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

torch.cuda.is_available（）返回False解决方案

torch.cuda.is_available() 是一个重要的函数，用于检查当前系统是否能够使用CUDA，即NVIDIA的GPU加速库。当这个函数返回 False 时，意味着你的程序将无法在GPU上运行，而会退化到使用CPU。以下是一些可能导致 ...

one hot编码：torch.Tensor.scatter_()函数用法详解

torch.Tensor.scatter_()是torch.gather()函数的方向反向操作。两个函数可以看成一对兄弟函数。gather用来解码one hot，scatter_用来编码one hot。 scatter_(dim, index, src) → Tensor dim (python:int) – 用来...

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...

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]) +...

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....

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...

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=...

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...

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)...

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...

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 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 * ...

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...

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) ...

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）。...

运用Pysyft框架syft版本0.2.4，torchvision版本0.5.0，torch版本1.4.0，以https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv作为数据集，编写一个联邦学习差分隐私保护的线性回归模型

model.weight.data += torch.normal(torch.zeros_like(model.weight.data), epsilon) model.bias.data += torch.normal(torch.zeros_like(model.bias.data), epsilon) model = model.fix_precision().share(X_...

写一个python Flask销售预测系统，系统有一个suanfa.py文件，该文件的作用：先读取shuju.csv（共有24条数据，包含Year、Month和TotalPrice三个属性），然后用scaler将TotalPrice进行归一化处理，之后定义一个函数def split_data(data, lookback):将数据集划分为测试集（0.2）和训练集（0.8），data_raw = data.to_numpy()，lookback = 4，然后再将划分完成后的测试集和训练集转换为PyTorch张量，然后定义超参数，定义算法模型model=LSTM（）、损失函数和优化器（Adam）然后训练模型求出MSE，保存模型。有一个predict.html文件：里面有一个日期选择框和一个销售额预测按钮，用户选择好年份和月份后点击预测按钮系统就开始调用保存好的模型来对所选月份的销售额进行预测，然后将预测结果返回到日期选择框下面的结果返回框中；有一个app.py文件：定义路径。用flask和bootstrap、LayUI写出完整详细代码

row = round(0.8 * result.shape[0]) train = result[:int(row), :] np.random.shuffle(train) x_train = train[:, :-1] y_train = train[:, -1][:, -1] x_test = result[int(row):, :-1] y_test = result...

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运用Pysyft框架syft版本0.2.4，torchvision版本0.5.0，torch版本1.4.0，以https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv作为数据集，编写一个联邦学习差分隐私保护的线性回归模型

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