Pytorch 深度学习实践 第4讲

import torch

# x, y是矩阵，3行1列，也就是说总共有3个数据，每个数据只有1个特征
x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0]])


# design model using class
"""
our model class should be inherit from nn.Module, which is base class for all neural network modules.
member methods __init__() and forward() have to be implemented
class nn.linear contain two member Tensors: weight and bias
class nn.Linear has implemented the magic method __call__(),which enable the instance of the class can
be called just like a function.Normally the forward() will be called 
"""

class LinearModel(torch.nn.Module):
    def __init__(self):
        # (1,1)是指输入x和输出y的特征维度，这里数据集中的x和y的特征都是1维的
        # 该线性层需要学习的参数是w和b  获取w/b的方式分别是~linear.weight/linear.bias
        super(LinearModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred


model = LinearModel()

# construct loss and optimizer
# criterion = torch.nn.MSELoss(size_average = False)
criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # model.parameters()自动完成参数的初始化操作
# torch.optim.

for epoch in range(100):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    loss.backward()  # backward: autograd，自动计算梯度
    optimizer.step()  # update 参数，即更新w和b的值
    optimizer.zero_grad()  # the grad computer by .backward() will be accumulated. so before backward, remember set the grad to zero

print('w= ', model.linear.weight.item())
print('b= ', model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)

作业：

import torch
import numpy as np
import matplotlib.pyplot as plt

x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0]])

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # 构造对象，并说明输入输出的维数，第三个参数默认为true，表示用到b

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred


model = LinearModel()

criterion = torch.nn.MSELoss(reduction='sum')
epoch_list = np.arange(0, 100, 1)
plt.figure()
optimizer_list = ['Adagrad', 'Adam', 'Adamax', 'ASGD', 'RMSprop', 'Rprop', 'sgd']
for opt_list in optimizer_list:
    if opt_list == 'Adagrad':
        optimizer = torch.optim.Adagrad(model.parameters(), lr=0.01)
    elif opt_list == 'Adam':
        optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    elif opt_list == 'Adamax':
        optimizer = torch.optim.Adamax(model.parameters(), lr=0.01)
    elif opt_list == 'ASGD':
        optimizer = torch.optim.ASGD(model.parameters(), lr=0.01)
    elif opt_list == 'RMSprop':
        optimizer = torch.optim.RMSprop(model.parameters(), lr=0.01)
    elif opt_list == 'Rprop':
        optimizer = torch.optim.Rprop(model.parameters(), lr=0.01)
    elif opt_list == 'sgd':
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

    loss_list = []
    for epoch in range(100):
        y_pred = model(x_data)
        loss = criterion(y_pred, y_data)
        loss_list.append(loss.item())

        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

    plt.plot(epoch_list, loss_list)
    plt.title(opt_list)
    plt.show()