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- def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
- with torch.no_grad():
- input_tensor = tensorFromSentenceWithChar(input_lang, sentence)
- input_length = input_tensor.size()[0]
- encoder_hidden = encoder.initHidden()
- encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
- for ei in range(input_length):
- encoder_output, encoder_hidden = encoder(input_tensor[ei],
- encoder_hidden)
- encoder_outputs[ei] += encoder_output[0, 0]
- decoder_input = torch.tensor([[SOS_token]], device=device) # SOS
- decoder_hidden = encoder_hidden
- decoded_words = []
- decoder_attentions = torch.zeros(max_length, max_length)
- return beam_decode(decoder_input, decoder_hidden, encoder_outputs, decoder), None
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