attention_weights = attention_weights * (self.AttentionWeightRange[1] - self.AttentionWeightRange[0]) + self.AttentionWeightRange[0] # 计算加权输入 weighted_input = torch.mul(input, attention_weights.unsqueeze(1).expand_as(input)) output = torch.sum(weighted_input, dim=0) return output 故事...
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Randomness is used because this class of machine learning algorithm performs better with it than without. The most common form of randomness used in neural networks is the random initialization of the network weights. Although randomness can be used in other areas, here is just a short list: ...
Neural networks (deep learning) are a stochastic machine learning algorithm. The random initial weights allow the model to try learning from a different starting point in the search space each algorithm run and allow the learning algorithm to “break symmetry” during learning. The random shuffle o...
class Variable(checkpointable.CheckpointableBase): """See the @{$variables$Variables How To} for a high level overview. A variable maintains state in the graph across calls to `run()`. You add a variable to the graph by constructing an instance of the class `Variable`. ...
class Variable(checkpointable.CheckpointableBase): """See the @{$variables$Variables How To} for a high level overview. A variable maintains state in the graph across calls to `run()`. You add a variable to the graph by constructing an instance of the class `Variable`. ...
The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training dataset. You must specify the input_dim which is the size of the vocabulary, the output_dim which is the size of the vector space of the embedding, and optionally the ...