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發(fā)視頻與詞相似度和類比任務一樣，我們也可以將預訓練詞向量應用于情感分析。由于第 16.1 節(jié)中的 IMDb 評論數(shù)據(jù)集 不是很大，使用在大規(guī)模語料庫上預訓練的文本表示可能會減少模型的過度擬合。作為圖 16.2.1所示的具體示例 ，我們將使用預訓練的 GloVe 模型表示每個標記，并將這些標記表示輸入多層雙向 RNN 以獲得文本序列表示，并將其轉換為情感分析輸出 （Maas等，2011）。對于相同的下游應用程序，我們稍后會考慮不同的架構選擇。
圖 16.2.1本節(jié)將預訓練的 GloVe 提供給基于 RNN 的架構進行情緒分析。
16.2.1。用 RNN 表示單個文本
在文本分類任務中，例如情感分析，變長的文本序列將被轉換為固定長度的類別。在下面的BiRNN類中，雖然文本序列的每個標記都通過嵌入層 ( self.embedding) 獲得其單獨的預訓練 GloVe 表示，但整個序列由雙向 RNN ( self.encoder) 編碼。更具體地說，雙向 LSTM 在初始和最終時間步的隱藏狀態(tài)（在最后一層）被連接起來作為文本序列的表示。然后通過具有兩個輸出（“正”和“負”）的全連接層 ( self.decoder) 將該單一文本表示轉換為輸出類別。
class BiRNN(nn.Module):
def __init__(self, vocab_size, embed_size, num_hiddens,
num_layers, **kwargs):
super(BiRNN, self).__init__(**kwargs)
self.embedding = nn.Embedding(vocab_size, embed_size)
# Set `bidirectional` to True to get a bidirectional RNN
self.encoder = nn.LSTM(embed_size, num_hiddens, num_layers=num_layers,
bidirectional=True)
self.decoder = nn.Linear(4 * num_hiddens, 2)
def forward(self, inputs):
# The shape of `inputs` is (batch size, no. of time steps). Because
# LSTM requires its input's first dimension to be the temporal
# dimension, the input is transposed before obtaining token
# representations. The output shape is (no. of time steps, batch size,
# word vector dimension)
embeddings = self.embedding(inputs.T)
self.encoder.flatten_parameters()
# Returns hidden states of the last hidden layer at different time
# steps. The shape of `outputs` is (no. of time steps, batch size,
# 2 * no. of hidden units)
outputs, _ = self.encoder(embeddings)
# Concatenate the hidden states at the initial and final time steps as
# the input of the fully connected layer. Its shape is (batch size,
# 4 * no. of hidden units)
encoding = torch.cat((outputs[0], outputs[-1]), dim=1)
outs = self.decoder(encoding)
return outs
class BiRNN(nn.Block):
def __init__(self, vocab_size, embed_size, num_hiddens,
num_layers, **kwargs):
super(BiRNN, self).__init__(**kwargs)
self.embedding = nn.Embedding(vocab_size, embed_size)
# Set `bidirectional` to True to get a bidirectional RNN
self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers,
bidirectional=True, input_size=embed_size)
self.decoder = nn.Dense(2)
def forward(self, inputs):
# The shape of `inputs` is (batch size, no. of time steps). Because
# LSTM requires its input's first dimension to be the temporal
# dimension, the input is transposed before obtaining token
# representations. The output shape is (no. of time steps, batch size,
# word vector dimension)
embeddings = self.embedding(inputs.T)
# Returns hidden states of the last hidden layer at different time
# steps. The shape of `outputs` is (no. of time steps, batch size,
# 2 * no. of hidden units)
outputs = self.encoder(embeddings)
# Concatenate the hidden states at the initial and final time steps as
# the input of the fully connected layer. Its shape is (batch size,
# 4 * no. of hidden units)
encoding = np.concatenate((outputs[0], outputs[-1]), axis=1)
outs = self.decoder(encoding)
return outs
讓我們構建一個具有兩個隱藏層的雙向 RNN 來表示用于情感分析的單個文本。
embed_size, num_hiddens, num_layers, devices = 100, 100, 2, d2l.try_all_gpus()
net = BiRNN(len(vocab), embed_size, num_hiddens, num_layers)
def init_weights(module):
if type(module) == nn.Linear:
nn.init.xavier_uniform_(module.weight)
if type(module) == nn.LSTM:
for param in module._flat_weights_names:
if "weight" in param:
nn.init.xavier_uniform_(module._parameters[param])
net.apply(init_weights);
16.2.2。加載預訓練詞向量
embed_size下面我們?yōu)樵~匯表中的標記加載預訓練的 100 維（需要與 一致）GloVe 嵌入。
打印詞匯表中所有標記的向量形狀。
我們使用這些預訓練的詞向量來表示評論中的標記，并且不會在訓練期間更新這些向量。
16.2.3。訓練和評估模型
現(xiàn)在我們可以訓練雙向 RNN 進行情感分析。
lr, num_epochs = 0.01, 5
trainer = torch.optim.Adam(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction="none")
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)
loss 0.311, train acc 0.872, test acc 0.850
574.5 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
loss 0.428, train acc 0.806, test acc 0.791
488.5 examples/sec on [gpu(0), gpu(1)]
我們定義了以下函數(shù)來使用經(jīng)過訓練的模型預測文本序列的情緒net。
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