先上一段代碼
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import time
%matplotlib inline
from IPython import display
# Hyper-parameters
input_size = 1
output_size = 1
learning_rate = 0.001
def load_data(filename):
xys=[]
with open(filename,'r') as f:
for line in f:
xys.append(map(float, line.strip().split()))
xs, ys = zip(*xys)#解壓，返回二維矩陣式
return np.asarray(xs), np.asarray(ys)
x_t, y_t=load_data(r'train.txt')
x=[]
y=[]
for i in range(len(x_t)):
x.append([x_t[i]])
y.append([y_t[i]])
x_train = np.array(x, dtype=np.float32)
y_train = np.array(y, dtype=np.float32)
plt.scatter(x_train,y_train)
plt.show()
output_1_0.png
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear = nn.Linear(input_size, output_size) # One in and one out
def forward(self, x):
y_pred = self.linear(x)
return y_pred
model = Model()
# model = nn.Linear(input_size, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.00001)
for epoch in range(50):
inputs=torch.from_numpy(x_train)
targets=torch.from_numpy(y_train)
outputs = model(inputs)
loss = criterion(outputs, targets)
# Zero gradients
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 5 == 0:
predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()
display.clear_output(wait=True)
plt.pause(1)
output_4_0.png
這玩意調(diào)了一整天而涉，就因?yàn)閷W(xué)習(xí)速率著瓶，剛開始設(shè)置的只有0.001，本來感覺夠小了啼县，做出來圖發(fā)現(xiàn)擬合線不斷上下跳躍材原，剛開始還以為是數(shù)據(jù)讀取的問題，換了隨機(jī)數(shù)據(jù)發(fā)現(xiàn)正常谭羔。試著調(diào)小學(xué)習(xí)速率华糖，擬合線才出來。第一次遇到……
