def test_accumulator(): with tf.Graph().as_default(): iw = tf.constant([[1, 2, 3, 0, 0], [2, 1, 3, 1, 9], [1, 0, 0, 0, 0]], dtype=tf.float32) iw = tf.expand_dims(iw, 2) iw_len = tf.constant([3, 5, 1]) dw = tf.constant([[1, 3, 3, 4, 0], [3, 4, 3, 4, 5], [3, 0, 0, 0, 0]], dtype=tf.float32) dw = tf.expand_dims(dw, 2) dw_len = tf.constant([4, 5, 1]) edit_vector = ev.accumulator_encoder(iw, dw, iw_len, dw_len, 10, 100, 0.1, 14, 0.8) with tf.Session() as sess: sess.run([ tf.global_variables_initializer(), tf.local_variables_initializer(), tf.tables_initializer() ]) o = sess.run(edit_vector) assert o.shape == (3, 10)
def editor_train(base_words, source_words, target_words, insert_words, delete_words, embed_matrix, vocab_table, hidden_dim, agenda_dim, edit_dim, num_encoder_layers, num_decoder_layers, attn_dim, beam_width, max_sent_length, dropout_keep, lamb_reg, norm_eps, norm_max, kill_edit, draw_edit, swap_memory, use_beam_decoder=False, use_dropout=False, no_insert_delete_attn=False, enable_vae=True): batch_size = tf.shape(source_words)[0] # [batch] base_len = seq.length_pre_embedding(base_words) src_len = seq.length_pre_embedding(source_words) tgt_len = seq.length_pre_embedding(target_words) iw_len = seq.length_pre_embedding(insert_words) dw_len = seq.length_pre_embedding(delete_words) # variable of shape [vocab_size, embed_dim] embeddings = vocab.get_embeddings() # [batch x max_len x embed_dim] base_word_embeds = vocab.embed_tokens(base_words) # src_word_embeds = vocab.embed_tokens(source_words) # tgt_word_embeds = vocab.embed_tokens(target_words) insert_word_embeds = vocab.embed_tokens(insert_words) delete_word_embeds = vocab.embed_tokens(delete_words) # [batch x max_len x rnn_out_dim], [batch x rnn_out_dim] base_sent_hidden_states, base_sent_embed = encoder.source_sent_encoder( base_word_embeds, base_len, hidden_dim, num_encoder_layers, use_dropout=use_dropout, dropout_keep=dropout_keep, swap_memory=swap_memory) # [batch x edit_dim] if kill_edit: edit_vector = tf.zeros(shape=(batch_size, edit_dim)) else: if draw_edit: edit_vector = edit_encoder.random_noise_encoder( batch_size, edit_dim, norm_max) else: edit_vector = edit_encoder.accumulator_encoder( insert_word_embeds, delete_word_embeds, iw_len, dw_len, edit_dim, lamb_reg, norm_eps, norm_max, dropout_keep, enable_vae=enable_vae) # [batch x agenda_dim] input_agenda = agn.linear(base_sent_embed, edit_vector, agenda_dim) train_dec_inp, train_dec_inp_len, \ train_dec_out, train_dec_out_len = prepare_decoder_input_output(target_words, tgt_len, vocab_table) train_decoder = decoder.train_decoder( input_agenda, embeddings, train_dec_inp, base_sent_hidden_states, insert_word_embeds, delete_word_embeds, train_dec_inp_len, base_len, iw_len, dw_len, attn_dim, hidden_dim, num_decoder_layers, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) if use_beam_decoder: infr_decoder = decoder.beam_eval_decoder( input_agenda, embeddings, vocab.get_token_id(vocab.START_TOKEN, vocab_table), vocab.get_token_id(vocab.STOP_TOKEN, vocab_table), base_sent_hidden_states, insert_word_embeds, delete_word_embeds, base_len, iw_len, dw_len, attn_dim, hidden_dim, num_decoder_layers, max_sent_length, beam_width, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) else: infr_decoder = decoder.greedy_eval_decoder( input_agenda, embeddings, vocab.get_token_id(vocab.START_TOKEN, vocab_table), vocab.get_token_id(vocab.STOP_TOKEN, vocab_table), base_sent_hidden_states, insert_word_embeds, delete_word_embeds, base_len, iw_len, dw_len, attn_dim, hidden_dim, num_decoder_layers, max_sent_length, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) return train_decoder, infr_decoder, train_dec_out, train_dec_out_len
def editor_train(base_words, extended_base_words, output_words, extended_output_words, source_words, target_words, insert_words, delete_words, oov, vocab_size, hidden_dim, agenda_dim, edit_dim, micro_edit_ev_dim, num_heads, num_encoder_layers, num_decoder_layers, attn_dim, beam_width, ctx_hidden_dim, ctx_hidden_layer, wa_hidden_dim, wa_hidden_layer, meve_hidden_dim, meve_hidden_layers, recons_dense_layers, max_sent_length, dropout_keep, lamb_reg, norm_eps, norm_max, kill_edit, draw_edit, swap_memory, use_beam_decoder=False, use_dropout=False, no_insert_delete_attn=False, enable_vae=True): batch_size = tf.shape(source_words)[0] # [batch] base_len = seq.length_pre_embedding(base_words) output_len = seq.length_pre_embedding(extended_output_words) src_len = seq.length_pre_embedding(source_words) tgt_len = seq.length_pre_embedding(target_words) iw_len = seq.length_pre_embedding(insert_words) dw_len = seq.length_pre_embedding(delete_words) # variable of shape [vocab_size, embed_dim] embeddings = vocab.get_embeddings() # [batch x max_len x embed_dim] base_word_embeds = vocab.embed_tokens(base_words) output_word_embeds = vocab.embed_tokens(output_words) # src_word_embeds = vocab.embed_tokens(source_words) # tgt_word_embeds = vocab.embed_tokens(target_words) insert_word_embeds = vocab.embed_tokens(insert_words) delete_word_embeds = vocab.embed_tokens(delete_words) # [batch x max_len x rnn_out_dim], [batch x rnn_out_dim] base_sent_hidden_states, base_sent_embed = encoder.source_sent_encoder( base_word_embeds, base_len, hidden_dim, num_encoder_layers, use_dropout=use_dropout, dropout_keep=dropout_keep, swap_memory=swap_memory) assert kill_edit == False and draw_edit == False # [batch x edit_dim] if kill_edit: edit_vector = tf.zeros(shape=(batch_size, edit_dim)) else: if draw_edit: edit_vector = random_noise_encoder(batch_size, edit_dim, norm_max) else: edit_vector = accumulator_encoder(insert_word_embeds, delete_word_embeds, iw_len, dw_len, edit_dim, lamb_reg, norm_eps, norm_max, dropout_keep, enable_vae=enable_vae) wa_inserted, wa_deleted = (tf.constant([[0]]), tf.constant( [[0]])), (tf.constant([[0]]), tf.constant([[0]])) # [batch x agenda_dim] base_agenda = agn.linear(base_sent_embed, edit_vector, agenda_dim) train_dec_inp, train_dec_inp_len, \ train_dec_out, train_dec_out_len = prepare_decoder_input_output(output_words, extended_output_words, output_len) train_dec_inp_extended = prepare_decoder_inputs(extended_output_words, tf.cast(-1, tf.int64)) train_decoder = decoder.train_decoder( base_agenda, embeddings, extended_base_words, oov, train_dec_inp, train_dec_inp_extended, base_sent_hidden_states, wa_inserted, wa_deleted, train_dec_inp_len, base_len, src_len, tgt_len, vocab_size, attn_dim, hidden_dim, num_decoder_layers, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) if use_beam_decoder: infr_decoder = decoder.beam_eval_decoder( base_agenda, embeddings, extended_base_words, oov, vocab.get_token_id(vocab.START_TOKEN), vocab.get_token_id(vocab.STOP_TOKEN), base_sent_hidden_states, wa_inserted, wa_deleted, base_len, src_len, tgt_len, vocab_size, attn_dim, hidden_dim, num_decoder_layers, max_sent_length, beam_width, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) else: infr_decoder = decoder.greedy_eval_decoder( base_agenda, embeddings, extended_base_words, oov, vocab.get_token_id(vocab.START_TOKEN), vocab.get_token_id(vocab.STOP_TOKEN), base_sent_hidden_states, wa_inserted, wa_deleted, base_len, src_len, tgt_len, vocab_size, attn_dim, hidden_dim, num_decoder_layers, max_sent_length, swap_memory, enable_dropout=use_dropout, dropout_keep=dropout_keep, no_insert_delete_attn=no_insert_delete_attn) edit_vector_recons = output_words_to_edit_vector( output_word_embeds, output_len, edit_dim, ctx_hidden_dim, ctx_hidden_layer, recons_dense_layers, swap_memory) optimizer.add_reconst_loss(edit_vector, edit_vector_recons) return train_decoder, infr_decoder, train_dec_out, train_dec_out_len