y_true = T[np.arange(T.shape[0]), T.astype(int)]

这行代码中，使用了NumPy库中的高级索引方法，用于对T数组进行索引和切片。

np.arange(T.shape[0])返回一个由0到T.shape[0]-1的整数组成的数组，用于指定第一个维度上的索引。

T.astype(int)将T数组中的元素转换为整型，用于指定第二个维度上的索引。

因此，这行代码的作用是使用T数组的值作为索引，从T数组中获取对应的元素，并将结果保存到y_true数组中。需要注意的是，T数组的形状必须满足高级索引的要求，即每个维度上的索引数组必须具有相同的形状。

相关问题

D:\pythonProject1\main.py:144: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.imshow(cm, cmap=plt.cm.Blues) D:\pythonProject1\main.py:154: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.show() D:\pythonProject1\main.py:185: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations y_true = y_test[np.arange(y_test.shape[0]), y.astype(np.str)] Traceback (most recent call last): File "D:\pythonProject1\main.py", line 185, in <module> y_true = y_test[np.arange(y_test.shape[0]), y.astype(np.str)] IndexError: arrays used as indices must be of integer (or boolean) type

这是一个Python程序运行时出现的警告和异常。警告信息提示FigureCanvas对象将在未来版本中不再支持属性required_interactive_framework，而异常信息则指出索引数组y的数据类型必须是整数或布尔类型。

警告信息可以忽略，因为它只是提醒这个属性即将被删除，不会影响程序的执行。但是，异常信息需要解决。根据异常信息，可以看出y数组的数据类型不正确，需要将其转换为整数类型。可以使用astype()函数将y数组的数据类型转换为整数类型，例如：

y_true = y_test[np.arange(y_test.shape[0]), y.astype(int)]

这样就可以避免这个异常了。另外，还可以将代码中的np.str改为str，以避免出现DeprecationWarning警告。

import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn import pdb from torch.autograd import Variable import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) # 对训练集测试集划分,划分比例0.8 train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): # 补充forward函数 out, h = self.rnn(x) out = self.linear(out[0]) # print("output的形状", out.shape) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()画出预测值真实值图

抱歉，我作为一个语言模型，无法在这里画图。不过，你可以通过以下代码来画出预测值和真实值的图：

net.eval()
train_predict = net(data_X)
data_predict = train_predict.data.numpy()
dataY_plot = data_Y.reshape(-1)
data_predict_plot = data_predict.reshape(-1)

plt.plot(dataY_plot, label='real')
plt.plot(data_predict_plot, label='predict')
plt.legend()
plt.show()