# cross entropy loss function in PyTorch, which includes softmax operation loss_func=nn.CrossEntropyLoss() # convert word_scores into a 2D array and y_id into a 1D array to calculate the loss loss=loss_func(word_scores[:,:−1,:].reshape(−1, vocab_size), y_id [:,1:].reshape...
In general, the gradient is a value that represents the slope of a function output with respect to the function input. In the computational graph setting, gradients exist for each parameter in the model and can be thought of as the parameter’s contribution to the error signal. In PyTorch,...
A practical implementing toolkit, API Torchmeta, was released by PyTorch to accelerate straightforward applications of meta-learning through existing data loaders and datasets. The official code is available athttps://github.com/tristandeleu/pytorch-meta, with the official documentation athttps://tristan...
Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch: Interfaces and variable sharing in PyTorch convention Excellent factorization and rich functionalities in TF convention. Rich Pre-trained Models, Rich Usage with ...
We regret to inform you that due the policy changing, it no longer provides a public storage for model sharing. We are working hard to find a solution. Multi-Task Deep Neural Networks for Natural Language Understanding This PyTorch package implements the Multi-Task Deep Neural Networks (MT-DNN...
Predicted structures from the IgFold model undergo two stages of refinement to resolve non-realistic features and add side-chain atoms. First, the backbone structure is optimized in PyTorch using a loss function consisting of idealization terms and an RMSD constraint: ...
classRNNLanguageModel(Model):def__init__(self,vocab:Vocabulary)->None:super().__init__(vocab)token_embedding=Embedding(num_embeddings=vocab.get_vocab_size('tokens'),embedding_dim=EMBEDDING_SIZE)self.embedder=BasicTextFieldEmbedder({"tokens":token_embedding})self.rnn=PytorchSeq2SeqWrapper(torch....
FBTT-Embeddinglibrary provides functionality to compress sparse embedding tables commonly used in machine learning models such as recommendation and natural language processing. The library can be used as a direct replacement toPyTorch’s EmbeddingBagfunctionality. It provides the forward and backward propa...
We regret to inform you that due the policy changing, it no longer provides a public storage for model sharing. We are working hard to find a solution. Multi-Task Deep Neural Networks for Natural Language Understanding This PyTorch package implements the Multi-Task Deep Neural Networks (MT-DNN...
We regret to inform you that due the policy changing, it no longer provides a public storage for model sharing. We are working hard to find a solution. Multi-Task Deep Neural Networks for Natural Language Understanding This PyTorch package implements the Multi-Task Deep Neural Networks (MT-DNN...