苏州的建筑公司网站,高铁建设官方网站,wordpress单页插件,昆明网站制作的方法目录 DAY 35 模型可视化与推理1.三种不同的模型可视化方法#xff1a;推荐torchinfo打印summary权重分布可视化2.进度条功能#xff1a;手动和自动写法#xff0c;让打印结果更加美观3.推理的写法#xff1a;评估模式作业#xff1a;调整模型定义时的超参数#xff0c;对… 目录 DAY 35 模型可视化与推理1.三种不同的模型可视化方法推荐torchinfo打印summary权重分布可视化2.进度条功能手动和自动写法让打印结果更加美观3.推理的写法评估模式作业调整模型定义时的超参数对比下效果。 DAY 35 模型可视化与推理
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import time
import matplotlib.pyplot as pltdevice torch.device(cuda:0 if torch.cuda.is_available() else cpu)
print(f使用设备: {device})iris load_iris()
X iris.data
y iris.target
X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.2, random_state42)
scaler MinMaxScaler()
X_train scaler.fit_transform(X_train)
X_test scaler.transform(X_test)
X_train torch.FloatTensor(X_train).to(device)
y_train torch.LongTensor(y_train).to(device)
X_test torch.FloatTensor(X_test).to(device)
y_test torch.LongTensor(y_test).to(device)class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.fc1 nn.Linear(4, 10)self.relu nn.ReLU()self.fc2 nn.Linear(10, 3)def forward(self, x):out self.fc1(x)out self.relu(out)out self.fc2(out)return outmodel MLP().to(device)
criterion nn.CrossEntropyLoss()
optimizer optim.SGD(model.parameters(), lr0.01)
num_epochs 20000
losses []
start_time time.time()for epoch in range(num_epochs):outputs model(X_train)loss criterion(outputs, y_train)optimizer.zero_grad()loss.backward()optimizer.step()if (epoch 1) % 200 0:losses.append(loss.item())print(fEpoch [{epoch1}/{num_epochs}], Loss: {loss.item():.4f})if (epoch 1) % 100 0:print(fEpoch [{epoch1}/{num_epochs}], Loss: {loss.item():.4f})time_all time.time() - start_time
print(fTraining time: {time_all:.2f} seconds)plt.plot(range(len(losses)), losses)
plt.xlabel(Epoch)
plt.ylabel(Loss)
plt.title(Training Loss over Epochs)
plt.show()
使用设备: cpu
Epoch [100/20000], Loss: 1.0420
Epoch [200/20000], Loss: 0.9975
Epoch [200/20000], Loss: 0.9975
Epoch [300/20000], Loss: 0.9480
Epoch [400/20000], Loss: 0.8947
Epoch [400/20000], Loss: 0.8947
Epoch [500/20000], Loss: 0.8393
Epoch [600/20000], Loss: 0.7838
Epoch [600/20000], Loss: 0.7838
Epoch [700/20000], Loss: 0.7300
Epoch [800/20000], Loss: 0.6797
Epoch [800/20000], Loss: 0.6797
Epoch [900/20000], Loss: 0.6337
Epoch [1000/20000], Loss: 0.5927
Epoch [1000/20000], Loss: 0.5927
Epoch [1100/20000], Loss: 0.5566
Epoch [1200/20000], Loss: 0.5251
Epoch [1200/20000], Loss: 0.5251
Epoch [1300/20000], Loss: 0.4975
Epoch [1400/20000], Loss: 0.4733
Epoch [1400/20000], Loss: 0.4733
Epoch [1500/20000], Loss: 0.4518
Epoch [1600/20000], Loss: 0.4325
Epoch [1600/20000], Loss: 0.4325
Epoch [1700/20000], Loss: 0.4150
Epoch [1800/20000], Loss: 0.3990
Epoch [1800/20000], Loss: 0.3990
Epoch [1900/20000], Loss: 0.3840
Epoch [2000/20000], Loss: 0.3701
Epoch [2000/20000], Loss: 0.3701
Epoch [2100/20000], Loss: 0.3570
Epoch [2200/20000], Loss: 0.3447
Epoch [2200/20000], Loss: 0.3447
Epoch [2300/20000], Loss: 0.3329
Epoch [2400/20000], Loss: 0.3218
Epoch [2400/20000], Loss: 0.3218
Epoch [2500/20000], Loss: 0.3112
Epoch [2600/20000], Loss: 0.3011
Epoch [2600/20000], Loss: 0.3011
Epoch [2700/20000], Loss: 0.2914
Epoch [2800/20000], Loss: 0.2822
Epoch [2800/20000], Loss: 0.2822
Epoch [2900/20000], Loss: 0.2735
Epoch [3000/20000], Loss: 0.2651
Epoch [3000/20000], Loss: 0.2651
Epoch [3100/20000], Loss: 0.2572
Epoch [3200/20000], Loss: 0.2496
Epoch [3200/20000], Loss: 0.2496
Epoch [3300/20000], Loss: 0.2423
Epoch [3400/20000], Loss: 0.2354
Epoch [3400/20000], Loss: 0.2354
Epoch [3500/20000], Loss: 0.2288
Epoch [3600/20000], Loss: 0.2226
Epoch [3600/20000], Loss: 0.2226
Epoch [3700/20000], Loss: 0.2166
Epoch [3800/20000], Loss: 0.2109
Epoch [3800/20000], Loss: 0.2109
Epoch [3900/20000], Loss: 0.2054
Epoch [4000/20000], Loss: 0.2003
Epoch [4000/20000], Loss: 0.2003
Epoch [4100/20000], Loss: 0.1953
Epoch [4200/20000], Loss: 0.1906
Epoch [4200/20000], Loss: 0.1906
Epoch [4300/20000], Loss: 0.1861
Epoch [4400/20000], Loss: 0.1818
Epoch [4400/20000], Loss: 0.1818
Epoch [4500/20000], Loss: 0.1777
Epoch [4600/20000], Loss: 0.1738
Epoch [4600/20000], Loss: 0.1738
Epoch [4700/20000], Loss: 0.1700
Epoch [4800/20000], Loss: 0.1664
Epoch [4800/20000], Loss: 0.1664
Epoch [4900/20000], Loss: 0.1630
Epoch [5000/20000], Loss: 0.1597
Epoch [5000/20000], Loss: 0.1597
Epoch [5100/20000], Loss: 0.1566
Epoch [5200/20000], Loss: 0.1536
Epoch [5200/20000], Loss: 0.1536
Epoch [5300/20000], Loss: 0.1507
Epoch [5400/20000], Loss: 0.1479
Epoch [5400/20000], Loss: 0.1479
Epoch [5500/20000], Loss: 0.1452
Epoch [5600/20000], Loss: 0.1427
Epoch [5600/20000], Loss: 0.1427
Epoch [5700/20000], Loss: 0.1402
Epoch [5800/20000], Loss: 0.1379
Epoch [5800/20000], Loss: 0.1379
Epoch [5900/20000], Loss: 0.1356
Epoch [6000/20000], Loss: 0.1335
Epoch [6000/20000], Loss: 0.1335
Epoch [6100/20000], Loss: 0.1314
Epoch [6200/20000], Loss: 0.1294
Epoch [6200/20000], Loss: 0.1294
Epoch [6300/20000], Loss: 0.1274
Epoch [6400/20000], Loss: 0.1256
Epoch [6400/20000], Loss: 0.1256
Epoch [6500/20000], Loss: 0.1238
Epoch [6600/20000], Loss: 0.1220
Epoch [6600/20000], Loss: 0.1220
Epoch [6700/20000], Loss: 0.1204
Epoch [6800/20000], Loss: 0.1188
Epoch [6800/20000], Loss: 0.1188
Epoch [6900/20000], Loss: 0.1172
Epoch [7000/20000], Loss: 0.1157
Epoch [7000/20000], Loss: 0.1157
Epoch [7100/20000], Loss: 0.1143
Epoch [7200/20000], Loss: 0.1129
Epoch [7200/20000], Loss: 0.1129
Epoch [7300/20000], Loss: 0.1115
Epoch [7400/20000], Loss: 0.1102
Epoch [7400/20000], Loss: 0.1102
Epoch [7500/20000], Loss: 0.1089
Epoch [7600/20000], Loss: 0.1077
Epoch [7600/20000], Loss: 0.1077
Epoch [7700/20000], Loss: 0.1065
Epoch [7800/20000], Loss: 0.1054
Epoch [7800/20000], Loss: 0.1054
Epoch [7900/20000], Loss: 0.1043
Epoch [8000/20000], Loss: 0.1032
Epoch [8000/20000], Loss: 0.1032
Epoch [8100/20000], Loss: 0.1022
Epoch [8200/20000], Loss: 0.1012
Epoch [8200/20000], Loss: 0.1012
Epoch [8300/20000], Loss: 0.1002
Epoch [8400/20000], Loss: 0.0992
Epoch [8400/20000], Loss: 0.0992
Epoch [8500/20000], Loss: 0.0983
Epoch [8600/20000], Loss: 0.0974
Epoch [8600/20000], Loss: 0.0974
Epoch [8700/20000], Loss: 0.0965
Epoch [8800/20000], Loss: 0.0957
Epoch [8800/20000], Loss: 0.0957
Epoch [8900/20000], Loss: 0.0949
Epoch [9000/20000], Loss: 0.0941
Epoch [9000/20000], Loss: 0.0941
Epoch [9100/20000], Loss: 0.0933
Epoch [9200/20000], Loss: 0.0926
Epoch [9200/20000], Loss: 0.0926
Epoch [9300/20000], Loss: 0.0918
Epoch [9400/20000], Loss: 0.0911
Epoch [9400/20000], Loss: 0.0911
Epoch [9500/20000], Loss: 0.0904
Epoch [9600/20000], Loss: 0.0898
Epoch [9600/20000], Loss: 0.0898
Epoch [9700/20000], Loss: 0.0891
Epoch [9800/20000], Loss: 0.0885
Epoch [9800/20000], Loss: 0.0885
Epoch [9900/20000], Loss: 0.0878
Epoch [10000/20000], Loss: 0.0872
Epoch [10000/20000], Loss: 0.0872
Epoch [10100/20000], Loss: 0.0866
Epoch [10200/20000], Loss: 0.0861
Epoch [10200/20000], Loss: 0.0861
Epoch [10300/20000], Loss: 0.0855
Epoch [10400/20000], Loss: 0.0850
Epoch [10400/20000], Loss: 0.0850
Epoch [10500/20000], Loss: 0.0844
Epoch [10600/20000], Loss: 0.0839
Epoch [10600/20000], Loss: 0.0839
Epoch [10700/20000], Loss: 0.0834
Epoch [10800/20000], Loss: 0.0829
Epoch [10800/20000], Loss: 0.0829
Epoch [10900/20000], Loss: 0.0824
Epoch [11000/20000], Loss: 0.0819
Epoch [11000/20000], Loss: 0.0819
Epoch [11100/20000], Loss: 0.0815
Epoch [11200/20000], Loss: 0.0810
Epoch [11200/20000], Loss: 0.0810
Epoch [11300/20000], Loss: 0.0806
Epoch [11400/20000], Loss: 0.0802
Epoch [11400/20000], Loss: 0.0802
Epoch [11500/20000], Loss: 0.0797
Epoch [11600/20000], Loss: 0.0793
Epoch [11600/20000], Loss: 0.0793
Epoch [11700/20000], Loss: 0.0789
Epoch [11800/20000], Loss: 0.0785
Epoch [11800/20000], Loss: 0.0785
Epoch [11900/20000], Loss: 0.0781
Epoch [12000/20000], Loss: 0.0778
Epoch [12000/20000], Loss: 0.0778
Epoch [12100/20000], Loss: 0.0774
Epoch [12200/20000], Loss: 0.0770
Epoch [12200/20000], Loss: 0.0770
Epoch [12300/20000], Loss: 0.0767
Epoch [12400/20000], Loss: 0.0763
Epoch [12400/20000], Loss: 0.0763
Epoch [12500/20000], Loss: 0.0760
Epoch [12600/20000], Loss: 0.0756
Epoch [12600/20000], Loss: 0.0756
Epoch [12700/20000], Loss: 0.0753
Epoch [12800/20000], Loss: 0.0750
Epoch [12800/20000], Loss: 0.0750
Epoch [12900/20000], Loss: 0.0747
Epoch [13000/20000], Loss: 0.0744
Epoch [13000/20000], Loss: 0.0744
Epoch [13100/20000], Loss: 0.0741
Epoch [13200/20000], Loss: 0.0738
Epoch [13200/20000], Loss: 0.0738
Epoch [13300/20000], Loss: 0.0735
Epoch [13400/20000], Loss: 0.0732
Epoch [13400/20000], Loss: 0.0732
Epoch [13500/20000], Loss: 0.0729
Epoch [13600/20000], Loss: 0.0726
Epoch [13600/20000], Loss: 0.0726
Epoch [13700/20000], Loss: 0.0724
Epoch [13800/20000], Loss: 0.0721
Epoch [13800/20000], Loss: 0.0721
Epoch [13900/20000], Loss: 0.0719
Epoch [14000/20000], Loss: 0.0716
Epoch [14000/20000], Loss: 0.0716
Epoch [14100/20000], Loss: 0.0713
Epoch [14200/20000], Loss: 0.0711
Epoch [14200/20000], Loss: 0.0711
Epoch [14300/20000], Loss: 0.0709
Epoch [14400/20000], Loss: 0.0706
Epoch [14400/20000], Loss: 0.0706
Epoch [14500/20000], Loss: 0.0704
Epoch [14600/20000], Loss: 0.0702
Epoch [14600/20000], Loss: 0.0702
Epoch [14700/20000], Loss: 0.0699
Epoch [14800/20000], Loss: 0.0697
Epoch [14800/20000], Loss: 0.0697
Epoch [14900/20000], Loss: 0.0695
Epoch [15000/20000], Loss: 0.0693
Epoch [15000/20000], Loss: 0.0693
Epoch [15100/20000], Loss: 0.0691
Epoch [15200/20000], Loss: 0.0689
Epoch [15200/20000], Loss: 0.0689
Epoch [15300/20000], Loss: 0.0687
Epoch [15400/20000], Loss: 0.0685
Epoch [15400/20000], Loss: 0.0685
Epoch [15500/20000], Loss: 0.0683
Epoch [15600/20000], Loss: 0.0681
Epoch [15600/20000], Loss: 0.0681
Epoch [15700/20000], Loss: 0.0679
Epoch [15800/20000], Loss: 0.0677
Epoch [15800/20000], Loss: 0.0677
Epoch [15900/20000], Loss: 0.0675
Epoch [16000/20000], Loss: 0.0673
Epoch [16000/20000], Loss: 0.0673
Epoch [16100/20000], Loss: 0.0671
Epoch [16200/20000], Loss: 0.0670
Epoch [16200/20000], Loss: 0.0670
Epoch [16300/20000], Loss: 0.0668
Epoch [16400/20000], Loss: 0.0666
Epoch [16400/20000], Loss: 0.0666
Epoch [16500/20000], Loss: 0.0664
Epoch [16600/20000], Loss: 0.0663
Epoch [16600/20000], Loss: 0.0663
Epoch [16700/20000], Loss: 0.0661
Epoch [16800/20000], Loss: 0.0660
Epoch [16800/20000], Loss: 0.0660
Epoch [16900/20000], Loss: 0.0658
Epoch [17000/20000], Loss: 0.0656
Epoch [17000/20000], Loss: 0.0656
Epoch [17100/20000], Loss: 0.0655
Epoch [17200/20000], Loss: 0.0653
Epoch [17200/20000], Loss: 0.0653
Epoch [17300/20000], Loss: 0.0652
Epoch [17400/20000], Loss: 0.0650
Epoch [17400/20000], Loss: 0.0650
Epoch [17500/20000], Loss: 0.0649
Epoch [17600/20000], Loss: 0.0647
Epoch [17600/20000], Loss: 0.0647
Epoch [17700/20000], Loss: 0.0646
Epoch [17800/20000], Loss: 0.0645
Epoch [17800/20000], Loss: 0.0645
Epoch [17900/20000], Loss: 0.0643
Epoch [18000/20000], Loss: 0.0642
Epoch [18000/20000], Loss: 0.0642
Epoch [18100/20000], Loss: 0.0640
Epoch [18200/20000], Loss: 0.0639
Epoch [18200/20000], Loss: 0.0639
Epoch [18300/20000], Loss: 0.0638
Epoch [18400/20000], Loss: 0.0636
Epoch [18400/20000], Loss: 0.0636
Epoch [18500/20000], Loss: 0.0635
Epoch [18600/20000], Loss: 0.0634
Epoch [18600/20000], Loss: 0.0634
Epoch [18700/20000], Loss: 0.0633
Epoch [18800/20000], Loss: 0.0631
Epoch [18800/20000], Loss: 0.0631
Epoch [18900/20000], Loss: 0.0630
Epoch [19000/20000], Loss: 0.0629
Epoch [19000/20000], Loss: 0.0629
Epoch [19100/20000], Loss: 0.0628
Epoch [19200/20000], Loss: 0.0627
Epoch [19200/20000], Loss: 0.0627
Epoch [19300/20000], Loss: 0.0626
Epoch [19400/20000], Loss: 0.0624
Epoch [19400/20000], Loss: 0.0624
Epoch [19500/20000], Loss: 0.0623
Epoch [19600/20000], Loss: 0.0622
Epoch [19600/20000], Loss: 0.0622
Epoch [19700/20000], Loss: 0.0621
Epoch [19800/20000], Loss: 0.0620
Epoch [19800/20000], Loss: 0.0620
Epoch [19900/20000], Loss: 0.0619
Epoch [20000/20000], Loss: 0.0618
Epoch [20000/20000], Loss: 0.0618
Training time: 8.60 seconds1.三种不同的模型可视化方法推荐torchinfo打印summary权重分布可视化
print(model)
MLP((fc1): Linear(in_features4, out_features10, biasTrue)(relu): ReLU()(fc2): Linear(in_features10, out_features3, biasTrue)
)for name, param in model.named_parameters():print(fParameter name: {name}, Shape: {param.shape})
Parameter name: fc1.weight, Shape: torch.Size([10, 4])
Parameter name: fc1.bias, Shape: torch.Size([10])
Parameter name: fc2.weight, Shape: torch.Size([3, 10])
Parameter name: fc2.bias, Shape: torch.Size([3])import numpy as np
weight_data {}
for name, param in model.named_parameters():if weight in name:weight_data[name] param.detach().cpu().numpy()fig, axes plt.subplots(1, len(weight_data), figsize(15, 5))
fig.suptitle(Weight Distribution of Layers)for i, (name, weights) in enumerate(weight_data.items()):weights_flat weights.flatten()axes[i].hist(weights_flat, bins50, alpha0.7)axes[i].set_title(name)axes[i].set_xlabel(Weight Value)axes[i].set_ylabel(Frequency)axes[i].grid(True, linestyle--, alpha0.7)plt.tight_layout()
plt.subplots_adjust(top0.85)
plt.show()print(\n 权重统计信息 )for name, weights in weight_data.items():mean np.mean(weights)std np.std(weights)min_val np.min(weights)max_val np.max(weights)print(f{name}:)print(f 均值: {mean:.6f})print(f 标准差: {std:.6f})print(f 最小值: {min_val:.6f})print(f 最大值: {max_val:.6f})print(- * 30) 权重统计信息
fc1.weight:均值: 0.038066标准差: 0.929686最小值: -2.286270最大值: 2.450587
------------------------------
fc2.weight:均值: -0.023167标准差: 1.232054最小值: -3.803612最大值: 2.585007
------------------------------from torchsummary import summarysummary(model, input_size(4,))
----------------------------------------------------------------Layer (type) Output Shape Param #
Linear-1 [-1, 10] 50ReLU-2 [-1, 10] 0Linear-3 [-1, 3] 33Total params: 83
Trainable params: 83
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
----------------------------------------------------------------from torchinfo import summarysummary(model, input_size(4, )) Layer (type:depth-idx) Output Shape Param #MLP [3] --
├─Linear: 1-1 [10] 50
├─ReLU: 1-2 [10] --
├─Linear: 1-3 [3] 33Total params: 83
Trainable params: 83
Non-trainable params: 0
Total mult-adds (M): 0.00Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.002.进度条功能手动和自动写法让打印结果更加美观
from tqdm import tqdm
import timewith tqdm(total10) as pbar: for i in range(10):time.sleep(0.5)pbar.update(1)
100%|██████████| 10/10 [00:0500:00, 1.95it/s]from tqdm import tqdm
import timewith tqdm(total5, desc下载文件, unit个) as pbar:for i in range(5):time.sleep(1)pbar.update(1)
下载文件: 100%|██████████| 5/5 [00:0500:00, 1.01s/个]from tqdm import tqdm
import timefor i in tqdm(range(3), desc处理任务, unitepoch):time.sleep(1)
处理任务: 100%|██████████| 3/3 [00:0300:00, 1.01s/epoch]from tqdm import tqdm
import timetotal 0
with tqdm(total10, desc累加进度) as pbar:for i in range(1, 11):time.sleep(0.3)total ipbar.update(1)pbar.set_postfix({当前总和: total})
累加进度: 100%|██████████| 10/10 [00:0300:00, 3.27it/s, 当前总和55]import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import time
import matplotlib.pyplot as plt
from tqdm import tqdm device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
print(f使用设备: {device})iris load_iris()
X iris.data
y iris.target
X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.2, random_state42)
scaler MinMaxScaler()
X_train scaler.fit_transform(X_train)
X_test scaler.transform(X_test)
X_train torch.FloatTensor(X_train).to(device)
y_train torch.LongTensor(y_train).to(device)
X_test torch.FloatTensor(X_test).to(device)
y_test torch.LongTensor(y_test).to(device)class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.fc1 nn.Linear(4, 10)self.relu nn.ReLU()self.fc2 nn.Linear(10, 3)def forward(self, x):out self.fc1(x)out self.relu(out)out self.fc2(out)return outmodel MLP().to(device)
criterion nn.CrossEntropyLoss()
optimizer optim.SGD(model.parameters(), lr0.01)
num_epochs 20000
losses []
epochs []
start_time time.time()with tqdm(totalnum_epochs, desc训练进度, unitepoch) as pbar:for epoch in range(num_epochs):outputs model(X_train)loss criterion(outputs, y_train)optimizer.zero_grad()loss.backward()optimizer.step()if (epoch 1) % 200 0:losses.append(loss.item())epochs.append(epoch 1)pbar.set_postfix({Loss: f{loss.item():.4f}})if (epoch 1) % 1000 0:pbar.update(1000)if pbar.n num_epochs:pbar.update(num_epochs - pbar.n)time_all time.time() - start_time
print(fTraining time: {time_all:.2f} seconds)
使用设备: cpu训练进度: 100%|██████████| 20000/20000 [00:0800:00, 2395.41epoch/s, Loss0.0608]Training time: 8.35 seconds3.推理的写法评估模式
model.eval()with torch.no_grad():outputs model(X_test)_, predicted torch.max(outputs, 1)correct (predicted y_test).sum().item()accuracy correct / y_test.size(0)print(f测试集准确率: {accuracy * 100:.2f}%)
测试集准确率: 96.67%作业调整模型定义时的超参数对比下效果。
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import time
import matplotlib.pyplot as plt
from tqdm import tqdm device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
print(f使用设备: {device}\n)iris load_iris()
X iris.data
y iris.target
X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.2, random_state42)
scaler MinMaxScaler()
X_train scaler.fit_transform(X_train)
X_test scaler.transform(X_test)
X_train torch.FloatTensor(X_train).to(device)
y_train torch.LongTensor(y_train).to(device)
X_test torch.FloatTensor(X_test).to(device)
y_test torch.LongTensor(y_test).to(device)class MLP_Original(nn.Module):def __init__(self):super(MLP_Original, self).__init__()self.fc1 nn.Linear(4, 10)self.relu nn.ReLU()self.fc2 nn.Linear(10, 3)def forward(self, x):out self.fc1(x)out self.relu(out)out self.fc2(out)return outclass MLP_Larger(nn.Module):def __init__(self):super(MLP_Larger, self).__init__()self.fc1 nn.Linear(4, 20)self.relu nn.ReLU()self.fc2 nn.Linear(20, 10)self.fc3 nn.Linear(10, 3)def forward(self, x):out self.fc1(x)out self.relu(out)out self.fc2(out)out self.relu(out)out self.fc3(out)return outclass MLP_Smaller(nn.Module):def __init__(self):super(MLP_Smaller, self).__init__()self.fc1 nn.Linear(4, 5)self.relu nn.ReLU()self.fc2 nn.Linear(5, 3)def forward(self, x):out self.fc1(x)out self.relu(out)out self.fc2(out)return outclass MLP_Tanh(nn.Module):def __init__(self):super(MLP_Tanh, self).__init__()self.fc1 nn.Linear(4, 10)self.act nn.Tanh()self.fc2 nn.Linear(10, 3)def forward(self, x):out self.fc1(x)out self.act(out)out self.fc2(out)return outdef train_and_evaluate(model_class, optimizer_class, lr, num_epochs20000):model model_class().to(device)criterion nn.CrossEntropyLoss()optimizer optimizer_class(model.parameters(), lrlr)losses []epochs []start_time time.time()with tqdm(totalnum_epochs, descf训练 {model_class.__name__}, unitepoch) as pbar:for epoch in range(num_epochs):outputs model(X_train)loss criterion(outputs, y_train)optimizer.zero_grad()loss.backward()optimizer.step()if (epoch 1) % 200 0:losses.append(loss.item())epochs.append(epoch 1)pbar.set_postfix({Loss: f{loss.item():.4f}})if (epoch 1) % 1000 0:pbar.update(1000)if pbar.n num_epochs:pbar.update(num_epochs - pbar.n)time_all time.time() - start_timewith torch.no_grad():outputs model(X_test)_, predicted torch.max(outputs.data, 1)accuracy (predicted y_test).sum().item() / y_test.size(0)print(f{model_class.__name__} 训练时间: {time_all:.2f}秒, 测试准确率: {accuracy:.4f}\n)return epochs, losses, accuracyconfigs [(MLP_Original, optim.SGD, 0.01),(MLP_Larger, optim.SGD, 0.01),(MLP_Smaller, optim.SGD, 0.01),(MLP_Tanh, optim.SGD, 0.01),(MLP_Original, optim.Adam, 0.001),(MLP_Original, optim.SGD, 0.1),(MLP_Original, optim.SGD, 0.001)
]plt.figure(figsize(12, 8))
for config in configs:epochs, losses, accuracy train_and_evaluate(*config)plt.plot(epochs, losses, labelf{config[0].__name__} {config[1].__name__} lr{config[2]} (Acc:{accuracy:.2f}))plt.xlabel(Epoch)
plt.ylabel(Loss)
plt.title(Training Loss Comparison with Different Hyperparameters)
plt.legend()
plt.grid(True)
plt.show()
使用设备: cpu训练 MLP_Original: 100%|██████████| 20000/20000 [00:0800:00, 2347.88epoch/s, Loss0.0629]MLP_Original 训练时间: 8.52秒, 测试准确率: 0.9667训练 MLP_Larger: 100%|██████████| 20000/20000 [00:1000:00, 1848.93epoch/s, Loss0.0480]MLP_Larger 训练时间: 10.82秒, 测试准确率: 1.0000训练 MLP_Smaller: 100%|██████████| 20000/20000 [00:0800:00, 2366.75epoch/s, Loss0.1377]MLP_Smaller 训练时间: 8.45秒, 测试准确率: 0.9667训练 MLP_Tanh: 100%|██████████| 20000/20000 [00:0800:00, 2326.77epoch/s, Loss0.0646]MLP_Tanh 训练时间: 8.60秒, 测试准确率: 0.9667训练 MLP_Original: 100%|██████████| 20000/20000 [00:1300:00, 1468.79epoch/s, Loss0.0466]MLP_Original 训练时间: 13.62秒, 测试准确率: 1.0000训练 MLP_Original: 100%|██████████| 20000/20000 [00:0800:00, 2334.48epoch/s, Loss0.0468]MLP_Original 训练时间: 8.57秒, 测试准确率: 1.0000训练 MLP_Original: 100%|██████████| 20000/20000 [00:0800:00, 2334.72epoch/s, Loss0.4256]MLP_Original 训练时间: 8.57秒, 测试准确率: 0.9000浙大疏锦行
