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Neural Machine Translation with Attention #20084
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}, | ||
"cell_type": "code", | ||
"source": [ | ||
"# first we remove the pronumciations\n", |
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pronunciations
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Done
" \n", | ||
" # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", | ||
" # the code automatically does that.\n", | ||
" if tf.test.is_gpu_available():\n", |
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Refactor this into a GRU function so you can remove the duplicates?
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Done
" # passing enc_output to the decoder\n", | ||
" predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n", | ||
" \n", | ||
" loss += loss_function(targ[:, t], predictions)\n", |
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I think you also need to worry here about sequences of different lengths, by masking the loss
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Done
Added Neural Machine Translation with Attention notebook