第三章 3.6 批大小的影响

第三章 3.4 训练神经网络

 

# https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch
# https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch###################  Chapter Three ######################################## 第三章  读取数据集并显示
from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
########################################################################
from torchvision import datasets
import torch
data_folder = '~/data/FMNIST' # This can be any directory you want to
# download FMNIST to
fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)
tr_images = fmnist.data
tr_targets = fmnist.targetsval_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)
val_images = val_fmnist.data
val_targets = val_fmnist.targets########################################################################
import matplotlib.pyplot as plt
#matplotlib inline
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
device = 'cuda' if torch.cuda.is_available() else 'cpu'########################################################################
class FMNISTDataset(Dataset):def __init__(self, x, y):x = x.float()/255 #归一化x = x.view(-1,28*28)self.x, self.y = x, ydef __getitem__(self, ix):x, y = self.x[ix], self.y[ix]return x.to(device), y.to(device)def __len__(self):return len(self.x)from torch.optim import SGD, Adam
def get_model():model = nn.Sequential(nn.Linear(28 * 28, 1000),nn.ReLU(),nn.Linear(1000, 10)).to(device)loss_fn = nn.CrossEntropyLoss()optimizer = Adam(model.parameters(), lr=1e-2)return model, loss_fn, optimizerdef train_batch(x, y, model, optimizer, loss_fn):model.train()prediction = model(x)batch_loss = loss_fn(prediction, y)batch_loss.backward()optimizer.step()optimizer.zero_grad()return batch_loss.item()def accuracy(x, y, model):model.eval()# this is the same as @torch.no_grad# at the top of function, only difference# being, grad is not computed in the with scope
    with torch.no_grad():prediction = model(x)max_values, argmaxes = prediction.max(-1)is_correct = argmaxes == yreturn is_correct.cpu().numpy().tolist()########################################################################
def get_data():train = FMNISTDataset(tr_images, tr_targets)trn_dl = DataLoader(train, batch_size=10000, shuffle=True) # 比较批大小分别是10000，和32是，损失函数值 和 准确度值，注意：数据集只有60000个样本val = FMNISTDataset(val_images, val_targets)val_dl = DataLoader(val, batch_size=len(val_images), shuffle=False)return trn_dl, val_dl
########################################################################
#@torch.no_grad()
def val_loss(x, y, model):with torch.no_grad():prediction = model(x)val_loss = loss_fn(prediction, y)return val_loss.item()########################################################################
trn_dl, val_dl = get_data()
model, loss_fn, optimizer = get_model()########################################################################
train_losses, train_accuracies = [], []
val_losses, val_accuracies = [], []
for epoch in range(5):print(epoch)train_epoch_losses, train_epoch_accuracies = [], []for ix, batch in enumerate(iter(trn_dl)):x, y = batchbatch_loss = train_batch(x, y, model, optimizer, loss_fn)train_epoch_losses.append(batch_loss)train_epoch_loss = np.array(train_epoch_losses).mean()for ix, batch in enumerate(iter(trn_dl)):x, y = batchis_correct = accuracy(x, y, model)train_epoch_accuracies.extend(is_correct)train_epoch_accuracy = np.mean(train_epoch_accuracies)for ix, batch in enumerate(iter(val_dl)):x, y = batchval_is_correct = accuracy(x, y, model)validation_loss = val_loss(x, y, model)val_epoch_accuracy = np.mean(val_is_correct)train_losses.append(train_epoch_loss)train_accuracies.append(train_epoch_accuracy)val_losses.append(validation_loss)val_accuracies.append(val_epoch_accuracy)########################################################################
epochs = np.arange(5)+1
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
#%matplotlib inline
plt.figure(figsize=(20,5))
plt.subplot(211)
plt.plot(epochs, train_losses, 'bo', label='Training loss')
plt.plot(epochs, val_losses, 'r', label='Validation loss')
plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))
plt.title('Training and validation loss when batch size is 32')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.grid('off')
#plt.show()
plt.subplot(212)
plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')
plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')
plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))
plt.title('Training and validation accuracy when batch size is 32')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()])
plt.legend()
plt.grid('off')
plt.show()