为什么model训练了，可是无法预测？

Question

import pandas as pd
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import torch
from torch.autograd import Variable

import matplotlib.pyplot as plt

if __name__ == '__main__':
    boston = load_boston()
    col_names = ['feature_{}'.format(i) for i in range(boston['data'].shape[1])]
    df_full = pd.DataFrame(boston['data'], columns=col_names)

    scalers_dict = {}
    for col in col_names:
        scaler = StandardScaler()
        df_full[col] = scaler.fit_transform(df_full[col].values.reshape(-1, 1))
        scalers_dict[col] = scaler

    x_train, x_test, y_train, y_test = train_test_split(df_full.values, boston['target'], test_size=0.2, random_state=2)

    model = torch.nn.Sequential(torch.nn.Linear(x_train.shape[1], 1), torch.nn.ReLU())

    criterion = torch.nn.MSELoss(reduction='mean')
    optimizer = torch.optim.Adam(model.parameters(), lr=0.1)

    n_epochs = 2000

    train_loss = []
    test_loss = []
    x_train = Variable(torch.from_numpy(x_train).float(), requires_grad=True)
    y_train = Variable(torch.from_numpy(y_train).float())

    for epoch in range(n_epochs):
        y_hat = model(x_train)
        loss = criterion(y_hat, y_train)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        epoch_loss = loss.data ** (1/2)
        train_loss.append(epoch_loss)
        if (epoch + 1) % 250 == 0:
            print("{}:loss = {}".format(epoch + 1, epoch_loss))

    order = y_train.argsort()
    y_train = y_train[order]
    x_train = x_train[order, :]

    model.eval()

    predicted = model(x_train).detach().numpy()
    actual = y_train.numpy()
    print('predicted:", predicted[:5].flatten(), actual[:5])
    plt.plot(predicted.flatten(), 'r-', label='predicted')
    plt.plot(actual, 'g-', label='actual')
    plt.show()

前面训练都可以，loss也是稳步下降，可是用model将训练数据测试显示，画到matplotlib上时和真实值对比时，就发现，预测值是一条水平线，而真实值是一条折线，完全不匹配？

Answer 1

首先，你的网络模型太浅，一层全连接很难回归一个13维的数据。我加了两层，取得了很好的效果。再者，你的y_train输入有问题，维度应该是（404,1）而不是（404，）。经过更正以后，预测值和真实值非常接近。如果不明白，可以研究我更正后的代码。

import pandas as pd
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import torch
from torch.autograd import Variable

import matplotlib.pyplot as plt

if __name__ == '__main__':
    boston = load_boston()
    col_names = ['feature_{}'.format(i) for i in range(boston['data'].shape[1])]
    df_full = pd.DataFrame(boston['data'], columns=col_names)

    scalers_dict = {}
    for col in col_names:
        scaler = StandardScaler()
        df_full[col] = scaler.fit_transform(df_full[col].values.reshape(-1, 1))
        scalers_dict[col] = scaler

    x_train, x_test, y_train, y_test = train_test_split(df_full.values, boston['target'], test_size=0.2, random_state=2)

    # model = torch.nn.Sequential(torch.nn.Linear(x_train.shape[1], 1), torch.nn.ReLU())
    model = torch.nn.Sequential(
        torch.nn.Linear(13, 20),
        torch.nn.ReLU(),
        torch.nn.Linear(20, 20),
        torch.nn.ReLU(),
        torch.nn.Linear(20, 1)
    )

    criterion = torch.nn.MSELoss(reduction='mean')
    optimizer = torch.optim.Adam(model.parameters(), lr=0.05)

    n_epochs = 20000

    train_loss = []
    test_loss = []

    x_train = Variable(torch.from_numpy(x_train).float(), requires_grad=False)
    y_train = Variable(torch.from_numpy(y_train).float())

    y_train = y_train.reshape(-1, 1)

    for epoch in range(n_epochs):
        prediction = model(x_train)
        loss = criterion(prediction, y_train)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        epoch_loss = loss.data ** (1 / 2)
        # train_loss.append(epoch_loss)
        if (epoch + 1) % 250 == 0:
            print("{}:loss = {}".format(epoch + 1, epoch_loss))


    order = y_train.flatten().argsort()
    y_train = y_train.flatten()[order]
    x_train = x_train[order, :]

    model.eval()

    predicted = model(x_train).detach().numpy()
    actual = y_train.numpy()
    print('predicted:', predicted[:5].flatten(), actual[:5])
    plt.plot(predicted.flatten(), 'r-', label='predicted')
    plt.plot(actual.flatten(), 'g-', label='actual')
    plt.ioff()
    plt.show()