[
{
"path": ".ipynb_checkpoints/main-v2-checkpoint.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"import time\\n\",\n \"import datetime\\n\",\n \"import os\\n\",\n \"from tqdm import tqdm\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签\\n\",\n \"plt.rcParams['axes.unicode_minus']=False\\n\",\n \"\\n\",\n \"os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"0, 1, 2, 3\\\" # 添加可用的gpu\\n\",\n \"physical_devices = tf.config.experimental.list_physical_devices('GPU')\\n\",\n \"for device in physical_devices:\\n\",\n \" tf.config.experimental.set_memory_growth(device, True)\\n\",\n \"\\n\",\n \"from params import Params as pm\\n\",\n \"from utils_v2 import en2idx, idx2en, de2idx, idx2de, dump2record, build_dataset, LRSchedule, masking, create_masks, plot_attention_weights\\n\",\n \"from bleu import bleu_metrics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tf.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"strategy = tf.distribute.MirroredStrategy()\\n\",\n \"\\n\",\n \"print('Number of device: {}'.format(strategy.num_replicas_in_sync))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_data(corpus_file):\\n\",\n \" return open(corpus_file, 'r', encoding='utf-8').read().splitlines()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"src_train, src_val = get_data(pm.src_train), get_data(pm.src_test)\\n\",\n \"tgt_train, tgt_val = get_data(pm.tgt_train), get_data(pm.tgt_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dump2record(pm.train_record, src_train, tgt_train)\\n\",\n \"dump2record(pm.test_record, src_val, tgt_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from modules_v2 import positional_encoding, scaled_dot_product_attention, multihead_attention, pointwise_feedforward, EncoderBlock, DecoderBlock, Encoder, Decoder, Transformer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Positional encoding\\n\",\n \"$$\\\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\\n\",\n \"$$\\\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pos_encoding = positional_encoding(50, 512, True)\\n\",\n \"print(pos_encoding.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Masking\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])\\n\",\n \"masking(x, task='padding')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"masking(x, task='look_ahead')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Scaled dot product attention\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"$$\\\\Large{Attention(Q, K, V) = softmax_k(\\\\frac{QK^T}{\\\\sqrt{d_k}}) V} $$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def print_out(q, k, v):\\n\",\n \" temp_out, temp_attn = scaled_dot_product_attention(q, k, v, None)\\n\",\n \" print ('Attention weights are:')\\n\",\n \" print (temp_attn)\\n\",\n \" print ('Output is:')\\n\",\n \" print (temp_out)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"np.set_printoptions(suppress=True)\\n\",\n \"\\n\",\n \"temp_k = tf.constant([[10,0,0],\\n\",\n \" [0,10,0],\\n\",\n \" [0,0,10],\\n\",\n \" [0,0,10]], dtype=tf.float32)\\n\",\n \"\\n\",\n \"temp_v = tf.constant([[ 1,0],\\n\",\n \" [ 10,0],\\n\",\n \" [ 100,5],\\n\",\n \" [1000,6]], dtype=tf.float32)\\n\",\n \"\\n\",\n \"temp_q = tf.constant([[0, 10, 0]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"temp_q = tf.constant([[0, 0, 10]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"temp_q = tf.constant([[0, 0, 10], [0, 10, 0], [10, 10, 0]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Multi-head attention\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- **Tips: Dimention-level split**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"temp_mha = multihead_attention(d_model=512, num_heads=8)\\n\",\n \"y = tf.random.uniform((1, 50, 512))\\n\",\n \"out, attn = temp_mha(y, k=y, q=y, mask=None)\\n\",\n \"out.shape, attn.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Pointwise feed forward network\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_ffn = pointwise_feedforward(512, 2048)\\n\",\n \"sample_ffn(tf.random.uniform((64, 50, 512))).shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Whole model (Encoder & Decoder)\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Encoder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_encoder_layer = EncoderBlock(512, 8, 2048)\\n\",\n \"sample_encoder_layer_output, _ = sample_encoder_layer(tf.random.uniform((64, 43, 512)), False, None)\\n\",\n \"sample_encoder_layer_output.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Decoder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_decoder_layer = DecoderBlock(512, 8, 2048)\\n\",\n \"\\n\",\n \"sample_decoder_layer_output, _, _ = sample_decoder_layer(\\n\",\n \" tf.random.uniform((64, 50, 512)), sample_encoder_layer_output, \\n\",\n \" False, None, None)\\n\",\n \"\\n\",\n \"sample_decoder_layer_output.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Packed Encoder & Decoder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_encoder = Encoder(num_blocks=2, d_model=512, num_heads=8, dff=2048, input_vocab_size=8500, plot_pos_embedding=False)\\n\",\n \"attn_dict = {}\\n\",\n \"sample_encoder_output, attn_dict = sample_encoder(tf.random.uniform((64, 62)), training=False, padding_mask=None, attn_dict=attn_dict)\\n\",\n \"sample_encoder_output.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_decoder = Decoder(num_blocks=2, d_model=512, num_heads=8, dff=2048, target_vocab_size=8000, plot_pos_embedding=False)\\n\",\n \"output, attn_dict = sample_decoder(tf.random.uniform((64, 26)), \\n\",\n \" enc_output=sample_encoder_output, \\n\",\n \" training=False, look_ahead_mask=None, \\n\",\n \" padding_mask=None, attn_dict=attn_dict)\\n\",\n \"output.shape, attn_dict['decoder_layer2_block'].shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Transformer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sample_transformer = Transformer(num_blocks=2, d_model=512, num_heads=8, dff=2048, input_vocab_size=8500, target_vocab_size=8000, plot_pos_embedding=False)\\n\",\n \"\\n\",\n \"temp_input = tf.random.uniform((64, 62))\\n\",\n \"temp_target = tf.random.uniform((64, 26))\\n\",\n \"\\n\",\n \"fn_out, _ = sample_transformer(temp_input, \\n\",\n \" temp_target, \\n\",\n \" training=False, \\n\",\n \" enc_padding_mask=None, \\n\",\n \" look_ahead_mask=None,\\n\",\n \" dec_padding_mask=None)\\n\",\n \"\\n\",\n \"fn_out.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_layers = pm.num_block\\n\",\n \"d_model = pm.d_model\\n\",\n \"dff = pm.dff\\n\",\n \"num_heads = pm.num_heads\\n\",\n \"\\n\",\n \"input_vocab_size = len(en2idx)\\n\",\n \"target_vocab_size = len(de2idx)\\n\",\n \"dropout_rate = pm.dropout_rate\\n\",\n \"\\n\",\n \"EPOCHS = pm.num_epochs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Learning rate schedule\\n\",\n \"$$\\\\Large{lrate = d_{model}^{-0.5} * min(step{\\\\_}num^{-0.5}, step{\\\\_}num * warmup{\\\\_}steps^{-1.5})}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"temp_learning_rate_schedule = LRSchedule(d_model)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12, 8))\\n\",\n \"plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\\n\",\n \"plt.ylabel(\\\"Learning Rate\\\")\\n\",\n \"plt.xlabel(\\\"Train Step\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with strategy.scope():\\n\",\n \" # 1、dataset\\n\",\n \" ## train_dataset = build_dataset(mode='array', batch_size=pm.batch_size * strategy.num_replicas_in_sync, cache_name='train_cache.tf-data', corpus=[src_train, tgt_train], is_training=True)\\n\",\n \" ## val_dataset = build_dataset(mode='array', batch_size=pm.batch_size * strategy.num_replicas_in_sync, cache_name='val_cache.tf-data', corpus=[src_val, tgt_val], is_training=True)\\n\",\n \" \\n\",\n \" train_dataset = build_dataset(mode='file', batch_size=pm.batch_size * strategy.num_replicas_in_sync, cache_name='train_cache.tf-data', filename=pm.train_record, is_training=True)\\n\",\n \" train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)\\n\",\n \" \\n\",\n \" # 2、loss function\\n\",\n \" loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)\\n\",\n \"\\n\",\n \" def loss_function(real, pred):\\n\",\n \" mask = tf.math.logical_not(tf.math.equal(real, 0))\\n\",\n \" loss_ = loss_object(real, pred)\\n\",\n \"\\n\",\n \" mask = tf.cast(mask, dtype=loss_.dtype)\\n\",\n \" loss_ *= mask\\n\",\n \"\\n\",\n \" return tf.reduce_mean(loss_), mask\\n\",\n \" \\n\",\n \" # 3、metrics to track loss and accuracy\\n\",\n \" train_loss = tf.keras.metrics.Mean(name='train_loss')\\n\",\n \" train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\\n\",\n \" \\n\",\n \" # 4、model config\\n\",\n \" transformer = Transformer(num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, pm.plot_pos_embedding, dropout_rate)\\n\",\n \" \\n\",\n \" learning_rate = LRSchedule(d_model)\\n\",\n \" optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=pm.beta_1, beta_2=pm.beta_2, epsilon=pm.epsilon)\\n\",\n \" \\n\",\n \" checkpoint_path = pm.ckpt_path\\n\",\n \"\\n\",\n \" ckpt = tf.train.Checkpoint(transformer=transformer, optimizer=optimizer)\\n\",\n \" ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\\n\",\n \"\\n\",\n \" if ckpt_manager.latest_checkpoint:\\n\",\n \" ckpt.restore(ckpt_manager.latest_checkpoint)\\n\",\n \" print ('Latest checkpoint restored!!')\\n\",\n \" \\n\",\n \" current_time = datetime.datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\")\\n\",\n \" log_dir = pm.logdir + '/gradient_tape/' + current_time\\n\",\n \" summary_writer = tf.summary.create_file_writer(log_dir)\\n\",\n \" \\n\",\n \" # 5、train step\\n\",\n \" def train_step(inp, tar):\\n\",\n \" tar_inp = tar[:, :-1]\\n\",\n \" tar_real = tar[:, 1:]\\n\",\n \"\\n\",\n \" enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)\\n\",\n \"\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" predictions, _ = transformer(inp, \\n\",\n \" tar_inp, \\n\",\n \" True, \\n\",\n \" enc_padding_mask, \\n\",\n \" combined_mask, \\n\",\n \" dec_padding_mask)\\n\",\n \"\\n\",\n \" loss, istarget = loss_function(tar_real, predictions)\\n\",\n \"\\n\",\n \" gradients = tape.gradient(loss, transformer.trainable_variables) \\n\",\n \" optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\\n\",\n \"\\n\",\n \" train_accuracy(tar_real, predictions, sample_weight=istarget)\\n\",\n \" \\n\",\n \" return loss\\n\",\n \" \\n\",\n \" @tf.function\\n\",\n \" def distributed_train_step(inp, tar):\\n\",\n \" per_replica_losses = strategy.experimental_run_v2(train_step, \\n\",\n \" args=(inp, tar, ))\\n\",\n \" return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)\\n\",\n \" \\n\",\n \" # 6、for loop\\n\",\n \" total_steps = 0\\n\",\n \" for epoch in range(EPOCHS):\\n\",\n \" start = time.time()\\n\",\n \"\\n\",\n \" train_loss.reset_states()\\n\",\n \" train_accuracy.reset_states()\\n\",\n \" \\n\",\n \" total_loss = 0.0\\n\",\n \" num_batches = 0\\n\",\n \" for (batch, (inp, tar)) in enumerate(train_dataset):\\n\",\n \" total_loss += distributed_train_step(inp, tar)\\n\",\n \" num_batches += 1\\n\",\n \" total_steps += 1\\n\",\n \"\\n\",\n \" if batch % 500 == 0:\\n\",\n \" with summary_writer.as_default():\\n\",\n \" tf.summary.scalar('loss', total_loss / num_batches, step=total_steps)\\n\",\n \" tf.summary.scalar('accuracy', train_accuracy.result() * 100, step=total_steps)\\n\",\n \" \\n\",\n \" print ('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\\n\",\n \" epoch + 1, batch, total_loss / num_batches, train_accuracy.result() * 100))\\n\",\n \" \\n\",\n \" train_loss(total_loss / num_batches)\\n\",\n \"\\n\",\n \" if (epoch + 1) % 5 == 0:\\n\",\n \" ckpt_save_path = ckpt_manager.save()\\n\",\n \" print ('Saving checkpoint for epoch {} at {}'.format(epoch + 1, ckpt_save_path))\\n\",\n \"\\n\",\n \" print ('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1, train_loss.result(), train_accuracy.result() * 100))\\n\",\n \" print ('Time taken for 1 epoch: {} secs\\\\n'.format(time.time() - start))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"val_dataset = build_dataset(mode='file', batch_size=pm.batch_size * strategy.num_replicas_in_sync, cache_name='val_cache.tf-data', filename=pm.test_record, is_training=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def evaluate(inp_sentence):\\n\",\n \" encoder_input = inp_sentence\\n\",\n \" \\n\",\n \" decoder_input = [2]\\n\",\n \" output = tf.expand_dims(decoder_input, 0)\\n\",\n \" output = tf.tile(output, [tf.shape(encoder_input)[0], 1])\\n\",\n \"\\n\",\n \" for i in range(pm.maxlen):\\n\",\n \" enc_padding_mask, combined_mask, dec_padding_mask = create_masks(encoder_input, output)\\n\",\n \"\\n\",\n \" predictions, attention_weights = transformer(encoder_input, \\n\",\n \" output,\\n\",\n \" False,\\n\",\n \" enc_padding_mask,\\n\",\n \" combined_mask,\\n\",\n \" dec_padding_mask)\\n\",\n \"\\n\",\n \" predictions = predictions[: ,-1:, :]\\n\",\n \" predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\\n\",\n \"\\n\",\n \" output = tf.concat([output, predicted_id], axis=-1)\\n\",\n \"\\n\",\n \" return output, attention_weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def cut_by_end(samples):\\n\",\n \" output_list = np.zeros(tf.shape(samples))\\n\",\n \" for i, sample in enumerate(samples):\\n\",\n \" dtype = sample.dtype\\n\",\n \" idx = tf.where(tf.equal(sample, 3))\\n\",\n \" \\n\",\n \" flag = tf.where(tf.equal(tf.size(idx), 0), 1, 0)\\n\",\n \" if flag:\\n\",\n \" output_list[i] = sample\\n\",\n \" else:\\n\",\n \" indices = tf.cast(idx[0, 0], dtype)\\n\",\n \" output_list[i] = tf.concat([sample[:indices], tf.zeros(tf.shape(sample)[0] - indices, dtype=dtype)], axis=0)\\n\",\n \"\\n\",\n \" return tf.cast(output_list, dtype)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eval_log = os.path.join(pm.eval_log_path, '{}_eval.tsv'.format(pm.project_name))\\n\",\n \"if not os.path.exists(pm.eval_log_path):\\n\",\n \" os.makedirs(pm.eval_log_path)\\n\",\n \"eval_file = open(eval_log, 'w', encoding='utf-8')\\n\",\n \"\\n\",\n \"start = time.time()\\n\",\n \"count, scores = 0, 0\\n\",\n \"for (batch, (inp, tar)) in enumerate(val_dataset):\\n\",\n \" prediction, attention_weights = evaluate(inp)\\n\",\n \" prediction = cut_by_end(prediction)\\n\",\n \" \\n\",\n \" preds, tars = [], []\\n\",\n \" for source, real_tar, pred in zip(inp, tar, prediction):\\n\",\n \" s = \\\" \\\".join([idx2en.get(i, 1) for i in source.numpy() if i < len(idx2en) and i not in [0, 2, 3]])\\n\",\n \" t = \\\"\\\".join([idx2de.get(i, 1) for i in real_tar.numpy() if i < len(idx2de) and i not in [0, 2, 3]])\\n\",\n \" p = \\\"\\\".join([idx2de.get(i, 1) for i in pred.numpy() if i < len(idx2de) and i not in [0, 2, 3]])\\n\",\n \" \\n\",\n \" preds.append(p)\\n\",\n \" tars.append([t])\\n\",\n \" \\n\",\n \" eval_file.write('-Source : {}\\\\n-Target : {}\\\\n-Pred : {}\\\\n\\\\n'.format(s, t, p))\\n\",\n \" eval_file.flush()\\n\",\n \" \\n\",\n \" scores += bleu_metrics(tars, preds, False, 3, True)\\n\",\n \" count += 1\\n\",\n \"\\n\",\n \"eval_file.write('-BLEU Score : {:.4f}'.format(scores / count))\\n\",\n \"eval_file.close()\\n\",\n \"\\n\",\n \"print(\\\"MSG : Done for evalutation ... Totolly {:.2f} sec.\\\".format(time.time() - start))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def predict(inp_sentence):\\n\",\n \" start_token = [2]\\n\",\n \" end_token = [3]\\n\",\n \"\\n\",\n \" inp_sentence = start_token + [en2idx.get(word, 1) for word in inp_sentence.split()] + end_token\\n\",\n \" encoder_input = tf.expand_dims(inp_sentence, 0)\\n\",\n \" \\n\",\n \" decoder_input = [2]\\n\",\n \" output = tf.expand_dims(decoder_input, 0)\\n\",\n \"\\n\",\n \" for i in range(pm.maxlen):\\n\",\n \" enc_padding_mask, combined_mask, dec_padding_mask = create_masks(encoder_input, output)\\n\",\n \"\\n\",\n \" predictions, attention_weights = transformer(encoder_input, \\n\",\n \" output,\\n\",\n \" False,\\n\",\n \" enc_padding_mask,\\n\",\n \" combined_mask,\\n\",\n \" dec_padding_mask)\\n\",\n \"\\n\",\n \" predictions = predictions[: ,-1:, :]\\n\",\n \" predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\\n\",\n \"\\n\",\n \" if tf.equal(predicted_id, 3):\\n\",\n \" return tf.squeeze(output, axis=0), attention_weights\\n\",\n \"\\n\",\n \" output = tf.concat([output, predicted_id], axis=-1)\\n\",\n \"\\n\",\n \" return tf.squeeze(output, axis=0), attention_weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def translate(sentence, plot=''):\\n\",\n \" result, attention_weights = predict(sentence)\\n\",\n \" \\n\",\n \" predicted_sentence = [idx2de.get(i, 1) for i in result.numpy() if i < len(idx2de) and i not in [0, 2, 3]]\\n\",\n \"\\n\",\n \" print('Input: {}'.format(sentence))\\n\",\n \" print('Predicted translation: {}'.format(\\\" \\\".join(predicted_sentence)))\\n\",\n \"\\n\",\n \" if plot:\\n\",\n \" plot_attention_weights(attention_weights, sentence, result, plot)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"translate(\\\"明 天 就 要 上 班 了\\\", plot='decoder_layer4_block')\\n\",\n \"print(\\\"Real translation: 還好我沒工作QQ\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "LICENSE",
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},
{
"path": "README.md",
"content": "An Implementation of Attention is all you need with Chinese Corpus\n===\n The code is an implementation of Paper [Attention is all you need](https://arxiv.org/abs/1706.03762) working for dialogue generation tasks like: **Chatbot**、 **Text Generation** and so on. \n **Thanks to every friends who have raised issues and helped solve them. Your contribution is very important for the improvement of this project. Due to the limited support of the 'static graph mode' in coding, we decided to move the features to 2.0.0-beta1 version. However if you worry about the problems from docker building and service creation with version issues, we still keep an old version of the code written by eager mode using tensorflow 1.12.x version to refer.**\n\n# Documents\n```\n|-- root/\n |-- data/\n |-- src-train.csv\n |-- src-val.csv\n |-- tgt-train.csv\n `-- tgt-val.csv\n |-- old_version/\n |-- data_loader.py\n |-- eval.py\n |-- make_dic.py\n |-- modules.py\n |-- params.py\n |-- requirements.txt\n `-- train.py\n |-- tf1.12.0-eager/\n |-- bleu.py\n |-- main.ipynb\n |-- modules.py\n |-- params.py\n |-- requirements.txt\n `-- utils.py\n |-- images/\n |-- bleu.py\n |-- main-v2.ipynb\n |-- modules-v2.py\n |-- params.py\n |-- requirements.txt\n `-- utils-v2.py\n```\n\n# Requirements\n- Numpy >= 1.13.1\n- Tensorflow-gpu == 1.12.0\n- **Tensorflow-gpu == 2.0.0-beta1**\n - cudatoolkit >= 10.0\n - cudnn >= 7.4 \n - nvidia cuda driver version >= 410.x\n- tqdm\n- nltk\n- jupyter notebook\n\n# Construction\n As we all know the Translation System can be used in implementing conversational model just by replacing the paris of two different sentences to questions and answers. After all, the basic conversation model named \"Sequence-to-Sequence\" is develped from translation system. Therefore, why we not to improve the efficiency of conversation model in generating dialogues? \n\n\n
![](\"https://github.com/EternalFeather/Transformer-in-generating-dialogue/blob/master/images/transformer.png\")
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