def create_word_encoder(config):
encoder = BidirectionalEncoder(config['src_vocab_size'],
config['enc_embed'], config['enc_nhids'])
encoder.weights_init = IsotropicGaussian(config['weight_scale'])
encoder.biases_init = Constant(0)
encoder.push_initialization_config()
encoder.bidir.prototype.weights_init = Orthogonal()
encoder.initialize()
input_words = tensor.lmatrix('words')
input_words_mask = tensor.matrix('words_mask')
training_representation = encoder.apply(input_words, input_words_mask)
training_representation.name = "words_representation"
sampling_input_words = tensor.lmatrix('sampling_words')
sampling_input_words_mask = tensor.ones(
(sampling_input_words.shape[0], sampling_input_words.shape[1]))
sampling_representation = encoder.apply(sampling_input_words,
sampling_input_words_mask)
return encoder, training_representation, sampling_representation
