inputs, _ = data
时间: 2024-06-03 21:08:58 浏览: 10
I'm sorry, but I cannot provide a complete answer as the code snippet you provided is incomplete. It seems like the variable `data` is being unpacked into two variables `inputs` and `_`. However, without the complete code and context, it is difficult to provide a more detailed answer.
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def forward(self, input_data, attention_mask=None, labels=None, position_ids=None, inputs_embeds=None, head_mask=None): outputs = self.bert(input_data, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds) sequence_output = outputs[0] # 去除cls,(batch,seq) sequence_output = sequence_output[:, 1:] sequence_output = self.dropout(sequence_output) # 得到判别值 logits = self.classifier(sequence_output) outputs = (logits,) if labels is not None: loss_mask = labels.gt(-1) loss = self.crf(logits, labels, loss_mask) * (-1) outputs = (loss,) + outputs # contain: (loss), scores return outputs
这是BertNER模型的前向传播函数,输入参数包括input_data,attention_mask,labels,position_ids,inputs_embeds,head_mask。其中,input_data是输入的文本数据,attention_mask是掩码,用于指示哪些词是padding,labels是标注序列,position_ids是位置编码,inputs_embeds是词嵌入向量,head_mask是多头注意力层的掩码。
在函数中,首先将输入数据input_data输入到BERT模型中,得到输出outputs,其中outputs[0]表示BERT模型的输出特征。接着,去掉输出特征中的[CLS]标记,并通过dropout层进行随机失活。然后,将输出特征输入到线性分类器中,得到每个位置上的标记得分,即预测值。如果labels不为空,则计算损失值,并将损失值添加到输出outputs中。最后,返回输出outputs。
input_shape = (None, 24) inputs = Input(shape=input_shape) # 定义TCN网络的中间层,可以设置多个卷积层和池化层 tcn_layer = TCN(nb_filters=4, kernel_size=3, dilations=[1, 2, 4])(inputs) # 定义模型,将输入层和TCN网络的中间层连接起来 model = Model(inputs=inputs, outputs=tcn_layer) # 使用predict函数获取特征,将数据分成多个batch进行处理 num_samples = x_data.shape[0] features = [] batch_size = 32 for i in range(0, num_samples, batch_size): batch = x_data[i:i + batch_size] batch = tf.expand_dims(batch, axis=0) if i + batch_size > num_samples: batch_num = num_samples - i if batch_num == 1: feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_num) else: feature_batch = model.predict(batch, batch_size=batch_num) features.append(feature_batch) else: feature_batch = model.predict(batch, batch_size=batch_size) features.append(feature_batch) features = np.concatenate(features, axis=0) print(features.shape)如果想要输出长度仍为x_data[0]
如果要在输出中保留原始数据 `x_data` 的第一个维度长度,你可以在预测结束后对 `features` 进行裁剪操作,使其第一个维度的长度与 `x_data` 的第一个维度长度相同。具体来说,你可以按照以下方式修改代码:
```python
import tensorflow as tf
# 定义输入层
input_shape = (None, 24)
inputs = Input(shape=input_shape)
# 定义 TCN 网络的中间层
tcn_layer = TCN(nb_filters=4, kernel_size=3, dilations=[1, 2, 4])(inputs)
# 定义模型
model = Model(inputs=inputs, outputs=tcn_layer)
# 使用 predict 函数获取特征,将数据分成多个 batch 进行处理
num_samples = x_data.shape[0]
features = []
batch_size = 32
for i in range(0, num_samples, batch_size):
batch = x_data[i:i + batch_size]
batch = tf.expand_dims(batch, axis=0)
if i + batch_size > num_samples:
batch_num = num_samples - i
if batch_num == 1:
feature_batch = model.predict(batch.reshape(1, *input_shape), batch_size=batch_num)
else:
feature_batch = model.predict(batch, batch_size=batch_num)
features.append(feature_batch)
else:
feature_batch = model.predict(batch, batch_size=batch_size)
features.append(feature_batch)
# 将 features 沿第一个维度拼接起来
features = np.concatenate(features, axis=0)
# 对 features 进行裁剪,使其第一个维度长度与 x_data 相同
features = features[:x_data.shape[0]]
print(features.shape)
```
在上面的代码中,我们在预测结束后对 `features` 进行了裁剪操作,使其第一个维度长度与 `x_data` 相同。这样就可以保留原始数据的第一个维度长度了。
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