Unknown layer: Bidirectional. Please ensure this object is passed to the `custom_objects` argument

这个错误通常是因为在加载模型时，没有添加必要的自定义层到 `custom_objects` 参数中。

如果你的模型中使用了自定义的层，你需要将这些自定义层添加到 `custom_objects` 参数中。例如，如果你的模型使用了 Bidirectional 层，则可以使用以下代码进行加载：

from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Bidirectional

model = load_model('my_model.h5', custom_objects={'Bidirectional': Bidirectional})

在上面的代码中，我们将 `Bidirectional` 层添加到了 `custom_objects` 参数中，这样在加载模型时就可以识别这个自定义层了。

注意，如果你的模型中使用了多个自定义层，你需要将它们全部添加到 `custom_objects` 参数中。

相关问题

class Model(nn.Module): def init(self, config): super(Model, self).init() if config.embedding_pretrained is not None: self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False) else: self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1) self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers, bidirectional=True, batch_first=True, dropout=config.dropout) self.fc = nn.Linear(config.hidden_size * 2, config.num_classes) def forward(self, x): x, _ = x out = self.embedding(x) # [batch_size, seq_len, embeding]=[128, 32, 300] out, _ = self.lstm(out) out = self.fc(out[:, -1, :]) # 句子最后时刻的 hidden state return out

这段代码是一个PyTorch的模型定义，其中包括embedding层、LSTM层和全连接层。模型的输入是x，输出是out。其中，输入x的形状为[batch_size, seq_len]，表示一批输入数据中每个序列的长度为seq_len，总共有batch_size个序列。模型会将输入的每个词转换为向量形式，然后通过LSTM层对整个序列进行编码，最后使用全连接层将编码结果映射到目标类别的概率分布上。

翻译成中文：BIFPN stands for "Bi-directional Feature Pyramid Network", which is a neural network architecture used for object detection in computer vision. BIFPN was introduced in a paper titled "BiFPN: Efficient Multi-scale Fusion with Repeated Pyramidal Structures" by Tan et al. in 2019. BIFPN is a type of Feature Pyramid Network (FPN) that aims to improve the performance of object detection models by incorporating multi-scale features. BIFPN achieves this by using a repeated pyramidal structure that combines low-level and high-level features through a bidirectional pathway. In BIFPN, the input features are passed through a series of bi-directional nodes that perform top-down and bottom-up feature fusion, resulting in a set of multi-scale features that are robust to variations in object size and scale. The repeated structure of BIFPN helps to ensure that features at all scales are appropriately integrated, while the bidirectional connections help to propagate information between the high-level and low-level features. BIFPN has been shown to be effective in improving the accuracy of object detection models, while also being computationally efficient. As a result, BIFPN has become a popular choice for many state-of-the-art object detection architectures.

BIFPN的全称是“双向特征金字塔网络”，是一种用于计算机视觉中物体检测的神经网络架构，2019年Tan等人发表的论文《BIFPN：带有重复的金字塔结构的高效多尺度融合》中首次提出。BIFPN是一种特征金字塔网络（FPN），旨在通过结合多尺度特征来提高物体检测模型的性能。BIFPN通过使用一个重复的金字塔结构，通过双向通道将低层特征和高层特征融合在一起，从而实现。在BIFPN中，输入特征通过一系列双向节点进行自上而下和自下而上的特征融合，得到一组对物体大小和尺度变化具有鲁棒性的多尺度特征。BIFPN的重复结构有助于确保所有尺度的特征得到适当的集成，而双向连接有助于在高层特征和低层特征之间传播信息。BIFPN已被证明可以有效提高物体检测模型的准确性，同时具有计算效率。因此，BIFPN已成为许多最先进的物体检测架构的首选。