def GetExpectGainData(inputDataSet): expectGainData = torch.zeros(inputDataSet.shape[0]) if not IsMatrix(inputDataSet): print('GetExpectGainData inputDataSet is not a matrix') return expectGainData trainDateArray = GetTrainDateArray(); trainData = [] for trainDate in trainDateArray: currentTrainData = MergeTargetValByCol(inputDataSet, 0, trainDate) trainData = torch.cat((trainData, currentTrainData), dim = 0) labelData = trainData[:, 2] labelData = labelData[1, :] newDayData = inputDataSet[torch.LongTensor([array1.shape[0]]) - 1] labelData = torch.cat(labelData, newDayData[2]) print(trainData) print(labelData) 000004.csv Traceback (most recent call last): File "D:\BlackOp\Source\QuantitativeTrade\Source\DataAnalyze.py", line 97, in <module> File "D:\BlackOp\Source\QuantitativeTrade\Source\DataAnalyze.py", line 39, in CalAnalyzeRst expectGainData = GetExpectGainData(inputDataSet) File "D:\BlackOp\Source\QuantitativeTrade\Source\DataAnalyze.py", line 77, in GetExpectGainData trainData = torch.cat((trainData, currentTrainData), dim = 0) TypeError: expected Tensor as element 0 in argument 0, but got list

dct = torch.fft.dct(torch.eye(256), norm='ortho') AttributeError: module 'torch.fft' has no attribute 'dct'

measurement_matrix = torch.fft.fft(torch.eye(256), dim=0).real compressed = measurement_matrix.matmul(image.flatten().unsqueeze(1)) # 使用 OMP 进行重构 reconstructed = omp(dct_matrix, compressed, k=...

请你把这段代码用def的形式改写data = torch.randn(16992, 307, 12, 2) week_feature = torch.zeros((data.shape[0], 1)) # 遍历所有时间点，获取对应的星期并将星期转化为对应的数字 for i in range(data.shape[0]): date_str = '2016-01-01 00:00:00' # 假设数据集中的时间从2016年1月1日开始 date = datetime.datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S') date += datetime.timedelta(minutes=5*i) # 每个时间点间隔5分钟 week = date.weekday() # 获取星期，0表示星期一，1表示星期二，以此类推 week_feature[i] = week + 1 # 将星期转化为对应的数字并将其存入星期特征张量中 # 将星期特征添加到原有的数据集中 week_feature=week_feature.unsqueeze(1).repeat(1, 307, 12, 1) week_feature=week_feature.reshape(16992,307,12,1) data = torch.cat((data, week_feature), dim=-1) # week_feature = week_feature.repeat(1, 307, 12, 1) # 将星期特征张量与数据集在最后一个维度上拼接 # data = torch.cat((data, week_feature), dim=-1) # 输出结果 print(data) print(data.shape) pytorch版本

week_feature = torch.zeros((data.shape[0], 1)) for i in range(data.shape[0]): date_str = '2016-01-01 00:00:00' date = datetime.datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S') date += datetime....

def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d self.conv1 = nn.Conv2d(input_nc, ndf, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 def forward(self, input): input = self.conv1(input) offset1 = self.conv_offset1(input) mask1 = torch.sigmoid(self.conv_mask1(input)) sequence1 = [ torchvision.ops.deform_conv2d(input=input, offset=offset1, weight=self.conv1.weight, mask=mask1, padding=(1, 1)) ] sequence2 = sequence1 + self.sequence2 self.model = nn.Sequential(*sequence2) nn.LeakyReLU(0.2, True) return self.model(input)，上述代码出现问题：RuntimeError: Given groups=1, weight of size [18, 512, 3, 3], expected input[1, 64, 512, 512] to have 512 channels, but got 64 channels instead，如何修改

self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) +...

import torch import torch.nn as nn import pandas as pd from sklearn.model_selection import train_test_split # 加载数据集 data = pd.read_csv('../dataset/train_10000.csv') # 数据预处理 X = data.drop('target', axis=1).values y = data['target'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() # 定义LSTM模型 class LSTMModel(nn.Module): def init(self, input_size, hidden_size, num_layers, output_size): super(LSTMModel, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out[:, -1, :]) return out # 初始化模型和定义超参数 input_size = X_train.shape[1] hidden_size = 64 num_layers = 2 output_size = 1 model = LSTMModel(input_size, hidden_size, num_layers, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 100 for epoch in range(num_epochs): model.train() outputs = model(X_train) loss = criterion(outputs, y_train) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # 在测试集上评估模型 model.eval() with torch.no_grad(): outputs = model(X_test) loss = criterion(outputs, y_test) print(f'Test Loss: {loss.item():.4f}') 我有额外的数据集CSV，请帮我数据集和测试集分离

import torch import torch.nn as nn import pandas as pd from sklearn.model_selection import train_test_split # 加载数据集 data = pd.read_csv('../dataset/train_10000.csv') # 数据预处理 X = data.drop('...

这段pytorch代码一直报错type object 'datetime.datetime' has no attribute 'datetime'，我该怎么调整呢？这是什么原因呢？最后生成一组数据让我看看修改是否可行def add_week_feature(data): data = np.load(flow_file) T_ =data['data'] week_feature = torch.zeros((T_.shape[0], 1)) for i in range(T_.shape[0]): date_str = '2016-01-01 00:00:00' date = datetime.datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S') date += datetime.timedelta(minutes=5*i) week = date.weekday() week_feature[i] = week + 1 week_feature = week_feature.unsqueeze(1).repeat(1, 307, 12, 1) week_feature = week_feature.reshape(16992,307,12,1) data= torch.cat((data, week_feature), dim=-1) return data

week_feature = torch.zeros((batch_size, seq_len, 1)) # 遍历所有时间点，获取对应的星期并将星期转化为对应的数字 for i in range(batch_size): date_str = '2016-01-01 00:00:00' # 假设数据集中的时间从...

请帮我把这段代码改成def的形式data = torch.randn(16992, 307, 12, 2) week_feature = torch.zeros((data.shape[0], 1)) # 遍历所有时间点，获取对应的星期并将星期转化为对应的数字 for i in range(data.shape[0]): date_str = '2016-01-01 00:00:00' # 假设数据集中的时间从2016年1月1日开始 date = datetime.datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S') date += datetime.timedelta(minutes=5*i) # 每个时间点间隔5分钟 week = date.weekday() # 获取星期，0表示星期一，1表示星期二，以此类推 week_feature[i] = week + 1 # 将星期转化为对应的数字并将其存入星期特征张量中 # 将星期特征添加到原有的数据集中 week_feature=week_feature.unsqueeze(1).repeat(1, 307, 12, 1) week_feature=week_feature.reshape(16992,307,12,1) data = torch.cat((data, week_feature), dim=-1) # week_feature = week_feature.repeat(1, 307, 12, 1) # 将星期特征张量与数据集在最后一个维度上拼接 # data = torch.cat((data, week_feature), dim=-1) # 输出结果 print(data) print(data.shape) ，pytorch版本

week_feature = torch.zeros((batch_size, seq_len, 1)) # 遍历所有时间点，获取对应的星期并将星期转化为对应的数字 for i in range(batch_size): date_str = '2016-01-01 00:00:00' # 假设数据集中的时间从...

1.创建一些随机数据，并将其转换为Tensor类型随机数据代码: np.random.seed(0) X = np.random.randn(100, 2) Y = np.zeros((100,)) Y[X[:,0] + X[:,1] > 0] = 1 2.定义一个逻辑回归模型（使用nn.Linear作为线性层，使用torch.sigmoid作为激活函数）； 3.定义损失函数（使用二元交叉熵损失函数）和优化器（使用随机梯度下降法）； 4.将模型训练指定轮数，每轮进行前向传播、反向传播和参数更新，并输出损失函数值； 5.输出模型参数并可视化结果。

Z = model(torch.cat((xx.reshape(-1,1), yy.reshape(-1,1)), dim=1)).detach().reshape(xx.shape) plt.contour(xx, yy, Z, levels=[0.5]) plt.show() 运行代码后，会输出每轮训练的损失函数值和最终的模型参数...

class CAM_Module(Module): """ Channel attention module""" def init(self, in_dim): super(CAM_Module, self).init() self.chanel_in = in_dim self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=-1) def forward(self,x): """ inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input feature attention: B X C X C """ m_batchsize, C, height, width, channle = x.size() #print(x.size()) proj_query = x.view(m_batchsize, C, -1) proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1) #形状转换并交换维度 energy = torch.bmm(proj_query, proj_key) energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy attention = self.softmax(energy_new) proj_value = x.view(m_batchsize, C, -1) out = torch.bmm(attention, proj_value) out = out.view(m_batchsize, C, height, width, channle) # print('out', out.shape) # print('x', x.shape) out = self.gammaout + x #CH*W return out 将其改成keras代码

self.gamma = self.add_weight(name='gamma', shape=[1], initializer='zeros', trainable=True) self.softmax = Softmax(axis=-1) def call(self, x): """ inputs : x : input feature maps( B X H X W X C)...

使用Pytorch完成逻辑回归问题 1.创建一些随机数据，并将其转换为Tensor类型随机数据代码: np.random.seed(0) X = np.random.randn(100, 2) Y = np.zeros((100,)) Y[X[:,0] + X[:,1] > 0] = 1 2.定义一个逻辑回归模型（使用nn.Linear作为线性层，使用torch.sigmoid作为激活函数）； 3.定义损失函数（使用二元交叉熵损失函数）和优化器（使用随机梯度下降法）； 4.将模型训练指定轮数，每轮进行前向传播、反向传播和参数更新，并输出损失函数值； 5.输出模型参数并可视化结果

if (epoch+1) % 100 == 0: print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item())) # 输出模型参数 print('Model parameters:') for name, param in model.named_parameters(): if ...

def evaluate(self, datloader_Test): Image_Feature_ALL = [] Image_Name = [] Sketch_Feature_ALL = [] Sketch_Name = [] start_time = time.time() self.eval() for i_batch, sanpled_batch in enumerate(datloader_Test): sketch_feature, positive_feature= self.test_forward(sanpled_batch) Sketch_Feature_ALL.extend(sketch_feature) Sketch_Name.extend(sanpled_batch['sketch_path']) for i_num, positive_name in enumerate(sanpled_batch['positive_path']): if positive_name not in Image_Name: Image_Name.append(sanpled_batch['positive_path'][i_num]) Image_Feature_ALL.append(positive_feature[i_num]) rank = torch.zeros(len(Sketch_Name)) Image_Feature_ALL = torch.stack(Image_Feature_ALL) for num, sketch_feature in enumerate(Sketch_Feature_ALL): s_name = Sketch_Name[num] sketch_query_name = '_'.join(s_name.split('/')[-1].split('_')[:-1]) position_query = Image_Name.index(sketch_query_name) distance = F.pairwise_distance(sketch_feature.unsqueeze(0), Image_Feature_ALL) target_distance = F.pairwise_distance(sketch_feature.unsqueeze(0), Image_Feature_ALL[position_query].unsqueeze(0)) rank[num] = distance.le(target_distance).sum() top1 = rank.le(1).sum().numpy() / rank.shape[0] top10 = rank.le(10).sum().numpy() / rank.shape[0] print('Time to EValuate:{}'.format(time.time() - start_time)) return top1, top10

这段代码是一个evaluate函数，用于评估模型在测试数据集上的性能。函数接受一个datloader_Test参数，该参数是一个数据加载器，用于加载测试数据集。函数首先初始化一些变量，包括用于存储图像特征、图像名称、素描...

将冒号后面的代码改写成一个nn.module类：data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

if epoch % 1 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) # 预测结果 trainPredict = model(trainX) testPredict = model(testX) trainPredict = scaler.inverse_transform(trainPredict....

将冒号后面的代码改写成一个nn.module类：import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = LSTMModel(input_size=1, hidden_size=50, output_size=1, num_layers=2, look_back=look_back).to(device) criterion = nn.MSE...

def Grad_Cam(model, image, layer_name): # 获取模型提取全链接之前的特征图 new_model = nn.Sequential(list(model.children())[:44]) print(new_model) new_model.eval() feature_maps = new_model(image) # 获取模型最后一层卷积层 target_layer = model._modules.get(layer_name) # 将模型最后一层卷积层的输出结果作为反向传播的梯度 gradient = torch.zeros(feature_maps.size()) # 返回一个形状与feature_maps相同全为标量 0 的张量 gradient[:, :, feature_maps.size()[2]//2, feature_maps.size()[3]//2] = 1 target_layer.zero_grad() # 将模型中参数的梯度置为0 feature_maps.backward(gradient=gradient) # 获取模型最后一层卷积层的输出结果和梯度 _, _, H, W = feature_maps.size() output_activations = feature_maps.detach().numpy()[0] gradients = target_layer.weight.grad.detach().numpy() # 计算特征图中每个像素点的权重 weights = np.mean(gradients, axis=(2, 3))[0] cam = np.zeros((H, W), dtype=np.float32) for i, w in enumerate(weights): cam += w output_activations[i, :, :] # 对权重进行归一化处理 cam = np.maximum(cam, 0) cam = cv2.resize(cam, (1440, 1440)) cam = cam - np.min(cam) cam = cam / np.max(cam) # 将热力图叠加到原图上 heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET) heatmap = np.float32(heatmap) / 255 image = image.detach().numpy() image = np.transpose(image, (0, 2, 3, 1)) img_CCT = cv2.imread("F:/BaiduSyncdisk/python/svm_CCT/picture CCT_CP/2L5830N023_CCT.png") img_CP = cv2.imread("F:/BaiduSyncdisk/python/svm_CCT/picture CCT_CP/2L5830N023_CP.png") img_CCT = cv2.resize(img_CCT, (1440, 1440)) img_CP = cv2.resize(img_CP, (1440, 1440)) cam_img = heatmap + np.float32(img_CCT[0]) cam_img = cam_img / np.max(cam_img) return np.uint8(255 * cam_img) 上述代码不显示热力图，怎么解决

heatmap = cv2.resize(cam, (image.shape[2], image.shape[3])) heatmap = heatmap - np.min(heatmap) heatmap = heatmap / np.max(heatmap) # 将热力图叠加到原图上 heatmap = cv2.applyColorMap(np.uint8(255 * ...

帮我看一些这段代码有什么问题：class EncoderDecoder(nn.Module): def init(self,encoder,decoder,source_embed,target_embed,generator): #encoder:代表编码器对象 #decoder:代表解码器对象 #source_embed:代表源数据的嵌入 #target_embed:代表目标数据的嵌入 #generator:代表输出部分类别生成器对象 super(EncoderDecoder,self).init() self.encoder=encoder self.decoder=decoder self.src_embed=source_embed self.tgt_embed=target_embed self.generator=generator def forward(self,source,target,source_mask,target_mask): #source:代表源数据 #target:代表目标数据 #source_mask:代表源数据的掩码张量 #target_mask:代表目标数据的掩码张量 return self.decode(self.encode(source,source_mask),source_mask, target,target_mask) def encode(self,source,source_mask): return self.encoder(self.src_embed(source),source_mask) def decode(self,memory,source_mask,target,target_mask): #memory:代表经历编码器编码后的输出张量 return self.decoder(self.tgt_embed(target),memory,source_mask,target) vocab_size=1000 d_model=512 encoder=en decoder=de source_embed=nn.Embedding(vocab_size,d_model) target_embed=nn.Embedding(vocab_size,d_model) generator=gen source=target=Variable(torch.LongTensor([[100,2,421,500],[491,998,1,221]])) source_mask=target_mask=Variable(torch.zeros(8,4,4)) ed=EncoderDecoder(encoder,decoder,source_embed,target_embed,generator ) ed_result=ed(source,target,source_mask,target_mask) print(ed_result) print(ed_result.shape)

source_mask = target_mask = torch.zeros(8, 4, 4) ed = EncoderDecoder(encoder, decoder, source_embed, target_embed, generator) ed_result = ed(source, target, source_mask, target_mask) print(ed_result) ...

LSTM的torch.nn实现

h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc...

请你用pytorch代码def数据预处理部分。原始数据形状为（16992，307，3）.16992是时间段数，307是传感器数量，3是特征维度，分别包括速度，流量和占空比。现在仅需保留速度这一个特征，即变成（16992，307）。然后为其添加度特征和星期特征，度特征根据邻接矩阵而来，星期特征根据时间段数而来。最终数据形状变成（16992，307，3），3是特征维度，分别包括速度，度和星期。请你用for循环补齐下列代码：def transform_data(data,degree,n_node): # transform from row data to formatted data len_record = data.shape[0] num_available_data = len_record - n_time - out_time x = np.zeros([num_available_data, n_time, n_node, 3])#0---speed,1---degree,2---weekday y = np.zeros([num_available_data, n_time, n_node]) 最好告诉我每行在做什么，最终return x和y，最后，生成形状为（16992，307，3）的随机原始数据集和形状为（307，307）的邻接矩阵调用一下上述函数，让我看看x，y的输出形状

print(x.shape, y.shape) 此代码中，transform_data 函数根据要求将原始数据转化为模型输入数据和标签，其中： - data：原始数据，形状为（16992，307，3）。 - degree：邻接矩阵，形状为（307，307）。...

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