第三章 3.4 训练神经网络

 pytorch  官网：https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module

代码：

# https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch
# https://github.com/PacktPublishing/Modern-Computer-Vision-with-PyTorch

###################  Chapter Three #######################################

# 第三章  
from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
########################################################################

#%matplotlib inline
device = "cuda" if torch.cuda.is_available() else "cpu"
from torchvision import datasets
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

########################################################################
class FMNISTDataset(Dataset):
    def __init__(self, x, y):
        x = x.float()
        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)
########################################################################
def get_data():
    train = FMNISTDataset(tr_images, tr_targets)
    trn_dl = DataLoader(train, batch_size=32, shuffle=True)
    return trn_dl
########################################################################

from torch.optim import SGD
def get_model():
    model = nn.Sequential(
        nn.Linear(28 * 28, 1000),
        nn.ReLU(),
        nn.Linear(1000, 10)
    ).to(device)
    loss_fn = nn.CrossEntropyLoss()
    optimizer = SGD(model.parameters(), lr=1e-2)
    return model, loss_fn, optimizer
########################################################################
def train_batch(x, y, model, opt, loss_fn):
    model.train() # <- let's hold on to this until we reach dropout section
    # call your model like any python function on your batch of inputs
    prediction = model(x)
    # compute loss
    batch_loss = loss_fn(prediction, y)
    # based on the forward pass in `model(x)` compute all the gradients of
    # 'model.parameters()'
    batch_loss.backward()
    # apply new-weights = f(old-weights, old-weight-gradients) where
    # "f" is the optimizer

    optimizer.step()
    # Flush gradients memory for next batch of calculations
    optimizer.zero_grad()
    return batch_loss.item()

########################################################################
def accuracy(x, y, model):
    model.eval() # <- let's wait till we get to dropout section
    # get the prediction matrix for a tensor of `x` images
    prediction = model(x)
    # compute if the location of maximum in each row coincides
    # with ground truth
    max_values, argmaxes = prediction.max(-1)
    is_correct = argmaxes == y
    return is_correct.cpu().numpy().tolist()
########################################################################
trn_dl = get_data()
model, loss_fn, optimizer = get_model()
########################################################################
losses, accuracies = [], []
for epoch in range(5):
    print(epoch)
    epoch_losses, epoch_accuracies = [], []
    for ix, batch in enumerate(iter(trn_dl)):
        x, y = batch
        batch_loss = train_batch(x, y, model, optimizer, loss_fn)
        epoch_losses.append(batch_loss)
    epoch_loss = np.array(epoch_losses).mean()
    for ix, batch in enumerate(iter(trn_dl)):
        x, y = batch
        is_correct = accuracy(x, y, model)
        epoch_accuracies.extend(is_correct)
    epoch_accuracy = np.mean(epoch_accuracies)
    losses.append(epoch_loss)
    accuracies.append(epoch_accuracy)
########################################################################

epochs = np.arange(5)+1
plt.figure(figsize=(20,5))
plt.subplot(121)
plt.title('Loss value over increasing epochs')
plt.plot(epochs, losses, label='Training Loss')
plt.legend()
plt.subplot(122)
plt.title('Accuracy value over increasing epochs')
plt.plot(epochs, accuracies, label='Training Accuracy')
#plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()])
plt.legend()
plt.show()