jieshixi解释下B = out.size(0)//p # repeat重复指定维度 hidden = self.rnn(out, hidden.repeat(p,1,1,1)) labels = torch.softmax(self.classifier(hidden), 1) labels = labels.reshape(p, B, self.classes, self.ggridsz**2) # (p，64，10，49) 预测出的labels # ent(p，64，49) ent = (- labels * torch.log(labels)).sum(-2) # ent=-x*log(x)[log以e为底],sum(-2)是以-2维度相加 # cent(p，64，49) pastp是没有rnn之前的概率上面一行的labels是rnn之后的概率 cent = (- labels * torch.log(pastp[None,:,:,None])).sum(-2) kld = (cent - ent).mean(0) # (64，49) kld[mask.reshape(B, self.ggridsz**2)] = -100 locmax = kld.argmax(-1) loc = torch.stack([locmax//self.ggridsz, locmax%self.ggridsz],-1) kld = kld.reshape(B, 1, self.ggridsz, self.ggridsz)

请补全以下代码：class AttModel(nn.Module): def init(self, n_input, n_hidden, seq_len): """ n_input: 单词数量 n_hidden: hidden state维度 sequence_len: 输入文本的长度 """ super(Model, self).init() # 传入参数 self.hidden_dim = n_hidden self.input_size = n_input self.output_size = n_input self.n_layers = 1 # Global Attention机制需要使用RNN的最大Timestep数 #即需要计算当前timestep和多少timestep的相似度权重（Alignment Weight） self.max_length = 10 # 定义结构 # RNN层可参考 https://pytorch.org/docs/stable/generated/torch.nn.RNN.html self.rnn = nn.RNN(self.input_size,self.hidden_dim,self.n_layers,batch_first=True) # 注意力层-用于计算score self.attn = torch.nn.Linear(in_features=, out_features=, bias=False) # 注意力层-用于已经拼接了ct和ht后的变换。 self.w_c = torch.nn.Linear(in_features=, out_features=) # 全联接层可参考 https://pytorch.org/docs/stable/generated/torch.nn.Linear.html self.fc = nn.Linear()

n_hidden: hidden state维度 sequence_len: 输入文本的长度 """ super(AttModel, self).__init__() # 正确的调用父类的方法 self.n_input = n_input self.n_hidden = n_hidden self.seq_len = seq_len self....

import torch import torch.nn as nn # 定义序列数据 sequence_data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 定义RNN模型 class RNN1(nn.Module): def init(self, input_size, hidden_size, num_layers, output_size): super(RNN1, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.RNN(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) # 修改这里的h0初始化 lu = x.unsqueeze(0) out, _ = self.rnn(lu, h0) # 添加unsqueeze(0)来增加batch维度 out = self.fc(out[:, -1, :]) # 取序列最后一个时间步的输出 return out # 定义模型参数 input_size = 3 # 输入大小，即序列的特征维度 hidden_size = 5 # 隐藏层大小 num_layers = 1 # RNN隐层数 output_size = 1 # 输出大小 # 创建模型实例 model = RNN1(input_size, hidden_size, num_layers, output_size) # 运行模型 outputs = model(sequence_data) # 输出结果 print(outputs) 这段代码报错：RuntimeError: Expected hidden size (1, 1, 5), got [1, 3, 5]，请分析原因

因此，h0 应该被初始化为 torch.zeros(num_layers, sequence_data.size(0), hidden_size)，而不是 torch.zeros(self.num_layers, x.size(0), self.hidden_size)。此外，为了让输入数据具有 batch 维度，需要在输入...

self.rnn=nn.LSTM(input_size=1,hidden_size=32,num_layers=1)

这是一个使用 PyTorch 框架中的 nn 模块定义的 LSTM 神经网络，其中 input_size 为输入数据的特征数，hidden_size 为隐藏层的神经元数，num_layers 为 LSTM 层的数量。

import torch import torch.nn as nn import numpy as np from torch.utils.data import DataLoader, TensorDataset import pandas as pd class RNN(nn.Module): def init(self, input_size, hidden_size, output_size): super(RNN, self).init() self.hidden_size = hidden_size self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(1, x.size(0), self.hidden_size) out, _ = self.rnn(x, h0) out = self.fc(out[:, -1, :]) return out input_size = 1 hidden_size = 32 output_size = 1 model = RNN(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) df = pd.read_csv('beijing_wangjing_125_new.csv') congestion_index = df['idx'].values congestion_index_tensor= torch.tensor(congestion_index, dtype=torch.float32) print(congestion_index_tensor) for epoch in range(100): outputs = model(congestion_index_tensor) loss = criterion(outputs, congestion_index_tensor) optimizer.zero_grad() loss.backward() optimizer.step()的代码中为什么会出现张量不匹配的情况，应该怎么修改？

h0 = torch.zeros(1, x.size(0), self.hidden_size) out, _ = self.rnn(x, h0) out = self.fc(out[:, -1, :]) return out input_size = 1 hidden_size = 32 output_size = 1 model = RNN(input_size, hidden_...

class RNN: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化参数 self.Wxh = np.random.randn(hidden_size, input_size) * 0.01 # 输入层到隐藏层的权重矩阵 self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01 # 隐藏层到隐藏层的权重矩阵 self.Why = np.random.randn(output_size, hidden_size) * 0.01 # 隐藏层到输出层的权重矩阵 self.bh = np.zeros((hidden_size, 1)) # 隐藏层偏置 self.by = np.zeros((output_size, 1)) # 输出层偏置 # 初始化隐藏状态 self.h = np.zeros((hidden_size, 1)) def forward(self, x): # 更新隐藏状态 self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh) # 计算输出 y = np.dot(self.Why, self.h) + self.by # 返回输出和隐藏状态 return y, self.h def backward(self, x, y, target, learning_rate): # 计算输出误差 dy = y - target # 计算隐藏状态误差 dh = np.dot(self.Why.T, dy) * (1 - self.h ** 2) # 计算权重和偏置的梯度 dWhy = np.dot(dy, self.h.T) dby = np.sum(dy, axis=1, keepdims=True) dWxh = np.dot(dh, x.T) dWhh = np.dot(dh, self.h.T) dbh = np.sum(dh, axis=1, keepdims=True) # 更新权重和偏置 self.Why -= learning_rate * dWhy self.by -= learning_rate * dby self.Wxh -= learning_rate * dWxh self.Whh -= learning_rate * dWhh self.bh -= learning_rate * dbh 帮写一下用online_shopping_10_cats数据集训练以上模型train函数以及test函数

model = RNN(input_size, hidden_size, output_size) # 训练模型 learning_rate = 0.01 epochs = 10 train(model, train_sequences, train_labels, learning_rate, epochs) # 测试模型 test(model, test_sequences...

self.hidden_size = hidden_size啥意思

这行代码是将输入的 hidden_size 参数赋值给模型的 self.hidden_size 属性。 hidden_size 是一个在 RNN 或线性层中定义隐藏状态的维度大小的参数。通过将其赋值给模型的 self.hidden_size 属性，可以在模型...

解释这段网络features = nn.ModuleList() for hidden in n_hiddens: rnn = nn.GRU( input_size=in_size, num_layers=1, hidden_size=hidden, batch_first=True, dropout=dropout ) features.append(rnn) in_size = hidden self.features = nn.Sequential(*features)

10. in_size = hidden: 更新下一层的输入大小 in_size 为当前隐藏层大小 hidden。 11. self.features = nn.Sequential(*features): 将特征列表 features 转换为 Sequential 对象，并将其赋值给模型的 ...

# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'

checkpoint_dir = './training_checkpoints' checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") checkpoint_callback=tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_prefix, save_...

output = tf.keras.rnn.static_rnn(cell, seqTimeSteps, dtype = tf.float64)

在 TensorFlow 2.x 中，tf.keras.rnn.static_rnn 函数已经被弃用，如果想要在 TensorFlow 2.x 中使用 RNN 网络，可以使用 tf.keras.layers.RNN 类，或者使用 tf.keras.layers.SimpleRNN、tf.keras.layers....

import torch import torch.nn as nn import torch.optim as optim import numpy as np 定义基本循环神经网络模型 class RNNModel(nn.Module): def init(self, rnn_type, input_size, hidden_size, output_size, num_layers=1): super(RNNModel, self).init() self.rnn_type = rnn_type self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.encoder = nn.Embedding(input_size, hidden_size) if rnn_type == 'RNN': self.rnn = nn.RNN(hidden_size, hidden_size, num_layers) elif rnn_type == 'GRU': self.rnn = nn.GRU(hidden_size, hidden_size, num_layers) self.decoder = nn.Linear(hidden_size, output_size) def forward(self, input, hidden): input = self.encoder(input) output, hidden = self.rnn(input, hidden) output = output.view(-1, self.hidden_size) output = self.decoder(output) return output, hidden def init_hidden(self, batch_size): if self.rnn_type == 'RNN': return torch.zeros(self.num_layers, batch_size, self.hidden_size) elif self.rnn_type == 'GRU': return torch.zeros(self.num_layers, batch_size, self.hidden_size) 定义数据集 with open('汉语音节表.txt', encoding='utf-8') as f: chars = f.readline() chars = list(chars) idx_to_char = list(set(chars)) char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)]) corpus_indices = [char_to_idx[char] for char in chars] 定义超参数 input_size = len(idx_to_char) hidden_size = 256 output_size = len(idx_to_char) num_layers = 1 batch_size = 32 num_steps = 5 learning_rate = 0.01 num_epochs = 100 定义模型、损失函数和优化器 model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) 训练模型 for epoch in range(num_epochs): model.train() hidden = model.init_hidden(batch_size) loss = 0 for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps): optimizer.zero_grad() hidden = hidden.detach() output, hidden = model(X, hidden) loss = criterion(output, Y.view(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() if epoch % 10 == 0: print(f"Epoch {epoch}, Loss: {loss.item()}")请正确缩进代码

self.rnn = nn.RNN(hidden_size, hidden_size, num_layers) elif rnn_type == 'GRU': self.rnn = nn.GRU(hidden_size, hidden_size, num_layers) self.decoder = nn.Linear(hidden_size, output_size) def...

def init(self, input_size, hidden_size, output_size): super(Net, self).init() self.rnn = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out

LSTM 层的输出维度为 (batch_size, sequence_length, hidden_size)，其中 hidden_size 表示 LSTM 层的隐藏状态的维度。在 forward 方法中，我们取出 LSTM 层的最后一个时间步的输出，然后通过全连接层将其映射到输出...

def forward(self, text): non_padded_position = text != self.pad_idx lens = non_padded_position.sum(dim=1) # 获取词向量 inputs_emb = self.embedding(text) packed_embedded = nn.utils.rnn.pack_padded_sequence(inputs_emb, lens.cpu(), batch_first=True, enforce_sorted=False) # 使用lstm处理数据 _,sequence_output= self.rnn(packed_embedded ) # # 使用聚合层聚合sequence_output # batch_mean_hidden = self.average_layer(sequence_output,sequence_length) # 输出文本分类output output = self.output(sequence_output) return outputlinear(): argument 'input' (position 1) must be Tensor, not tuple

3. self.output函数的输入参数sequence_output是一个tuple，可能需要修改为batch_mean_hidden或者sequence_output[0]。建议您检查一下代码，特别是average_layer和self.output这两个函数的实现和输入...

import torch import torch.nn as nn from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator # 数据预处理 tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') counter = Counter() for (label, line) in train_iter: counter.update(tokenizer(line)) vocab = build_vocab_from_iterator([counter], specials=["<unk>"]) word2idx = dict(vocab.stoi) # 设定超参数 embedding_dim = 64 hidden_dim = 128 num_epochs = 10 batch_size = 64 # 定义模型 class RNN(nn.Module): def init(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).init() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # 初始化模型、优化器和损失函数 model = RNN(len(vocab), embedding_dim, hidden_dim) optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() # 定义数据加载器 train_iter = AG_NEWS(split='train') train_data = [] for (label, line) in train_iter: label = torch.tensor([int(label)-1]) line = torch.tensor([word2idx[word] for word in tokenizer(line)]) train_data.append((line, label)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: model.zero_grad() output = model(input) loss = criterion(output, target.squeeze()) loss.backward() optimizer.step() total_loss += loss.item() * input.size(0) print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))改错

self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) ...

self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)

self.dropout = 0.1 self.rnn_hidden = 128 self.num_layers = 2

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self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)

self.dropout = 0.1 self.rnn_hidden = 128 self.num_layers = 2

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self.rnn=nn.LSTM(input_size=1,hidden_size=32,num_layers=1)

self.hidden_size = hidden_size啥意思

解释这段网络features = nn.ModuleList() for hidden in n_hiddens: rnn = nn.GRU( input_size=in_size, num_layers=1, hidden_size=hidden, batch_first=True, dropout=dropout ) features.append(rnn) in_size = hidden self.features = nn.Sequential(*features)

# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'

output = tf.keras.rnn.static_rnn(cell, seqTimeSteps, dtype = tf.float64)

def __init__(self, input_size, hidden_size, output_size): super(Net, self).__init__() self.rnn = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out

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