多层感知机从零开始的实现
import torch import numpy as np import sys sys.path.append("/home/kesci/input") import d2lzh1981 as d2l print(torch.__version__)
1.3.0
获取训练集
batch_size = 256 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,root='/home/kesci/input/FashionMNIST2065')
定义模型参数
num_inputs, num_outputs, num_hiddens = 784, 10, 256 W1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float) b1 = torch.zeros(num_hiddens, dtype=torch.float) W2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float) b2 = torch.zeros(num_outputs, dtype=torch.float) params = [W1, b1, W2, b2] for param in params: param.requires_grad_(requires_grad=True)
定义激活函数
def relu(X): return torch.max(input=X, other=torch.tensor(0.0))
定义网络
def net(X): X = X.view((-1, num_inputs)) H = relu(torch.matmul(X, W1) + b1) return torch.matmul(H, W2) + b2
定义损失函数
loss = torch.nn.CrossEntropyLoss()
训练
num_epochs, lr = 5, 100.0 # def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, # params=None, lr=None, optimizer=None): # for epoch in range(num_epochs): # train_l_sum, train_acc_sum, n = 0.0, 0.0, 0 # for X, y in train_iter: # y_hat = net(X) # l = loss(y_hat, y).sum() # # # 梯度清零 # if optimizer is not None: # optimizer.zero_grad() # elif params is not None and params[0].grad is not None: # for param in params: # param.grad.data.zero_() # # l.backward() # if optimizer is None: # d2l.sgd(params, lr, batch_size) # else: # optimizer.step() # “softmax回归的简洁实现”一节将用到 # # # train_l_sum += l.item() # train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item() # n += y.shape[0] # test_acc = evaluate_accuracy(test_iter, net) # print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' # % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)
epoch 1, loss 0.0030, train acc 0.712, test acc 0.806 epoch 2, loss 0.0019, train acc 0.821, test acc 0.806 epoch 3, loss 0.0017, train acc 0.847, test acc 0.825 epoch 4, loss 0.0015, train acc 0.856, test acc 0.834 epoch 5, loss 0.0015, train acc 0.863, test acc 0.847
多层感知机pytorch实现
import torch from torch import nn from torch.nn import init import numpy as np import sys sys.path.append("/home/kesci/input") import d2lzh1981 as d2l print(torch.__version__)
1.3.0
初始化模型和各个参数
num_inputs, num_outputs, num_hiddens = 784, 10, 256 net = nn.Sequential( d2l.FlattenLayer(), nn.Linear(num_inputs, num_hiddens), nn.ReLU(), nn.Linear(num_hiddens, num_outputs), ) for params in net.parameters(): init.normal_(params, mean=0, std=0.01)
训练
batch_size = 256 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,root='/home/kesci/input/FashionMNIST2065') loss = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(net.parameters(), lr=0.5) num_epochs = 5 d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
epoch 1, loss 0.0031, train acc 0.701, test acc 0.774 epoch 2, loss 0.0019, train acc 0.821, test acc 0.806 epoch 3, loss 0.0017, train acc 0.841, test acc 0.805 epoch 4, loss 0.0015, train acc 0.855, test acc 0.834 epoch 5, loss 0.0014, train acc 0.866, test acc 0.840
