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# number of sequences contained in a mini-batch.batch_size=64# the hyper params for Adam optimizer.learning_rate=0.001beta1=0.9beta2=0.98eps=1e-9# the params for learning rate schedulingwarmup_steps=4000src_vocab_size=2909trg_vocab_size=3149# the dimension for word embeddings, which is also the last dimension of# the input and output of multi-head attention, position-wise feed-forward# networks, encoder and decoder.d_model=512# size of the hidden layer in position-wise feed-forward networks.d_inner_hid=1024# the dimension that keys are projected to for dot-product attention.d_key=64# the dimension that values are projected to for dot-product attention.d_value=64# number of head used in multi-head attention.n_head=8# number of sub-layers to be stacked in the encoder and decoder.n_layer=6# dropout rate used by all dropout layers.dropout=0.
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Fluid和Pytorch均使用如下的模型和训练参数(目前dropout_op有bug,这里暂时去掉dropout),并统一初始化方法,Transformer在WMT'16数据集上有如图的cost曲线对照图。
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