传统RNN网络及其案例--人名分类
传统的RNN模型简介
RNN
先上图
这图看起来莫名其妙，想拿着跟CNN对比着学第一眼看上去有点摸不着头脑，其实我们可以把每一个时刻的图展开来，如下
其中，为了简化计算，我们默认每一个隐层参数相同，这样看来RNN的结构就比较简单了，相比较CNN来说，RNN引入了更多的时序信息。
LSTM
在训练 RNN 时，每个时间步的输出都依赖于之前时间步的状态，这种依赖关系形成了一个链式结构。当反向传播时，梯度需要通过多个时间步传播回去，由于链式法则的存在，这个过程中梯度会多次进行乘法运算。如果这些乘法运算的结果小于1，梯度就会随着时间步的增加逐渐衰减，最终可能消失到几乎为零，就会导致梯度消失。RNN 中常用的激活函数如 Sigmoid 或者 tanh 函数，它们的输出范围都在 (0, 1) 或者 (-1, 1) 之间。在反向传播时，如果梯度在这些函数的导数范围内，则可以稳定地传播；但如果超出了这个范围，梯度可能会指数级增长或减少，导致梯度爆炸。而且这些在处理长序列时特别容易发生，因此，出现了RNN的改良版，LSTM。
先看图：
谈到LSTM就无法避免的提及它的三个门和最上面的记忆单元
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记忆单元：记忆单元是LSTM的核心，用于存储信息。它可以看作是一条信息通道，贯穿整个 LSTM单元链条，允许信息直接传递，减少信息丢失。
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遗忘门：遗忘门决定哪些信息需要从记忆单元中删除。它通过sigmoid函数（将当前输入和前一时刻的隐藏状态作为输入）输出一个0到1之间的值。接近0的值表示需要遗忘的信息，接近1的值表示需要保留的信息。
\[f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f) \] -
输入门： 输入门决定哪些新的信息需要添加到记忆单元中。它由两个部分组成：一个sigmoid层决定哪些值将被更新；一个tanh层生成新的候选值向量。
\[i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i) \]\[\tilde{C}_t = \tanh(W_C \cdot [h_{t-1}, x_t] + b_C) $$ \]\[o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o) \]\[h_t = o_t * \tanh(C_t)$$ \] -
遗忘阶段：计算遗忘门的值，以确定当前记忆单元状态中需要遗忘的部分。
\[C_t = f_t * C_{t-1} \] -
输入阶段：计算输入门的值，并生成新的候选记忆内容。
\[C_t = C_t + i_t * \tilde{C}_t \] -
更新记忆单元：结合遗忘门和输入门的输出，更新当前记忆单元的状态。
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输出阶段：计算输出门的值，并生成新的隐藏状态。
完整公式流程：
GRU
LSTM固然很强，解决了RNN对于长序列模型表现很拉跨的难题，但是仔细查看LSTM的过程就会发现，相对于RNN来说他引入了太多的参数，很容易就过拟合和训练时间大大加长，因此，GRU改进这一问题
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更新门：更新门控制着前一时间步的信息和当前时间步的新信息之间的混合。它通过sigmoid函数决定有多少过去的信息需要保留，以及有多少新的信息需要添加。
\[z_t = \sigma(W_z \cdot [h_{t-1}, x_t] + b_z) \] -
重置门：重置门控制着前一时间步的隐藏状态在当前时间步中被遗忘的比例。它通过sigmoid函数决定有多少前一时间步的信息需要被重置。
\[r_t = \sigma(W_r \cdot [h_{t-1}, x_t] + b_r) \] -
候选隐藏状态：候选隐藏状态结合了重置门的结果，决定当前时间步的隐藏状态。
\[\tilde{h}_t = \tanh(W \cdot [r_t * h_{t-1}, x_t] + b) \] -
隐藏状态：最终的隐藏状态是更新门和候选隐藏状态的组合。
\[h_t = (1 - z_t) * h_{t-1} + z_t * \tilde{h}_t \]
工作流程如下：
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重置阶段：计算重置门的值，以确定前一时间步的信息在当前时间步中被重置的比例。
\[r_t = \sigma(W_r \cdot [h_{t-1}, x_t] + b_r) \] -
更新阶段：计算更新门的值，以确定有多少信息从前一时间步保留到当前时间步。
\[z_t = \sigma(W_z \cdot [h_{t-1}, x_t] + b_z) \] -
候选隐藏状态阶段：计算候选隐藏状态，该状态结合了重置门的结果和当前输入信息。
\[\tilde{h}_t = \tanh(W \cdot [r_t * h_{t-1}, x_t] + b) \] -
隐藏状态更新阶段：结合更新门和候选隐藏状态，更新当前时间步的隐藏状态。
\[h_t = (1 - z_t) * h_{t-1} + z_t * \tilde{h}_t \]完整工作流程
\[z_t = \sigma(W_z \cdot [h_{t-1}, x_t] + b_z) \]\[r_t = \sigma(W_r \cdot [h_{t-1}, x_t] + b_r) \]\[\tilde{h}_t = \tanh(W \cdot [r_t * h_{t-1}, x_t] + b) \]\[ h_t = (1 - z_t) * h_{t-1} + z_t * \tilde{h}_t $$ \]
1. 准备工作
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导入一些包和写一个读取数据的函数（这段代码不是重点，直接抄就行了，只需要记住几个关键的变量）
category_lines: 人名类别与具体人名对应关系的字典，形式为{人名类别:[人名1，人名2，...]} all_categories：所有的类别构成的列表 all_letters：所有的字符import torch from io import open import glob import os import unicodedata import string import random import time import math import torch.nn as nn import matplotlib.pyplot as plt data_path = './data/names/' all_letters = string.ascii_letters + " .,;'" def unicodeToAscii(text): """ Converts a Unicode string to an ASCII string. Args: text (str): The Unicode string to convert. Returns: str: The ASCII string. """ return ''.join([ unicodedata.normalize('NFKD', char) for char in text if not unicodedata.combining(char) ]).encode('ascii', 'ignore').decode('ascii') def readLines(filename): lines = open(filename, encoding='utf-8').read().strip().split('\n') return [unicodeToAscii(line) for line in lines] # 构建一个人名类别与具体人名对应关系的字典 category_lines = {} # 构建所有类别的列表 all_categories = [] # 遍历所有的文件，使用glob.glob中可以利用正则表达式遍历 for filename in glob.glob(data_path + '*.txt'): category = os.path.splitext(os.path.basename(filename))[0] all_categories.append(category) lines = readLines(filename) # 将类别与人名对应关系存储到字典中 category_lines[category] = lines # 测试 print(category_lines['Italian'][:5]) -
字符无法直接被网络识别，因此要将其编码，这里使用最简单的one-hot编码，实现一个函数
lineToTensor(line)，将输入的名字编码成张量def lineToTensor(line): # 首先初始化一个全0的张量，大小为len(line) * 1 * n_letters # 代表人名每个字母都用一个(1 * n_letters)的one-hot向量表示 tensor = torch.zeros(len(line), 1, len(all_letters)) # 遍历人名的每个字母, 并搜索其在所有字母中的位置，将其对应的位置置为1 for li, letter in enumerate(line): tensor[li][0][all_letters.find(letter)] = 1 return tensor # 测试 line = "Bai" tensor = lineToTensor(line) print("line_tensor:", tensor) print("line_tensor_size:", tensor.size())
2. 模型搭建
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搭建RNN模型
class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(RNN, self).__init__() # input_size: 输入数据的特征维度 # hidden_size: RNN隐藏层的最后一个维度 # output_size: RNN网络最后线性层的输出维度 # num_layers: RNN网络的层数 self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.rnn = nn.RNN(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden): # input: 人名分类器中的输入张量，形状是1*n_letters # hidden: 代表隐藏层张量，形状是self.num_layers*1*hidden_size # 输入到RNN中的张量要求是三维张量，所以需要用unsqueeze()函数扩充维度 input1 = input1.unsqueeze(0) rr, hn = self.rnn(input1, hidden) # 将RNN中获得的结果通过线性层变换和softmax函数输出 return self.softmax(self.linear(rr)), hn def initHidden(self): # 初始化隐藏层张量，全0张量，维度是3 return torch.zeros(self.num_layers, 1, self.hidden_size) -
搭建LSTM模型
class LSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden, c): # 注意：LSTM网络的输入有三个张量，不能忘记细胞状态C input1 = input1.unsqueeze(0) rr, (hn, cn) = self.lstm(input1, (hidden, c)) return self.softmax(self.linear(rr)), hn, cn def initHiddenAndC(self): c = hidden = torch.zeros(self.num_layers, 1, self.hidden_size) return hidden, c -
搭建GRU模型
class GRU(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(GRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.gru = nn.GRU(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden): input1 = input1.unsqueeze(0) rr, hn = self.gru(input1, hidden) return self.softmax(self.linear(rr)), hn def initHidden(self): return torch.zeros(self.num_layers, 1, self.hidden_size)
3. 模型的实例化与训练
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定义一些参数以及实例化模型
# 参数 input_size = len(all_letters) n_hidden = 128 output_size = n_categories input1 = lineToTensor('B').squeeze(0) hidden = c = torch.zeros(1, 1, n_hidden) rnn = RNN(input_size, n_hidden, output_size) lstm = LSTM(input_size, n_hidden, output_size) gru = GRU(input_size, n_hidden, output_size) # 测试 rnn_output, rnn_hidden = rnn(input1, hidden) lstm_output, lstm_hidden, next_c = lstm(input1, hidden, c) gru_output, gru_hidden = gru(input1, hidden) # 打印输出信息 print("rnn_output:", rnn_output) print("rnn_shape:", rnn_output.shape) print("********************************") print("lstm_output:", lstm_output) print("lstm_shape:", lstm_output.shape) print("********************************") print("gru_output:", gru_output) print("gru_shape:", gru_output.shape) -
categoryFromOutput(output)函数功能为从模型输出中获取最大值和最大值的索引def categoryFromOutput(output): # 从输出中获取最大值和最大值的索引 top_n, top_i = output.topk(1) category_i = top_i[0].item() return all_categories[category_i], category_i -
randomTrainingExample()函数功能为随机选训练所需的数据def randomTrainingExample(): # 随机选择一个类别 category = random.choice(all_categories) # 从该类别中随机选择一个人名 line = random.choice(category_lines[category]) # 将人名转换为张量 category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long) line_tensor = lineToTensor(line) return category, line, category_tensor, line_tensor -
构建训练函数
# 构建传统RNN训练函数 criterion = nn.NLLLoss() learning_rate = 0.005 def trainRNN(category_tensor, line_tensor): hidden = rnn.initHidden() rnn.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden = rnn(line_tensor[i], hidden) # rnn对象由nn.RNN实例化得到，最终输出得到的是三维张量，为了满足category_tensor的维度要求，需要将其转换为二维张量 loss = criterion(output.squeeze(0), category_tensor) loss.backward() # 更新参数 for p in rnn.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() def trainLSTM(category_tensor, line_tensor): hidden, c = lstm.initHiddenAndC() lstm.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden, c = lstm(line_tensor[i], hidden, c) loss = criterion(output.squeeze(0), category_tensor) loss.backward() for p in lstm.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() def trainGRU(category_tensor, line_tensor): hidden = gru.initHidden() gru.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden = gru(line_tensor[i], hidden) loss = criterion(output.squeeze(0), category_tensor) loss.backward() for p in gru.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() -
绘图的辅助函数
timeSince(since)用于记录代码运行时间# 构建时间计算函数 def timeSince(since): now = time.time() s = now - since m = math.floor(s / 60) s -= m * 60 return "%dm %ds" % (m, s) -
构建完整的训练函数
# 训练轮次 n_iters = 1000 # 每隔print_every打印一次信息 print_every = 50 # 每个plot_every作为一次绘图采样点 plot_every = 10 def train(train_type_fn): # 每个制图间隔损失保存列表 all_losses = [] # 获得开始的时间戳 start = time.time() current_loss = 0 for iter in range(1, n_iters + 1): category, line, category_tensor, line_tensor = randomTrainingExample() output, loss = train_type_fn(category_tensor, line_tensor) current_loss += loss if iter % print_every == 0: guess, guess_i = categoryFromOutput(output) correct = "✓" if guess == category else "✗ (%s)" % category print("%d %d%% (%s) %.4f %s / %s %s" % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct)) if iter % plot_every == 0: all_losses.append(current_loss / plot_every) current_loss = 0 return all_losses, int(time.time() - start) -
训练并绘图
# 训练并制作对比图 all_losses1, period1 = train(trainRNN) all_losses2, period2 = train(trainLSTM) all_losses3, period3 = train(trainGRU) plt.figure(0) plt.plot(all_losses1, label='RNN') plt.plot(all_losses2, color='red', label='LSTM') plt.plot(all_losses3, color='green', label='GRU') plt.legend(loc='upper left') plt.figure(1) x_data = ['RNN', 'LSTM', 'GRU'] y_data = [period1, period2, period3] plt.bar(range(len(x_data)), y_data, color='green', tick_label=x_data) -
为方便测试，训练轮次只设置了一千，图形跑出来看不是很清楚，以下为训练1e5次的效果
4. 构建评估函数和预测函数
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评估函数
def evaluateRNN(line_tensor): output = None hidden = rnn.initHidden() for i in range(line_tensor.size()[0]): output, hidden = rnn(line_tensor[i], hidden) return output.squeeze(0) def evaluateLSTM(line_tensor): output = None hidden, c = lstm.initHiddenAndC() for i in range(line_tensor.size()[0]): output, hidden, c = lstm(line_tensor[i], hidden, c) return output.squeeze(0) def evaluateGRU(line_tensor): output = None hidden = gru.initHidden() for i in range(line_tensor.size()[0]): output, hidden = gru(line_tensor[i], hidden) return output.squeeze(0) -
预测函数
def predict(input_line, evaluate, n_predictions=3): print("\n> %s" % input_line) with torch.no_grad(): output = evaluate(lineToTensor(input_line)) topv, topi = output.topk(n_predictions, 1, True) predictions = [] for i in range(n_predictions): value = topv[0][i].item() category_index = topi[0][i].item() print("(%.2f) %s" % (value, all_categories[category_index])) predictions.append([value, all_categories[category_index]]) # 测试 for evaluate_fn in [evaluateRNN, evaluateLSTM, evaluateGRU]: predict('Dovesky', evaluate_fn) predict('Jackson', evaluate_fn) predict('Satoshi', evaluate_fn) -
完整代码版（方便复制来测试）
import torch from io import open import glob import os import unicodedata import string import random import time import math import torch.nn as nn import matplotlib.pyplot as plt data_path = './data/names/' all_letters = string.ascii_letters + " .,;'" def unicodeToAscii(text): """ Converts a Unicode string to an ASCII string. Args: text (str): The Unicode string to convert. Returns: str: The ASCII string. """ return ''.join([ unicodedata.normalize('NFKD', char) for char in text if not unicodedata.combining(char) ]).encode('ascii', 'ignore').decode('ascii') def readLines(filename): lines = open(filename, encoding='utf-8').read().strip().split('\n') return [unicodeToAscii(line) for line in lines] # 构建一个人名类别与具体人名对应关系的字典 category_lines = {} # 构建所有类别的列表 all_categories = [] # 遍历所有的文件，使用glob.glob中可以利用正则表达式遍历 for filename in glob.glob(data_path + '*.txt'): category = os.path.splitext(os.path.basename(filename))[0] all_categories.append(category) lines = readLines(filename) # 将类别与人名对应关系存储到字典中 category_lines[category] = lines n_categories = len(all_categories) def lineToTensor(line): # 首先初始化一个全0的张量，大小为len(line) * 1 * n_letters # 代表人名每个字母都用一个(1 * n_letters)的one-hot向量表示 tensor = torch.zeros(len(line), 1, len(all_letters)) # 遍历人名的每个字母, 并搜索其在所有字母中的位置，将其对应的位置置为1 for li, letter in enumerate(line): tensor[li][0][all_letters.find(letter)] = 1 return tensor class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(RNN, self).__init__() # input_size: 输入数据的特征维度 # hidden_size: RNN隐藏层的最后一个维度 # output_size: RNN网络最后线性层的输出维度 # num_layers: RNN网络的层数 self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.rnn = nn.RNN(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden): # input: 人名分类器中的输入张量，形状是1*n_letters # hidden: 代表隐藏层张量，形状是self.num_layers*1*hidden_size # 输入到RNN中的张量要求是三维张量，所以需要用unsqueeze()函数扩充维度 input1 = input1.unsqueeze(0) rr, hn = self.rnn(input1, hidden) # 将RNN中获得的结果通过线性层变换和softmax函数输出 return self.softmax(self.linear(rr)), hn def initHidden(self): # 初始化隐藏层张量，全0张量，维度是3 return torch.zeros(self.num_layers, 1, self.hidden_size) class LSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden, c): # 注意：LSTM网络的输入有三个张量，不能忘记细胞状态C input1 = input1.unsqueeze(0) rr, (hn, cn) = self.lstm(input1, (hidden, c)) return self.softmax(self.linear(rr)), hn, cn def initHiddenAndC(self): c = hidden = torch.zeros(self.num_layers, 1, self.hidden_size) return hidden, c class GRU(nn.Module): def __init__(self, input_size, hidden_size, output_size, num_layers=1): super(GRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.gru = nn.GRU(input_size, hidden_size, num_layers) self.linear = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=-1) def forward(self, input1, hidden): input1 = input1.unsqueeze(0) rr, hn = self.gru(input1, hidden) return self.softmax(self.linear(rr)), hn def initHidden(self): return torch.zeros(self.num_layers, 1, self.hidden_size) # 参数 input_size = len(all_letters) n_hidden = 128 output_size = n_categories hidden = c = torch.zeros(1, 1, n_hidden) rnn = RNN(input_size, n_hidden, output_size) lstm = LSTM(input_size, n_hidden, output_size) gru = GRU(input_size, n_hidden, output_size) def categoryFromOutput(output): # 从输出中获取最大值和最大值的索引 top_n, top_i = output.topk(1) category_i = top_i[0].item() return all_categories[category_i], category_i # category, category_i = categoryFromOutput(rnn_output) # print("category:", category) # print("category_i:", category_i) def randomTrainingExample(): # 随机选择一个类别 category = random.choice(all_categories) # 从该类别中随机选择一个人名 line = random.choice(category_lines[category]) # 将人名转换为张量 category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long) line_tensor = lineToTensor(line) return category, line, category_tensor, line_tensor # 构建传统RNN训练函数 criterion = nn.NLLLoss() learning_rate = 0.005 def trainRNN(category_tensor, line_tensor): hidden = rnn.initHidden() rnn.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden = rnn(line_tensor[i], hidden) # rnn对象由nn.RNN实例化得到，最终输出得到的是三维张量，为了满足category_tensor的维度要求，需要将其转换为二维张量 loss = criterion(output.squeeze(0), category_tensor) loss.backward() # 更新参数 for p in rnn.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() def trainLSTM(category_tensor, line_tensor): hidden, c = lstm.initHiddenAndC() lstm.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden, c = lstm(line_tensor[i], hidden, c) loss = criterion(output.squeeze(0), category_tensor) loss.backward() for p in lstm.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() def trainGRU(category_tensor, line_tensor): hidden = gru.initHidden() gru.zero_grad() output = None for i in range(line_tensor.size()[0]): output, hidden = gru(line_tensor[i], hidden) loss = criterion(output.squeeze(0), category_tensor) loss.backward() for p in gru.parameters(): p.data.add_(-learning_rate, p.grad.data) return output, loss.item() # 构建时间计算函数 def timeSince(since): now = time.time() s = now - since m = math.floor(s / 60) s -= m * 60 return "%dm %ds" % (m, s) # 完整的训练函数 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_iters = 1000 print_every = 50 plot_every = 10 def train(train_type_fn): # 每个制图间隔损失保存列表 all_losses = [] # 获得开始的时间戳 start = time.time() current_loss = 0 for iter in range(1, n_iters + 1): category, line, category_tensor, line_tensor = randomTrainingExample() output, loss = train_type_fn(category_tensor, line_tensor) current_loss += loss if iter % print_every == 0: guess, guess_i = categoryFromOutput(output) correct = "✓" if guess == category else "✗ (%s)" % category print("%d %d%% (%s) %.4f %s / %s %s" % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct)) if iter % plot_every == 0: all_losses.append(current_loss / plot_every) current_loss = 0 return all_losses, int(time.time() - start) # 训练并制作对比图 all_losses1, period1 = train(trainRNN) all_losses2, period2 = train(trainLSTM) all_losses3, period3 = train(trainGRU) plt.figure(0) plt.plot(all_losses1, label='RNN') plt.plot(all_losses2, color='red', label='LSTM') plt.plot(all_losses3, color='green', label='GRU') plt.legend(loc='upper left') plt.figure(1) x_data = ['RNN', 'LSTM', 'GRU'] y_data = [period1, period2, period3] plt.bar(range(len(x_data)), y_data, color='green', tick_label=x_data) # 构建评估函数 def evaluateRNN(line_tensor): output = None hidden = rnn.initHidden() for i in range(line_tensor.size()[0]): output, hidden = rnn(line_tensor[i], hidden) return output.squeeze(0) def evaluateLSTM(line_tensor): output = None hidden, c = lstm.initHiddenAndC() for i in range(line_tensor.size()[0]): output, hidden, c = lstm(line_tensor[i], hidden, c) return output.squeeze(0) def evaluateGRU(line_tensor): output = None hidden = gru.initHidden() for i in range(line_tensor.size()[0]): output, hidden = gru(line_tensor[i], hidden) return output.squeeze(0) # 构建预测函数 def predict(input_line, evaluate, n_predictions=3): print("\n> %s" % input_line) with torch.no_grad(): output = evaluate(lineToTensor(input_line)) topv, topi = output.topk(n_predictions, 1, True) predictions = [] for i in range(n_predictions): value = topv[0][i].item() category_index = topi[0][i].item() print("(%.2f) %s" % (value, all_categories[category_index])) predictions.append([value, all_categories[category_index]]) # 调用试试 for evaluate_fn in [evaluateRNN, evaluateLSTM, evaluateGRU]: predict('Dovesky', evaluate_fn) predict('Jackson', evaluate_fn) predict('Satoshi', evaluate_fn)