我想利用深度神经网络对高光谱图像进行分类。但每次我运行这段代码时,它都会给我一个错误“TypeError:forward()缺少1个必需的位置参数:'负'”。 代码显示如下(未完成):
import numpy as np
import scipy.io as sio
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import math
REBUILD_DATA = True
要读取数据:
class DATA():
# 读取样本和标签,并转换为numpy数组格式
Pavia = sio.loadmat('G:\研究生\Matlab_code\dataset\Classification\paviaU.mat')
PaviaGT = sio.loadmat('G:\研究生\Matlab_code\dataset\Classification\paviaU_GT.mat')
# print(sorted(Pavia.keys())) 返回字典中的键值key()
# print(sorted(PaviaGT.keys()))
Sample = Pavia['data']
Sample = np.array(Sample, dtype = np.int32)
Label = PaviaGT['groundT']
Label = np.array(Label, dtype = np.int32)
# 将样本每一维度的数值存到a,b,c中,以便后续使用
[a,b,c]=Sample.shape
# 将数据reshape成matlab中的格式
SampleT = Sample.transpose(1, 0, 2)
SampleX = SampleT.reshape(-1,103)
""" sio.savemat('G:\研究生\Sample.mat',{'dataX':SampleX}) """
LabelT = Label.transpose(1,0)
Label = LabelT.reshape(-1,1)
# 如何将样本和标签合并,输入神经网络的数据为[-1,band]
""" sio.savemat('G:\研究生\Label.mat',{'LabelX':Label}) """
totalcount = np.zeros((10,1),dtype = np.int32)
trainset = []
testset = []
# 将样本和标签合并
def integrated_data(self):
rebuilddata = []
for i in range(0,self.a*self.b):
rebuilddata.append([np.array(self.SampleX[i]),np.array(self.Label[i])])
for j in range(0,10):
if self.Label[i] == j:
self.totalcount[j] += 1
rebuilddata = np.array(rebuilddata)
return rebuilddata
# 并制作训练和测试数据
def make_trainset_and_testset(self, rebuilddata, ratio):
TrainIndex = []
TestIndex = []
# 取出每一类的训练集和测试集坐标
for i in range(1,np.max(self.Label)+1):
class_coor = np.argwhere(self.Label == i)
index = class_coor[:,0].tolist()
np.random.shuffle(index)
VAL_SIZE = int(np.floor(len(index)*ratio))
ClassTrainIndex = index[:VAL_SIZE]
ClassTestIndex = index[-VAL_SIZE:]
TrainIndex += ClassTrainIndex
TestIndex += ClassTestIndex
# 返回训练集和测试集样本
TrainSample = rebuilddata[TrainIndex]
TestSample = rebuilddata[TestIndex]
return TrainIndex,TestIndex,TrainSample,TestSample
这是我的dnn模块:
class DNN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(103, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 9)
def forward(self,x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return F.softmax(x,dim=1)
培训和测试功能:
def train(dnn):
BATCH_SIZE = 100
EPOCHS = 3
for epoch in range(EPOCHS):
for i in tqdm(range(0, len(train_X), BATCH_SIZE)):
batch_X = train_X[i:i+BATCH_SIZE]
batch_y = train_y[i:i+BATCH_SIZE]
dnn.zero_grad()
outputs = dnn(batch_X)
loss = loss_function(outputs, batch_y)
loss.backward()
optimizer.step()
print(loss)
def test(net):
correct = 0
total = 0
with torch.no_grad():
for i in tqdm(range(len(test_X))):
real_class = torch.argmax(test_y[i]).to(device)
net_out = dnn(test_X[i].view(-1, 1, 50, 50).to(device))[0]
predicted_class = torch.argmax(net_out)
if predicted_class == real_class:
correct += 1
total += 1
print("Accuracy:", round(correct/total,3))
if REBUILD_DATA:
Data = DATA()
datay = Data.integrated_data()
Trainindex, Testindex, TrainSet, TestSet = Data.make_trainset_and_testset(rebuilddata=datay,ratio=0.1)
train_X = torch.Tensor([i[0] for i in TrainSet])
train_y = torch.Tensor([i[1] for i in TrainSet])
train_X = train_X/3000
test_X = torch.Tensor([i[0] for i in TestSet])
test_y = torch.Tensor([i[1] for i in TestSet])
print(train_X[0])
dnn = DNN()
optimizer = optim.SGD(dnn.parameters(), lr = 0.001)
loss_function = nn.TripletMarginLoss()
train(dnn)
您正在使用^{} 作为损失函数。
此特定损失函数需要三个输入来计算损失:
anchor
、positive
和negative
。您的代码只传递两个参数
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