PyTorch 逻辑回归，数据集：UCI Iris Data Set

导入依赖包

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import visdom

torch.__version__, torch.manual_seed(33)

('1.12.1+cu102', <torch._C.Generator at 0x7f753ca60e90>)

加载数据集

path = "/workspace/disk1/datasets/scalar/iris.data.csv"

data = pd.read_csv(path)
data.head(3)

len(data)

set(data["Iris-setosa"].values)

{'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'}

data_setosa = data[data["Iris-setosa"] == "Iris-setosa"]
data_versicolor = data[data["Iris-setosa"] == "Iris-versicolor"]
data_virginica = data[data["Iris-setosa"] == "Iris-virginica"]

len(data_setosa), len(data_versicolor), len(data_virginica)

(49, 50, 50)

data_using = data[data["Iris-setosa"] != "Iris-setosa"]
X = data_using.iloc[:, :-1]
Y = data_using.iloc[:, -1].replace("Iris-versicolor", 0).replace("Iris-virginica", 1)
Y.unique()

array([0, 1])

X = torch.from_numpy(X.values).type(torch.float32)
X.shape

torch.Size([100, 4])

Y = torch.from_numpy(Y.values.reshape(-1, 1)).type(torch.float32)
Y.shape

torch.Size([100, 1])

train_set_ratio = 0.8
train_set_num = int(train_set_ratio * X.shape[0])
X_train = X[:train_set_num]
Y_train = Y[:train_set_num]
X_test = X[train_set_num:]
Y_test = Y[train_set_num:]

model = nn.Sequential(
    nn.Linear(in_features=X.shape[1], out_features=Y.shape[1]),
    nn.Sigmoid(),
)

model

Sequential(
  (0): Linear(in_features=4, out_features=1, bias=True)
  (1): Sigmoid()
)

loss_fn = nn.BCELoss()

optimizer = optim.Adam(model.parameters(), lr=0.001)

batch = 20
num_of_batch = X_train.shape[0] // batch
epochs = 20000

# 使用 pytorch 可视化模块 visdom 可视化训练损失变化情况
viz = visdom.Visdom(
    server="http://localhost",
    port=8097,
    base_url="/visdom",
    username="jinzhongxu",
    password="123123",
)
win = "logistic regression loss"
opts = dict(
    title="train_losses",
    xlabel="epoch",
    ylabel="loss",
    markers=True,
    legend=["loss", "acc"],
)
viz.line(
    [
        [
            0.0,
            0.0,
        ]
    ],
    [0.0],
    win=win,
    opts=opts,
)

Setting up a new session...





'logistic regression loss'

for epoch in range(epochs):
    losses = []
    for n in range(num_of_batch):
        start = n * batch
        end = (n + 1) * batch
        x = X_train[start:end]
        y = Y_train[start:end]
        y_pred = model(x)
        loss = loss_fn(y_pred, y)
        losses.append(loss.data)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

if epoch % 100 == 0:
        acc = (
            (model(X_test).data.numpy() > 0.5).astype(np.int8) == Y_test.numpy()
        ).mean()
        print(
            f"epoch: {str(epoch).rjust(len(str(epochs)), '0')}, loss:{np.mean(losses):.5f}, acc: {acc:.3f}"
        )
        viz.line(
            [
                [
                    np.mean(losses),
                    acc,
                ]
            ],
            [epoch],
            win=win,
            update="append",
        )

epoch: 00000, loss:0.81945, acc: 0.000
epoch: 00100, loss:0.72729, acc: 0.000
epoch: 00200, loss:0.71364, acc: 0.000
epoch: 00300, loss:0.70016, acc: 0.000
epoch: 00400, loss:0.68695, acc: 0.000
epoch: 00500, loss:0.67402, acc: 0.000
epoch: 00600, loss:0.66138, acc: 0.000
epoch: 00700, loss:0.64902, acc: 0.000
epoch: 00800, loss:0.63694, acc: 0.000
epoch: 00900, loss:0.62514, acc: 0.000
epoch: 01000, loss:0.61361, acc: 0.000
epoch: 01100, loss:0.60233, acc: 0.000
epoch: 01200, loss:0.59132, acc: 0.100
epoch: 01300, loss:0.58056, acc: 0.250
epoch: 01400, loss:0.57005, acc: 0.350
epoch: 01500, loss:0.55979, acc: 0.400
epoch: 01600, loss:0.54976, acc: 0.450
epoch: 01700, loss:0.53998, acc: 0.450
epoch: 01800, loss:0.53042, acc: 0.500
epoch: 01900, loss:0.52110, acc: 0.600
epoch: 02000, loss:0.51199, acc: 0.650
epoch: 02100, loss:0.50311, acc: 0.650
epoch: 02200, loss:0.49444, acc: 0.700
epoch: 02300, loss:0.48598, acc: 0.700
epoch: 02400, loss:0.47773, acc: 0.700
epoch: 02500, loss:0.46968, acc: 0.700
epoch: 02600, loss:0.46183, acc: 0.700
epoch: 02700, loss:0.45417, acc: 0.700
epoch: 02800, loss:0.44670, acc: 0.700
epoch: 02900, loss:0.43942, acc: 0.700
epoch: 03000, loss:0.43232, acc: 0.700
epoch: 03100, loss:0.42539, acc: 0.700
epoch: 03200, loss:0.41863, acc: 0.700
epoch: 03300, loss:0.41204, acc: 0.700
epoch: 03400, loss:0.40561, acc: 0.750
epoch: 03500, loss:0.39935, acc: 0.800
epoch: 03600, loss:0.39324, acc: 0.800
epoch: 03700, loss:0.38728, acc: 0.800
epoch: 03800, loss:0.38146, acc: 0.800
epoch: 03900, loss:0.37579, acc: 0.800
epoch: 04000, loss:0.37026, acc: 0.800
epoch: 04100, loss:0.36487, acc: 0.800
epoch: 04200, loss:0.35961, acc: 0.800
epoch: 04300, loss:0.35447, acc: 0.800
epoch: 04400, loss:0.34947, acc: 0.800
epoch: 04500, loss:0.34458, acc: 0.800
epoch: 04600, loss:0.33982, acc: 0.800
epoch: 04700, loss:0.33516, acc: 0.800
epoch: 04800, loss:0.33063, acc: 0.800
epoch: 04900, loss:0.32620, acc: 0.850
epoch: 05000, loss:0.32187, acc: 0.850
epoch: 05100, loss:0.31765, acc: 0.850
epoch: 05200, loss:0.31353, acc: 0.850
epoch: 05300, loss:0.30951, acc: 0.850
epoch: 05400, loss:0.30559, acc: 0.850
epoch: 05500, loss:0.30175, acc: 0.850
epoch: 05600, loss:0.29801, acc: 0.850
epoch: 05700, loss:0.29435, acc: 0.850
epoch: 05800, loss:0.29078, acc: 0.850
epoch: 05900, loss:0.28729, acc: 0.850
epoch: 06000, loss:0.28388, acc: 0.850
epoch: 06100, loss:0.28055, acc: 0.850
epoch: 06200, loss:0.27730, acc: 0.850
epoch: 06300, loss:0.27412, acc: 0.850
epoch: 06400, loss:0.27101, acc: 0.850
epoch: 06500, loss:0.26797, acc: 0.850
epoch: 06600, loss:0.26500, acc: 0.850
epoch: 06700, loss:0.26209, acc: 0.850
epoch: 06800, loss:0.25925, acc: 0.850
epoch: 06900, loss:0.25647, acc: 0.850
epoch: 07000, loss:0.25376, acc: 0.850
epoch: 07100, loss:0.25110, acc: 0.850
epoch: 07200, loss:0.24850, acc: 0.850
epoch: 07300, loss:0.24596, acc: 0.850
epoch: 07400, loss:0.24347, acc: 0.900
epoch: 07500, loss:0.24103, acc: 0.900
epoch: 07600, loss:0.23865, acc: 0.900
epoch: 07700, loss:0.23631, acc: 0.900
epoch: 07800, loss:0.23403, acc: 0.900
epoch: 07900, loss:0.23179, acc: 0.900
epoch: 08000, loss:0.22960, acc: 0.900
epoch: 08100, loss:0.22746, acc: 0.950
epoch: 08200, loss:0.22535, acc: 0.950
epoch: 08300, loss:0.22330, acc: 0.950
epoch: 08400, loss:0.22128, acc: 0.950
epoch: 08500, loss:0.21930, acc: 0.950
epoch: 08600, loss:0.21737, acc: 0.950
epoch: 08700, loss:0.21547, acc: 0.950
epoch: 08800, loss:0.21361, acc: 0.950
epoch: 08900, loss:0.21179, acc: 0.950
epoch: 09000, loss:0.21000, acc: 0.950
epoch: 09100, loss:0.20825, acc: 0.950
epoch: 09200, loss:0.20653, acc: 0.950
epoch: 09300, loss:0.20484, acc: 0.950
epoch: 09400, loss:0.20319, acc: 0.950
epoch: 09500, loss:0.20157, acc: 0.950
epoch: 09600, loss:0.19998, acc: 0.950
epoch: 09700, loss:0.19842, acc: 0.950
epoch: 09800, loss:0.19689, acc: 0.950
epoch: 09900, loss:0.19538, acc: 0.950
epoch: 10000, loss:0.19391, acc: 0.950
epoch: 10100, loss:0.19246, acc: 0.950
epoch: 10200, loss:0.19104, acc: 0.950
epoch: 10300, loss:0.18964, acc: 0.950
epoch: 10400, loss:0.18827, acc: 0.950
epoch: 10500, loss:0.18693, acc: 0.950
epoch: 10600, loss:0.18561, acc: 0.950
epoch: 10700, loss:0.18431, acc: 0.950
epoch: 10800, loss:0.18303, acc: 0.950
epoch: 10900, loss:0.18178, acc: 0.950
epoch: 11000, loss:0.18055, acc: 0.950
epoch: 11100, loss:0.17934, acc: 0.950
epoch: 11200, loss:0.17815, acc: 0.950
epoch: 11300, loss:0.17698, acc: 0.950
epoch: 11400, loss:0.17583, acc: 0.950
epoch: 11500, loss:0.17471, acc: 0.950
epoch: 11600, loss:0.17360, acc: 0.950
epoch: 11700, loss:0.17250, acc: 0.950
epoch: 11800, loss:0.17143, acc: 0.950
epoch: 11900, loss:0.17038, acc: 0.950
epoch: 12000, loss:0.16934, acc: 0.950
epoch: 12100, loss:0.16832, acc: 0.950
epoch: 12200, loss:0.16731, acc: 0.950
epoch: 12300, loss:0.16632, acc: 0.950
epoch: 12400, loss:0.16535, acc: 0.950
epoch: 12500, loss:0.16440, acc: 0.950
epoch: 12600, loss:0.16345, acc: 0.950
epoch: 12700, loss:0.16253, acc: 0.950
epoch: 12800, loss:0.16162, acc: 0.950
epoch: 12900, loss:0.16072, acc: 0.950
epoch: 13000, loss:0.15983, acc: 0.950
epoch: 13100, loss:0.15896, acc: 0.950
epoch: 13200, loss:0.15811, acc: 0.950
epoch: 13300, loss:0.15726, acc: 0.950
epoch: 13400, loss:0.15643, acc: 0.950
epoch: 13500, loss:0.15561, acc: 0.950
epoch: 13600, loss:0.15481, acc: 0.950
epoch: 13700, loss:0.15401, acc: 0.950
epoch: 13800, loss:0.15323, acc: 0.950
epoch: 13900, loss:0.15246, acc: 0.950
epoch: 14000, loss:0.15170, acc: 0.950
epoch: 14100, loss:0.15095, acc: 0.950
epoch: 14200, loss:0.15022, acc: 0.950
epoch: 14300, loss:0.14949, acc: 0.950
epoch: 14400, loss:0.14877, acc: 0.950
epoch: 14500, loss:0.14807, acc: 0.950
epoch: 14600, loss:0.14737, acc: 0.950
epoch: 14700, loss:0.14669, acc: 0.950
epoch: 14800, loss:0.14601, acc: 0.950
epoch: 14900, loss:0.14534, acc: 0.950
epoch: 15000, loss:0.14468, acc: 0.950
epoch: 15100, loss:0.14404, acc: 0.950
epoch: 15200, loss:0.14340, acc: 0.950
epoch: 15300, loss:0.14276, acc: 0.950
epoch: 15400, loss:0.14214, acc: 0.950
epoch: 15500, loss:0.14153, acc: 0.950
epoch: 15600, loss:0.14092, acc: 0.950
epoch: 15700, loss:0.14032, acc: 0.950
epoch: 15800, loss:0.13973, acc: 0.950
epoch: 15900, loss:0.13915, acc: 0.950
epoch: 16000, loss:0.13858, acc: 0.950
epoch: 16100, loss:0.13801, acc: 0.950
epoch: 16200, loss:0.13745, acc: 0.950
epoch: 16300, loss:0.13690, acc: 0.950
epoch: 16400, loss:0.13635, acc: 0.950
epoch: 16500, loss:0.13581, acc: 0.950
epoch: 16600, loss:0.13528, acc: 0.950
epoch: 16700, loss:0.13476, acc: 0.950
epoch: 16800, loss:0.13424, acc: 0.950
epoch: 16900, loss:0.13373, acc: 0.950
epoch: 17000, loss:0.13322, acc: 0.950
epoch: 17100, loss:0.13272, acc: 0.950
epoch: 17200, loss:0.13223, acc: 0.950
epoch: 17300, loss:0.13174, acc: 0.950
epoch: 17400, loss:0.13126, acc: 0.950
epoch: 17500, loss:0.13079, acc: 0.950
epoch: 17600, loss:0.13032, acc: 0.950
epoch: 17700, loss:0.12985, acc: 0.950
epoch: 17800, loss:0.12940, acc: 0.950
epoch: 17900, loss:0.12894, acc: 0.950
epoch: 18000, loss:0.12850, acc: 0.950
epoch: 18100, loss:0.12805, acc: 0.950
epoch: 18200, loss:0.12762, acc: 0.950
epoch: 18300, loss:0.12719, acc: 0.950
epoch: 18400, loss:0.12676, acc: 0.950
epoch: 18500, loss:0.12634, acc: 0.950
epoch: 18600, loss:0.12592, acc: 0.950
epoch: 18700, loss:0.12551, acc: 0.950
epoch: 18800, loss:0.12510, acc: 0.950
epoch: 18900, loss:0.12470, acc: 0.950
epoch: 19000, loss:0.12430, acc: 0.950
epoch: 19100, loss:0.12390, acc: 0.950
epoch: 19200, loss:0.12351, acc: 0.950
epoch: 19300, loss:0.12313, acc: 0.950
epoch: 19400, loss:0.12275, acc: 0.950
epoch: 19500, loss:0.12237, acc: 0.950
epoch: 19600, loss:0.12200, acc: 0.950
epoch: 19700, loss:0.12163, acc: 0.950
epoch: 19800, loss:0.12126, acc: 0.950
epoch: 19900, loss:0.12090, acc: 0.950

model.state_dict()

OrderedDict([('0.weight', tensor([[-1.4480, -2.8220,  2.5209,  5.8656]])),
             ('0.bias', tensor([-5.3296]))])

训练损失和测试集预测准确率曲线：