The ConvLSTM class supports an arbitrary number of layers. In this case, it can be specified the hidden dimension (that is, the number of channels) and the kernel size of each layer. In the case more layers are present but a single value is provided, this is replicated for all the layers.

└─convlstm ├──test.py # 验证脚本 ├──export.py # 导出脚本 ├──train.py # 启智平台训练脚本 ├──train_.py # 启智平台调试脚本 ├── README.md # README └─ src # 辅助脚本 └─ data ├─movingMNIST.py # 数据集 └─ model ├─convlstm.py # 模型结构 └─ tools ├─callback ...

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST Topics time-series lstm gru rnn spatio-temporal encoder-decoder convlstm convgru pytorch-implementation

The ConvLSTM model is mainly used as skeleton to design a BCI (Brain Computer Interface) decoder for our project (Decode the kinematic signal from neural signal). This repo is implementation of ConvLSTM in Pytorch. The implemenation is inherited from the paper: Convolutional LSTM Network-A Machine LearningApproach for Precipitation Nowcasting

Ultralytics YOLO11 🚀 + ConvLSTM. Contribute to VelDia/yolov11_convLSTM development by creating an account on GitHub.

Implementation of Convolutional LSTM in PyTorch. Contribute to ndrplz/ConvLSTM_pytorch development by creating an account on GitHub.

This repo has implemented a pytorch-based encoder-forecaster model with RNNs including (TrajGRU, ConvLSTM) to do precipitation nowcasting. For more information about TrajGRU, please refer to HKO-7. If you are interested in my implementation of ConvLSTM and TrajGRU, please see ConvLSTM and TrajGRU. It is assumed that the input shape should be ...

Uses convLSTM-cells to predict frames from the movingMNIST dataset. There are two models: Single-step model.

The above figure is SAM-ConvLSTM formulation process. alpha_{h} in the figure is used for visualizing attention maps in evaluation (pipeline/evaluator.py). Also see the following files for all calculation process. self_attention_memory_convlstm/cell.py self_attention_memory_convlstm/model.py self ...

Implementation of a ConvLSTM architecture in TensorFlow Keras. - felipeagarcia/ConvLSTM. This code implements a network proposed by Karim et. al. [1], wich consists on three convolutional layers and a LSTM layer processing the input data parallel, then, the outputs of each layer are concatenated and used by a softmax layer.