kersasはVG 16 CIFAR 10データセット方式を実現します。

余計なことを言わないで、コードを見てください。

import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from keras import optimizers
import numpy as np
from keras.layers.core import Lambda
from keras import backend as K
from keras.optimizers import SGD
from keras import regularizers
 
#import data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
 
weight_decay = 0.0005
nb_epoch=100
batch_size=32
 
#layer1 32*32*3
model = Sequential()
model.add(Conv2D(64, (3, 3), padding='same',
input_shape=(32,32,3),kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
#layer2 32*32*64
model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#layer3 16*16*64
model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer4 16*16*128
model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#layer5 8*8*128
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer6 8*8*256
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer7 8*8*256
model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#layer8 4*4*256
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer9 4*4*512
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer10 4*4*512
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#layer11 2*2*512
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer12 2*2*512
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
#layer13 2*2*512
model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
#layer14 1*1*512
model.add(Flatten())
model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
#layer15 512
model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
#layer16 512
model.add(Dropout(0.5))
model.add(Dense(10))
model.add(Activation('softmax'))
# 10
 
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])
 
model.fit(x_train,y_train,epochs=nb_epoch, batch_size=batch_size,
       validation_split=0.1, verbose=1)

知識を補充します：pytouchは一歩VG 16の上で自分のデータ集を訓練します。
データセットとロードを準備し、ImageFolder
多くの機械学習や深度学習の任務の中で、私たちは自分の写真を提供します。つまり、私たちのデータセットは前もって処理したのではなく、mnist、cifar 10などはすでにあなたに処理してあげました。もっと多いのはオリジナルの写真です。例えば、私たちは猫と犬を分類します。dataファイルの下には、trinとvalの二つのフォルダがあります。そしてtrinの下にはcatとdogの二つのフォルダがあります。中には自分の画像データがあります。valフォルダはtrainと同じです。これで私たちのデータセットが用意されます。

ImageFolderはカタログ名をラベルとしてデータセットを区分することができます。以下はpytoch中国語の文書でImageFolderについて説明します。

#         
train_transforms = transforms.Compose([
  transforms.RandomResizedCrop(224), #            
  transforms.RandomHorizontalFlip(), #    
  transforms.ToTensor(),   #     
  transforms.Normalize((.5, .5, .5), (.5, .5, .5)) #     
])
#       
val_transforms = transforms.Compose([
  transforms.Resize(256),
  transforms.RandomResizedCrop(224),
  transforms.ToTensor(),
  transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])

train_dir = "G:/data/train"      #     
#     
train_datasets = datasets.ImageFolder(train_dir, transform=train_transforms)
#     
train_dataloader = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True)

val_dir = "G:/datat/val" 
val_datasets = datasets.ImageFolder(val_dir, transform=val_transforms)
val_dataloader = torch.utils.data.DataLoader(val_datasets, batch_size=batch_size, shuffle=True)

移転学習はVG 16を例にしている。
以下は移行コードの実現です。

class VGGNet(nn.Module):
  def __init__(self, num_classes=2):  #num_classes，         2
    super(VGGNet, self).__init__()
    net = models.vgg16(pretrained=True)  #        VGG16    
    net.classifier = nn.Sequential() #      ，           
    self.features = net #  VGG16    
    self.classifier = nn.Sequential(  #        
        nn.Linear(512 * 7 * 7, 512), #512 * 7 * 7     ， VGG16     ，               
        nn.ReLU(True),
        nn.Dropout(),
        nn.Linear(512, 128),
        nn.ReLU(True),
        nn.Dropout(),
        nn.Linear(128, num_classes),
    )

  def forward(self, x):
    x = self.features(x)
    x = x.view(x.size(0), -1)
    x = self.classifier(x)
    return x

完全コードは以下の通りです

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
from torchvision import models

batch_size = 16
learning_rate = 0.0002
epoch = 10

train_transforms = transforms.Compose([
  transforms.RandomResizedCrop(224),
  transforms.RandomHorizontalFlip(),
  transforms.ToTensor(),
  transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])
val_transforms = transforms.Compose([
  transforms.Resize(256),
  transforms.RandomResizedCrop(224),
  transforms.ToTensor(),
  transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])

train_dir = './VGGDataSet/train'
train_datasets = datasets.ImageFolder(train_dir, transform=train_transforms)
train_dataloader = torch.utils.data.DataLoader(train_datasets, batch_size=batch_size, shuffle=True)

val_dir = './VGGDataSet/val'
val_datasets = datasets.ImageFolder(val_dir, transform=val_transforms)
val_dataloader = torch.utils.data.DataLoader(val_datasets, batch_size=batch_size, shuffle=True)

class VGGNet(nn.Module):
  def __init__(self, num_classes=3):
    super(VGGNet, self).__init__()
    net = models.vgg16(pretrained=True)
    net.classifier = nn.Sequential()
    self.features = net
    self.classifier = nn.Sequential(
        nn.Linear(512 * 7 * 7, 512),
        nn.ReLU(True),
        nn.Dropout(),
        nn.Linear(512, 128),
        nn.ReLU(True),
        nn.Dropout(),
        nn.Linear(128, num_classes),
    )

  def forward(self, x):
    x = self.features(x)
    x = x.view(x.size(0), -1)
    x = self.classifier(x)
    return x

#--------------------    ---------------------------------
model = VGGNet()
if torch.cuda.is_available():
  model.cuda()
params = [{'params': md.parameters()} for md in model.children()
     if md in [model.classifier]]
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
loss_func = nn.CrossEntropyLoss()

Loss_list = []
Accuracy_list = []

for epoch in range(100):
  print('epoch {}'.format(epoch + 1))
  # training-----------------------------
  train_loss = 0.
  train_acc = 0.
  for batch_x, batch_y in train_dataloader:
    batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda()
    out = model(batch_x)
    loss = loss_func(out, batch_y)
    train_loss += loss.data[0]
    pred = torch.max(out, 1)[1]
    train_correct = (pred == batch_y).sum()
    train_acc += train_correct.data[0]
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
  print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(
    train_datasets)), train_acc / (len(train_datasets))))

  # evaluation--------------------------------
  model.eval()
  eval_loss = 0.
  eval_acc = 0.
  for batch_x, batch_y in val_dataloader:
    batch_x, batch_y = Variable(batch_x, volatile=True).cuda(), Variable(batch_y, volatile=True).cuda()
    out = model(batch_x)
    loss = loss_func(out, batch_y)
    eval_loss += loss.data[0]
    pred = torch.max(out, 1)[1]
    num_correct = (pred == batch_y).sum()
    eval_acc += num_correct.data[0]
  print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
    val_datasets)), eval_acc / (len(val_datasets))))
    
	Loss_list.append(eval_loss / (len(val_datasets)))
  Accuracy_list.append(100 * eval_acc / (len(val_datasets)))

x1 = range(0, 100)
x2 = range(0, 100)
y1 = Accuracy_list
y2 = Loss_list
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('Test accuracy vs. epoches')
plt.ylabel('Test accuracy')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('Test loss vs. epoches')
plt.ylabel('Test loss')
plt.show()
# plt.savefig("accuracy_loss.jpg")

以上のkersはVGG 16 CIFAR 10のデータセット方式を実現しました。小編集は皆さんに提供したすべての内容を共有しています。参考にしてもらいたいです。どうぞよろしくお願いします。