d_loss_real = criterion(real_out, real_label)

在这段代码中，criterion是一个损失函数，用于计算模型输出与真实标签之间的差异。real_out是模型输出的结果，real_label是真实标签。d_loss_real表示模型在真实样本上的损失值。具体来说，这里使用的损失函数可能是二分类交叉熵损失函数，将真实标签看作类别1，将负样本标签看作类别0，计算模型输出为类别1的损失值。该损失值越小，说明模型对真实样本的判别能力越好。

相关问题

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()

F:\Python_Projects\FSA\fsa_methods.py:66: UserWarning: Casting complex values to real discards the imaginary part (Triggered internally at C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\Copy.cpp:250.) pha_unwrap = torch.fft.ifft2(torch.complex(fft_clone[:, :, :, :, 0], fft_clone[:, :, :, :, 1]), Traceback (most recent call last): File "train_ext.py", line 459, in <module> train(epoch) File "train_ext.py", line 340, in train loss_id = criterion_id(out0, labels) File "C:\Users\Tom\.conda\envs\pytorch_gpu\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "C:\Users\Tom\.conda\envs\pytorch_gpu\lib\site-packages\torch\nn\modules\loss.py", line 1174, in forward return F.cross_entropy(input, target, weight=self.weight, File "C:\Users\Tom\.conda\envs\pytorch_gpu\lib\site-packages\torch\nn\functional.py", line 3026, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) RuntimeError: "nll_loss_forward_reduce_cuda_kernel_2d_index" not implemented for 'Int'

这个警告和错误提示给出了一些有用的信息。

警告提示你正在将复数值转换为实数值，丢失了虚数部分。这可能会导致你的结果不准确。你需要查看代码并确定是否需要保留虚数部分。

错误提示你的 `nll_loss` 函数输入有问题，它期望的张量类型应该是浮点型，而你传递了一个整型张量。你可以尝试将整型张量转换为浮点型张量。在 PyTorch 中，你可以使用 `.float()` 方法进行转换。例如：

import torch

# 创建整数类型的张量
x = torch.tensor([1, 2, 3], dtype=torch.int)

# 将整数类型的张量转换为浮点型张量
x = x.float()

# 使用 nll_loss 函数
loss = torch.nn.functional.nll_loss(x, ...)

如果你仍然遇到问题，请提供更多的上下文和代码，以便更好地帮助你解决问题。