第三章：3.8.1 绘制各层参数分布图 hist
Chapter03/Varying_learning_rate_on_scaled_data.ipynb
绘制 各层参数分布图
# 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.targets val_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, y def __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, optimizer def 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 == y return is_correct.cpu().numpy().tolist() ######################################################################## def get_data(): train = FMNISTDataset(tr_images, tr_targets) trn_dl = DataLoader(train, batch_size=32, shuffle=True)#批大小 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(10): #轮数 10次 print(epoch) train_epoch_losses, train_epoch_accuracies = [], [] for ix, batch in enumerate(iter(trn_dl)): x, y = batch batch_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 = batch is_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 = batch val_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(10)+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() plt.figure(figsize=(20,5)) for ix, par in enumerate(model.parameters()): print(f'绘图：{ix}') print(f'数据：{par.shape}') if(ix==0): plt.subplot(411) plt.hist(par.cpu().detach().numpy().flatten()) plt.title('Distribution of weights conencting input to hidden layer') #plt.show() elif(ix ==1): plt.subplot(412) plt.hist(par.cpu().detach().numpy().flatten()) plt.title('Distribution of biases of hidden layer') #plt.show() elif(ix==2): plt.subplot(413) plt.hist(par.cpu().detach().numpy().flatten()) plt.title('Distribution of weights conencting hidden to output layer') #plt.show() elif(ix ==3): plt.subplot(414) plt.hist(par.cpu().detach().numpy().flatten()) plt.title('Distribution of biases of output layer') plt.show()
