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()