PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

文章目录

  • 一、强大的 hub 模块
    • 1. hub 模块的使用
    • 2. hub 模块的代码演示
  • 二、搭建神经网络进行气温预测
    • 1. 数据信息处理
    • 2. 数据图画绘制
    • 3. 构建网络模型
    • 4. 更简单的构建网络模型

本文参加新星计划人工智能(Pytorch)赛道：https://bbs.csdn.net/topics/613989052
PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

一、强大的 hub 模块

1. hub 模块的使用

PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

2. hub 模块的代码演示

import torch
model = torch.hub.load('pytorch/vision:v0.4.2', 'deeplabv3_resnet101', pretrained=True)
model.eval()

torch.hub.list('pytorch/vision:v0.4.2')
#Using cache found in C:\Users\Administrator/.cache\torch\hub\pytorch_vision_v0.4.2
#['alexnet',
# 'deeplabv3_resnet101',
# 'densenet121',
# 'densenet161',
# 'densenet169',
# 'densenet201',
# 'fcn_resnet101',
# 'googlenet',
# 'inception_v3',
# 'mobilenet_v2',
# 'resnet101',
# 'resnet152',
# 'resnet18',
# 'resnet34',
# 'resnet50',
# 'resnext101_32x8d',
# 'resnext50_32x4d',
# 'shufflenet_v2_x0_5',
# 'shufflenet_v2_x1_0',
# 'squeezenet1_0',
# 'squeezenet1_1',
# 'vgg11',
# 'vgg11_bn',
# 'vgg13',
# 'vgg13_bn',
# 'vgg16',
# 'vgg16_bn',
# 'vgg19',
# 'vgg19_bn',
# 'wide_resnet101_2',
# 'wide_resnet50_2']

# Download an example image from the pytorch website
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)

# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
​
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
​

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')
​
with torch.no_grad():
    output = model(input_batch)['out'][0]
output_predictions = output.argmax(0)

# create a color pallette, selecting a color for each class
palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
colors = (colors % 255).numpy().astype("uint8")

# plot the semantic segmentation predictions of 21 classes in each color
r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
r.putpalette(colors)
​
import matplotlib.pyplot as plt
plt.imshow(r)
plt.show()

PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

二、搭建神经网络进行气温预测

1. 数据信息处理

import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline

features = pd.read_csv('temps.csv')
features.head()
#year	month	day	week	temp_2	temp_1	average	actual	friend
#0	2016	1	1	Fri	45	45	45.6	45	29
#1	2016	1	2	Sat	44	45	45.7	44	61
#2	2016	1	3	Sun	45	44	45.8	41	56
#3	2016	1	4	Mon	44	41	45.9	40	53
#4	2016	1	5	Tues	41	40	46.0	44	41

print('数据维度:', features.shape)
#数据维度: (348, 9)

# 处理时间数据
import datetime
​
# 分别得到年，月，日
years = features['year']
months = features['month']
days = features['day']
​
# datetime格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

dates[:5]
#[datetime.datetime(2016, 1, 1, 0, 0),
# datetime.datetime(2016, 1, 2, 0, 0),
# datetime.datetime(2016, 1, 3, 0, 0),
# datetime.datetime(2016, 1, 4, 0, 0),
# datetime.datetime(2016, 1, 5, 0, 0)]

2. 数据图画绘制

plt.style.use('fivethirtyeight')

# 设置布局
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))
fig.autofmt_xdate(rotation = 45)

#标签值
ax1.plot(dates, features['actual'])
ax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')

# 昨天
ax2.plot(dates, features['temp_1'])
ax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')

# 前天
ax3.plot(dates, features['temp_2'])
ax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')

# 我的逗逼朋友
ax4.plot(dates, features['friend'])
ax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')

plt.tight_layout(pad=2)

PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

# 独热编码
features = pd.get_dummies(features)
features.head(5)
#year	month	day	temp_2	temp_1	average	actual	friend	week_Fri	week_Mon	week_Sat	week_Sun	week_Thurs	week_Tues	week_Wed
#0	2016	1	1	45	45	45.6	45	29	1	0	0	0	0	0	0
#1	2016	1	2	44	45	45.7	44	61	0	0	1	0	0	0	0
#2	2016	1	3	45	44	45.8	41	56	0	0	0	1	0	0	0
#3	2016	1	4	44	41	45.9	40	53	0	1	0	0	0	0	0
#4	2016	1	5	41	40	46.0	44	41	0	0	0	0	0	1	0

# 标签
labels = np.array(features['actual'])
​
# 在特征中去掉标签
features= features.drop('actual', axis = 1)
​
# 名字单独保存一下，以备后患
feature_list = list(features.columns)
​
# 转换成合适的格式
features = np.array(features)

features.shape
#(348, 14)

from sklearn import preprocessing
input_features = preprocessing.StandardScaler().fit_transform(features)
input_features[0]
#array([ 0.        , -1.5678393 , -1.65682171, -1.48452388, -1.49443549,
#       -1.3470703 , -1.98891668,  2.44131112, -0.40482045, -0.40961596,
#       -0.40482045, -0.40482045, -0.41913682, -0.40482045])

3. 构建网络模型

x = torch.tensor(input_features, dtype = float)
​
y = torch.tensor(labels, dtype = float)
​
# 权重参数初始化
weights = torch.randn((14, 128), dtype = float, requires_grad = True) 
biases = torch.randn(128, dtype = float, requires_grad = True) 
weights2 = torch.randn((128, 1), dtype = float, requires_grad = True) 
biases2 = torch.randn(1, dtype = float, requires_grad = True) 
​
learning_rate = 0.001 
losses = []
​
for i in range(1000):
    # 计算隐层
    hidden = x.mm(weights) + biases
    # 加入激活函数
    hidden = torch.relu(hidden)
    # 预测结果
    predictions = hidden.mm(weights2) + biases2
    # 通计算损失
    loss = torch.mean((predictions - y) ** 2) 
    losses.append(loss.data.numpy())
    # 打印损失值
    if i % 100 == 0:
        print('loss:', loss)
    #返向传播计算
    loss.backward()
    #更新参数
    weights.data.add_(- learning_rate * weights.grad.data)  
    biases.data.add_(- learning_rate * biases.grad.data)
    weights2.data.add_(- learning_rate * weights2.grad.data)
    biases2.data.add_(- learning_rate * biases2.grad.data)
    # 每次迭代都得记得清空
    weights.grad.data.zero_()
    biases.grad.data.zero_()
    weights2.grad.data.zero_()
    biases2.grad.data.zero_()
​#loss: tensor(8347.9924, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(152.3170, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(145.9625, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(143.9453, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(142.8161, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(142.0664, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(141.5386, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(141.1528, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(140.8618, dtype=torch.float64, grad_fn=<MeanBackward0>)
#loss: tensor(140.6318, dtype=torch.float64, grad_fn=<MeanBackward0>)

predictions.shape
#torch.Size([348, 1])

4. 更简单的构建网络模型

input_size = input_features.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
my_nn = torch.nn.Sequential(
    torch.nn.Linear(input_size, hidden_size),
    torch.nn.Sigmoid(),
    torch.nn.Linear(hidden_size, output_size),
)
cost = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)
# 训练网络
losses = []
for i in range(1000):
    batch_loss = []
    # MINI-Batch方法来进行训练
    for start in range(0, len(input_features), batch_size):
        end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
        xx = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True)
        yy = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True)
        prediction = my_nn(xx)
        loss = cost(prediction, yy)
        optimizer.zero_grad()
        loss.backward(retain_graph=True)
        optimizer.step()
        batch_loss.append(loss.data.numpy())
    # 打印损失
    if i % 100==0:
        losses.append(np.mean(batch_loss))
        print(i, np.mean(batch_loss))
#0 3950.7627
#100 37.9201
#200 35.654438
#300 35.278366
#400 35.116814
#500 34.986076
#600 34.868954
#700 34.75414
#800 34.637356
#900 34.516705

x = torch.tensor(input_features, dtype = torch.float)
predict = my_nn(x).data.numpy()
# 转换日期格式
dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]
​
# 创建一个表格来存日期和其对应的标签数值
true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})
​
# 同理，再创建一个来存日期和其对应的模型预测值
months = features[:, feature_list.index('month')]
days = features[:, feature_list.index('day')]
years = features[:, feature_list.index('year')]
​
test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]
​
test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]
​
predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predict.reshape(-1)}) 
# 真实值
plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')
​
# 预测值
plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')
plt.xticks(rotation = '60'); 
plt.legend()
​
# 图名
plt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');

PyTorch 之 强大的 hub 模块和搭建神经网络进行气温预测

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