list[block_id]): # 第几个卷积层if block_id ! 0 and layer_id 0: # 对除第一个block以外的每个block的输入进行下采样block ResnetBlock(self.out_filters, strides2, residual_pathTrue)else:block ResnetBlock(self.out_filters, residual_pathFalse)self.blocks.add(block) # 将构建好的block加入resnetself.out_filters * 2 # 下一个block的卷积核数是上一个block的2倍self.p1 tf.keras.layers.GlobalAveragePooling2D()self.f1 tf.keras.layers.Dense(10, activationsoftmax, kernel_regularizertf.keras.regularizers.l2())def call(self, inputs):x self.c1(inputs)x self.b1(x)x self.a1(x)x self.blocks(x)x self.p1(x)y self.f1(x)return ymodel ResNet18([2, 2, 2, 2])model.compile(optimizeradam,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsFalse),metrics[sparse_categorical_accuracy])checkpoint_save_path ./checkpoint/ResNet18.ckpt
if os.path.exists(checkpoint_save_path .index):print(-------------load the model-----------------)model.load_weights(checkpoint_save_path)cp_callback tf.keras.callbacks.ModelCheckpoint(filepathcheckpoint_save_path,save_weights_onlyTrue,save_best_onlyTrue)history model.fit(x_train, y_train, batch_size32, epochs5, validation_data(x_test, y_test), validation_freq1,callbacks[cp_callback])
model.summary()# print(model.trainable_variables)
file open(./weights.txt, w)
for v in model.trainable_variables:file.write(str(v.name) \n)file.write(str(v.shape) \n)file.write(str(v.numpy()) \n)
file.close()############################################### show ################################################ 显示训练集和验证集的acc和loss曲线
acc history.history[sparse_categorical_accuracy]
val_acc history.history[val_sparse_categorical_accuracy]
loss history.history[loss]
val_loss history.history[val_loss]plt.subplot(1, 2, 1)
plt.plot(acc, labelTraining Accuracy)
plt.plot(val_acc, labelValidation Accuracy)
plt.title(Training and Validation Accuracy)
plt.legend()plt.subplot(1, 2, 2)
plt.plot(loss, labelTraining Loss)
plt.plot(val_loss, labelValidation Loss)
plt.title(Training and Validation Loss)
plt.legend()
plt.show()总结
lenet 卷积网络的开篇之作共享卷积核减少网络参数。
AlexNet 使用relu激活函数提升训练速度使用Dropout缓解过拟合
VGGnet 小尺寸卷积核减少参数网络结构规整适合并行加速
InceptionNet一层内使用不同尺寸卷积核提升感知力使用批标准化缓解梯度消失。
