这篇文章主要介绍了PyTorch一小时掌握之神经网络气温预测篇，本文给大家介绍的非常详细，对大家的学习或工作具有一定的参考借鉴价值，需要的朋友可以参考下
概述
具体的案例描述在此就不多赘述. 同一数据集我们在机器学习里的随机森林模型中已经讨论过.
导包
import numpy as np import pandas as pd import datetime import matplotlib.pyplot as plt from pandas.plotting import register_matplotlib_converters from sklearn.preprocessing import StandardScaler import torch
数据读取
# ------------------1. 数据读取------------------ # 读取数据 data = pd.read_csv("temps.csv") # 看看数据长什么样子 print(data.head()) # 查看数据维度 print("数据维度:", data.shape) # 产看数据类型 print("数据类型:", type(data))
输出结果:
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
数据维度: (348, 9)
数据类型:
数据预处理
# ------------------2. 数据预处理------------------ # datetime 格式 dates = pd.PeriodIndex(year=data["year"], month=data["month"], day=data["day"], freq="D").astype(str) dates = [datetime.datetime.strptime(date, "%Y-%m-%d") for date in dates] print(dates[:5]) # 编码转换 data = pd.get_dummies(data) print(data.head()) # 画图 plt.style.use("fivethirtyeight") register_matplotlib_converters() # 标签 labels = np.array(data["actual"]) # 取消标签 data = data.drop(["actual"], axis= 1) print(data.head()) # 保存一下列名 feature_list = list(data.columns) # 格式转换 data_new = np.array(data) data_new = StandardScaler().fit_transform(data_new) print(data_new[:5])
输出结果:
[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)]
year month day temp_2 … week_Sun week_Thurs week_Tues week_Wed
0 2016 1 1 45 … 0 0 0 0
1 2016 1 2 44 … 0 0 0 0
2 2016 1 3 45 … 1 0 0 0
3 2016 1 4 44 … 0 0 0 0
4 2016 1 5 41 … 0 0 1 0[5 rows x 15 columns]
year month day temp_2 … week_Sun week_Thurs week_Tues week_Wed
0 2016 1 1 45 … 0 0 0 0
1 2016 1 2 44 … 0 0 0 0
2 2016 1 3 45 … 1 0 0 0
3 2016 1 4 44 … 0 0 0 0
4 2016 1 5 41 … 0 0 1 0[5 rows x 14 columns]
[[ 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]
[ 0. -1.5678393 -1.54267126 -1.56929813 -1.49443549 -1.33755752
0.06187741 -0.40961596 -0.40482045 2.44131112 -0.40482045 -0.40482045
-0.41913682 -0.40482045]
[ 0. -1.5678393 -1.4285208 -1.48452388 -1.57953835 -1.32804474
-0.25855917 -0.40961596 -0.40482045 -0.40961596 2.47023092 -0.40482045
-0.41913682 -0.40482045]
[ 0. -1.5678393 -1.31437034 -1.56929813 -1.83484692 -1.31853195
-0.45082111 -0.40961596 2.47023092 -0.40961596 -0.40482045 -0.40482045
-0.41913682 -0.40482045]
[ 0. -1.5678393 -1.20021989 -1.8236209 -1.91994977 -1.30901917
-1.2198689 -0.40961596 -0.40482045 -0.40961596 -0.40482045 -0.40482045
2.38585576 -0.40482045]]
构建网络模型
# ------------------3. 构建网络模型------------------ x = torch.tensor(data_new) y = torch.tensor(labels) # 权重参数初始化 weights1 = torch.randn((14,128), dtype=float, requires_grad= True) biases1 = 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(weights1) + biases1 # 加入激活函数 hidden = torch.relu(hidden) # 预测结果 predictions = hidden.mm(weights2) + biases2 # 计算损失 loss = torch.mean((predictions - y) ** 2) # 打印损失值 if i % 100 == 0: print("loss:", loss) # 反向传播计算 loss.backward() # 更新参数 weights1.data.add_(-learning_rate * weights1.grad.data) biases1.data.add_(-learning_rate * biases1.grad.data) weights2.data.add_(-learning_rate * weights2.grad.data) biases2.data.add_(-learning_rate * biases2.grad.data) # 每次迭代清空 weights1.grad.data.zero_() biases1.grad.data.zero_() weights2.grad.data.zero_() biases2.grad.data.zero_()
loss: tensor(4746.8598, dtype=torch.float64, grad_fn=)
loss: tensor(156.5691, dtype=torch.float64, grad_fn=)
loss: tensor(148.9419, dtype=torch.float64, grad_fn=)
loss: tensor(146.1035, dtype=torch.float64, grad_fn=)
loss: tensor(144.5652, dtype=torch.float64, grad_fn=)
loss: tensor(143.5376, dtype=torch.float64, grad_fn=)
loss: tensor(142.7823, dtype=torch.float64, grad_fn=)
loss: tensor(142.2151, dtype=torch.float64, grad_fn=)
loss: tensor(141.7770, dtype=torch.float64, grad_fn=)
loss: tensor(141.4294, dtype=torch.float64, grad_fn=)
数据可视化
# ------------------4. 数据可视化------------------ def graph1(): # 创建子图 f, ax = plt.subplots(2, 2, figsize=(10, 10)) # 标签值 ax[0, 0].plot(dates, labels, color="#ADD8E6") ax[0, 0].set_xticks([""]) ax[0, 0].set_ylabel("Temperature") ax[0, 0].set_title("Max Temp") # 昨天 ax[0, 1].plot(dates, data["temp_1"], color="#87CEFA") ax[0, 1].set_xticks([""]) ax[0, 1].set_ylabel("Temperature") ax[0, 1].set_title("Previous Max Temp") # 前天 ax[1, 0].plot(dates, data["temp_2"], color="#00BFFF") ax[1, 0].set_xticks([""]) ax[1, 0].set_xlabel("Date") ax[1, 0].set_ylabel("Temperature") ax[1, 0].set_title("Two Days Prior Max Temp") # 朋友 ax[1, 1].plot(dates, data["friend"], color="#1E90FF") ax[1, 1].set_xticks([""]) ax[1, 1].set_xlabel("Date") ax[1, 1].set_ylabel("Temperature") ax[1, 1].set_title("Friend Estimate") plt.show()
输出结果:
完整代码
import numpy as np import pandas as pd import datetime import matplotlib.pyplot as plt from pandas.plotting import register_matplotlib_converters from sklearn.preprocessing import StandardScaler import torch # ------------------1. 数据读取------------------ # 读取数据 data = pd.read_csv("temps.csv") # 看看数据长什么样子 print(data.head()) # 查看数据维度 print("数据维度:", data.shape) # 产看数据类型 print("数据类型:", type(data)) # ------------------2. 数据预处理------------------ # datetime 格式 dates = pd.PeriodIndex(year=data["year"], month=data["month"], day=data["day"], freq="D").astype(str) dates = [datetime.datetime.strptime(date, "%Y-%m-%d") for date in dates] print(dates[:5]) # 编码转换 data = pd.get_dummies(data) print(data.head()) # 画图 plt.style.use("fivethirtyeight") register_matplotlib_converters() # 标签 labels = np.array(data["actual"]) # 取消标签 data = data.drop(["actual"], axis= 1) print(data.head()) # 保存一下列名 feature_list = list(data.columns) # 格式转换 data_new = np.array(data) data_new = StandardScaler().fit_transform(data_new) print(data_new[:5]) # ------------------3. 构建网络模型------------------ x = torch.tensor(data_new) y = torch.tensor(labels) # 权重参数初始化 weights1 = torch.randn((14,128), dtype=float, requires_grad= True)<mark style="color:transparent">来源gaodaimacom搞#代%码网</mark> biases1 = 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(weights1) + biases1 # 加入激活函数 hidden = torch.relu(hidden) # 预测结果 predictions = hidden.mm(weights2) + biases2 # 计算损失 loss = torch.mean((predictions - y) ** 2) # 打印损失值 if i % 100 == 0: print("loss:", loss) # 反向传播计算 loss.backward() # 更新参数 weights1.data.add_(-learning_rate * weights1.grad.data) biases1.data.add_(-learning_rate * biases1.grad.data) weights2.data.add_(-learning_rate * weights2.grad.data) biases2.data.add_(-learning_rate * biases2.grad.data) # 每次迭代清空 weights1.grad.data.zero_() biases1.grad.data.zero_() weights2.grad.data.zero_() biases2.grad.data.zero_() # ------------------4. 数据可视化------------------ def graph1(): # 创建子图 f, ax = plt.subplots(2, 2, figsize=(10, 10)) # 标签值 ax[0, 0].plot(dates, labels, color="#ADD8E6") ax[0, 0].set_xticks([""]) ax[0, 0].set_ylabel("Temperature") ax[0, 0].set_title("Max Temp") # 昨天 ax[0, 1].plot(dates, data["temp_1"], color="#87CEFA") ax[0, 1].set_xticks([""]) ax[0, 1].set_ylabel("Temperature") ax[0, 1].set_title("Previous Max Temp") # 前天 ax[1, 0].plot(dates, data["temp_2"], color="#00BFFF") ax[1, 0].set_xticks([""]) ax[1, 0].set_xlabel("Date") ax[1, 0].set_ylabel("Temperature") ax[1, 0].set_title("Two Days Prior Max Temp") # 朋友 ax[1, 1].plot(dates, data["friend"], color="#1E90FF") ax[1, 1].set_xticks([""]) ax[1, 1].set_xlabel("Date") ax[1, 1].set_ylabel("Temperature") ax[1, 1].set_title("Friend Estimate") plt.show() if __name__ == "__main__": graph1()
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