├── .ipynb_checkpoints
└── main-v2-checkpoint.ipynb
├── LICENSE
├── README.md
├── __init__.py
├── bleu.py
├── data
├── src-train.txt
├── src-val.txt
├── tgt-train.txt
└── tgt-val.txt
├── images
├── learning_rate.png
├── multi_head_attention.png
├── scaled_attention.png
└── transformer.png
├── main-v2.ipynb
├── modules_v2.py
├── old_version
├── __init__.py
├── data_loader.py
├── eval.py
├── make_dic.py
├── modules.py
├── params.py
├── requirements.txt
└── train.py
├── params.py
├── requirements.txt
├── tf1.12.0-eager
├── __init__.py
├── bleu.py
├── main.ipynb
├── modules.py
├── params.py
├── requirements.txt
└── utils.py
└── utils_v2.py
/.ipynb_checkpoints/main-v2-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "from __future__ import absolute_import, division, print_function, unicode_literals\n",
10 | "\n",
11 | "import tensorflow as tf\n",
12 | "\n",
13 | "import time\n",
14 | "import datetime\n",
15 | "import os\n",
16 | "from tqdm import tqdm\n",
17 | "import numpy as np\n",
18 | "import matplotlib.pyplot as plt\n",
19 | "plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签\n",
20 | "plt.rcParams['axes.unicode_minus']=False\n",
21 | "\n",
22 | "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0, 1, 2, 3\" # 添加可用的gpu\n",
23 | "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
24 | "for device in physical_devices:\n",
25 | " tf.config.experimental.set_memory_growth(device, True)\n",
26 | "\n",
27 | "from params import Params as pm\n",
28 | "from utils_v2 import en2idx, idx2en, de2idx, idx2de, dump2record, build_dataset, LRSchedule, masking, create_masks, plot_attention_weights\n",
29 | "from bleu import bleu_metrics"
30 | ]
31 | },
32 | {
33 | "cell_type": "code",
34 | "execution_count": null,
35 | "metadata": {},
36 | "outputs": [],
37 | "source": [
38 | "tf.__version__"
39 | ]
40 | },
41 | {
42 | "cell_type": "markdown",
43 | "metadata": {},
44 | "source": [
45 | "---"
46 | ]
47 | },
48 | {
49 | "cell_type": "code",
50 | "execution_count": null,
51 | "metadata": {},
52 | "outputs": [],
53 | "source": [
54 | "strategy = tf.distribute.MirroredStrategy()\n",
55 | "\n",
56 | "print('Number of device: {}'.format(strategy.num_replicas_in_sync))"
57 | ]
58 | },
59 | {
60 | "cell_type": "code",
61 | "execution_count": null,
62 | "metadata": {},
63 | "outputs": [],
64 | "source": [
65 | "def get_data(corpus_file):\n",
66 | " return open(corpus_file, 'r', encoding='utf-8').read().splitlines()"
67 | ]
68 | },
69 | {
70 | "cell_type": "code",
71 | "execution_count": null,
72 | "metadata": {},
73 | "outputs": [],
74 | "source": [
75 | "src_train, src_val = get_data(pm.src_train), get_data(pm.src_test)\n",
76 | "tgt_train, tgt_val = get_data(pm.tgt_train), get_data(pm.tgt_test)"
77 | ]
78 | },
79 | {
80 | "cell_type": "code",
81 | "execution_count": null,
82 | "metadata": {},
83 | "outputs": [],
84 | "source": [
85 | "dump2record(pm.train_record, src_train, tgt_train)\n",
86 | "dump2record(pm.test_record, src_val, tgt_val)"
87 | ]
88 | },
89 | {
90 | "cell_type": "markdown",
91 | "metadata": {},
92 | "source": [
93 | "---"
94 | ]
95 | },
96 | {
97 | "cell_type": "code",
98 | "execution_count": null,
99 | "metadata": {},
100 | "outputs": [],
101 | "source": [
102 | "from modules_v2 import positional_encoding, scaled_dot_product_attention, multihead_attention, pointwise_feedforward, EncoderBlock, DecoderBlock, Encoder, Decoder, Transformer"
103 | ]
104 | },
105 | {
106 | "cell_type": "markdown",
107 | "metadata": {},
108 | "source": [
109 | "# Positional encoding\n",
110 | "$$\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\n",
111 | "$$\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$"
112 | ]
113 | },
114 | {
115 | "cell_type": "code",
116 | "execution_count": null,
117 | "metadata": {},
118 | "outputs": [],
119 | "source": [
120 | "pos_encoding = positional_encoding(50, 512, True)\n",
121 | "print(pos_encoding.shape)"
122 | ]
123 | },
124 | {
125 | "cell_type": "markdown",
126 | "metadata": {},
127 | "source": [
128 | "# Masking"
129 | ]
130 | },
131 | {
132 | "cell_type": "code",
133 | "execution_count": null,
134 | "metadata": {},
135 | "outputs": [],
136 | "source": [
137 | "x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])\n",
138 | "masking(x, task='padding')"
139 | ]
140 | },
141 | {
142 | "cell_type": "code",
143 | "execution_count": null,
144 | "metadata": {},
145 | "outputs": [],
146 | "source": [
147 | "masking(x, task='look_ahead')"
148 | ]
149 | },
150 | {
151 | "cell_type": "markdown",
152 | "metadata": {},
153 | "source": [
154 | "# Scaled dot product attention"
155 | ]
156 | },
157 | {
158 | "cell_type": "markdown",
159 | "metadata": {},
160 | "source": [
161 | "\n",
162 | "$$\\Large{Attention(Q, K, V) = softmax_k(\\frac{QK^T}{\\sqrt{d_k}}) V} $$"
163 | ]
164 | },
165 | {
166 | "cell_type": "code",
167 | "execution_count": null,
168 | "metadata": {},
169 | "outputs": [],
170 | "source": [
171 | "def print_out(q, k, v):\n",
172 | " temp_out, temp_attn = scaled_dot_product_attention(q, k, v, None)\n",
173 | " print ('Attention weights are:')\n",
174 | " print (temp_attn)\n",
175 | " print ('Output is:')\n",
176 | " print (temp_out)"
177 | ]
178 | },
179 | {
180 | "cell_type": "code",
181 | "execution_count": null,
182 | "metadata": {},
183 | "outputs": [],
184 | "source": [
185 | "np.set_printoptions(suppress=True)\n",
186 | "\n",
187 | "temp_k = tf.constant([[10,0,0],\n",
188 | " [0,10,0],\n",
189 | " [0,0,10],\n",
190 | " [0,0,10]], dtype=tf.float32)\n",
191 | "\n",
192 | "temp_v = tf.constant([[ 1,0],\n",
193 | " [ 10,0],\n",
194 | " [ 100,5],\n",
195 | " [1000,6]], dtype=tf.float32)\n",
196 | "\n",
197 | "temp_q = tf.constant([[0, 10, 0]], dtype=tf.float32)\n",
198 | "print_out(temp_q, temp_k, temp_v)"
199 | ]
200 | },
201 | {
202 | "cell_type": "code",
203 | "execution_count": null,
204 | "metadata": {},
205 | "outputs": [],
206 | "source": [
207 | "temp_q = tf.constant([[0, 0, 10]], dtype=tf.float32)\n",
208 | "print_out(temp_q, temp_k, temp_v)"
209 | ]
210 | },
211 | {
212 | "cell_type": "code",
213 | "execution_count": null,
214 | "metadata": {},
215 | "outputs": [],
216 | "source": [
217 | "temp_q = tf.constant([[0, 0, 10], [0, 10, 0], [10, 10, 0]], dtype=tf.float32)\n",
218 | "print_out(temp_q, temp_k, temp_v)"
219 | ]
220 | },
221 | {
222 | "cell_type": "markdown",
223 | "metadata": {},
224 | "source": [
225 | "# Multi-head attention"
226 | ]
227 | },
228 | {
229 | "cell_type": "markdown",
230 | "metadata": {},
231 | "source": [
232 | ""
233 | ]
234 | },
235 | {
236 | "cell_type": "markdown",
237 | "metadata": {},
238 | "source": [
239 | "- **Tips: Dimention-level split**"
240 | ]
241 | },
242 | {
243 | "cell_type": "code",
244 | "execution_count": null,
245 | "metadata": {},
246 | "outputs": [],
247 | "source": [
248 | "temp_mha = multihead_attention(d_model=512, num_heads=8)\n",
249 | "y = tf.random.uniform((1, 50, 512))\n",
250 | "out, attn = temp_mha(y, k=y, q=y, mask=None)\n",
251 | "out.shape, attn.shape"
252 | ]
253 | },
254 | {
255 | "cell_type": "markdown",
256 | "metadata": {},
257 | "source": [
258 | "# Pointwise feed forward network"
259 | ]
260 | },
261 | {
262 | "cell_type": "code",
263 | "execution_count": null,
264 | "metadata": {},
265 | "outputs": [],
266 | "source": [
267 | "sample_ffn = pointwise_feedforward(512, 2048)\n",
268 | "sample_ffn(tf.random.uniform((64, 50, 512))).shape"
269 | ]
270 | },
271 | {
272 | "cell_type": "markdown",
273 | "metadata": {},
274 | "source": [
275 | "# Whole model (Encoder & Decoder)\n",
276 | ""
277 | ]
278 | },
279 | {
280 | "cell_type": "markdown",
281 | "metadata": {},
282 | "source": [
283 | "## Encoder"
284 | ]
285 | },
286 | {
287 | "cell_type": "code",
288 | "execution_count": null,
289 | "metadata": {},
290 | "outputs": [],
291 | "source": [
292 | "sample_encoder_layer = EncoderBlock(512, 8, 2048)\n",
293 | "sample_encoder_layer_output, _ = sample_encoder_layer(tf.random.uniform((64, 43, 512)), False, None)\n",
294 | "sample_encoder_layer_output.shape"
295 | ]
296 | },
297 | {
298 | "cell_type": "markdown",
299 | "metadata": {},
300 | "source": [
301 | "## Decoder"
302 | ]
303 | },
304 | {
305 | "cell_type": "code",
306 | "execution_count": null,
307 | "metadata": {},
308 | "outputs": [],
309 | "source": [
310 | "sample_decoder_layer = DecoderBlock(512, 8, 2048)\n",
311 | "\n",
312 | "sample_decoder_layer_output, _, _ = sample_decoder_layer(\n",
313 | " tf.random.uniform((64, 50, 512)), sample_encoder_layer_output, \n",
314 | " False, None, None)\n",
315 | "\n",
316 | "sample_decoder_layer_output.shape"
317 | ]
318 | },
319 | {
320 | "cell_type": "markdown",
321 | "metadata": {},
322 | "source": [
323 | "## Packed Encoder & Decoder"
324 | ]
325 | },
326 | {
327 | "cell_type": "code",
328 | "execution_count": null,
329 | "metadata": {},
330 | "outputs": [],
331 | "source": [
332 | "sample_encoder = Encoder(num_blocks=2, d_model=512, num_heads=8, dff=2048, input_vocab_size=8500, plot_pos_embedding=False)\n",
333 | "attn_dict = {}\n",
334 | "sample_encoder_output, attn_dict = sample_encoder(tf.random.uniform((64, 62)), training=False, padding_mask=None, attn_dict=attn_dict)\n",
335 | "sample_encoder_output.shape"
336 | ]
337 | },
338 | {
339 | "cell_type": "code",
340 | "execution_count": null,
341 | "metadata": {},
342 | "outputs": [],
343 | "source": [
344 | "sample_decoder = Decoder(num_blocks=2, d_model=512, num_heads=8, dff=2048, target_vocab_size=8000, plot_pos_embedding=False)\n",
345 | "output, attn_dict = sample_decoder(tf.random.uniform((64, 26)), \n",
346 | " enc_output=sample_encoder_output, \n",
347 | " training=False, look_ahead_mask=None, \n",
348 | " padding_mask=None, attn_dict=attn_dict)\n",
349 | "output.shape, attn_dict['decoder_layer2_block'].shape"
350 | ]
351 | },
352 | {
353 | "cell_type": "markdown",
354 | "metadata": {},
355 | "source": [
356 | "# Transformer"
357 | ]
358 | },
359 | {
360 | "cell_type": "code",
361 | "execution_count": null,
362 | "metadata": {},
363 | "outputs": [],
364 | "source": [
365 | "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",
366 | "\n",
367 | "temp_input = tf.random.uniform((64, 62))\n",
368 | "temp_target = tf.random.uniform((64, 26))\n",
369 | "\n",
370 | "fn_out, _ = sample_transformer(temp_input, \n",
371 | " temp_target, \n",
372 | " training=False, \n",
373 | " enc_padding_mask=None, \n",
374 | " look_ahead_mask=None,\n",
375 | " dec_padding_mask=None)\n",
376 | "\n",
377 | "fn_out.shape"
378 | ]
379 | },
380 | {
381 | "cell_type": "markdown",
382 | "metadata": {},
383 | "source": [
384 | "# Training"
385 | ]
386 | },
387 | {
388 | "cell_type": "code",
389 | "execution_count": null,
390 | "metadata": {},
391 | "outputs": [],
392 | "source": [
393 | "num_layers = pm.num_block\n",
394 | "d_model = pm.d_model\n",
395 | "dff = pm.dff\n",
396 | "num_heads = pm.num_heads\n",
397 | "\n",
398 | "input_vocab_size = len(en2idx)\n",
399 | "target_vocab_size = len(de2idx)\n",
400 | "dropout_rate = pm.dropout_rate\n",
401 | "\n",
402 | "EPOCHS = pm.num_epochs"
403 | ]
404 | },
405 | {
406 | "cell_type": "markdown",
407 | "metadata": {},
408 | "source": [
409 | "- Learning rate schedule\n",
410 | "$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$"
411 | ]
412 | },
413 | {
414 | "cell_type": "code",
415 | "execution_count": null,
416 | "metadata": {},
417 | "outputs": [],
418 | "source": [
419 | "temp_learning_rate_schedule = LRSchedule(d_model)\n",
420 | "\n",
421 | "plt.figure(figsize=(12, 8))\n",
422 | "plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))\n",
423 | "plt.ylabel(\"Learning Rate\")\n",
424 | "plt.xlabel(\"Train Step\")"
425 | ]
426 | },
427 | {
428 | "cell_type": "markdown",
429 | "metadata": {},
430 | "source": [
431 | "---"
432 | ]
433 | },
434 | {
435 | "cell_type": "code",
436 | "execution_count": null,
437 | "metadata": {},
438 | "outputs": [],
439 | "source": [
440 | "with strategy.scope():\n",
441 | " # 1、dataset\n",
442 | " ## 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",
443 | " ## 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",
444 | " \n",
445 | " 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",
446 | " train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)\n",
447 | " \n",
448 | " # 2、loss function\n",
449 | " loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)\n",
450 | "\n",
451 | " def loss_function(real, pred):\n",
452 | " mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
453 | " loss_ = loss_object(real, pred)\n",
454 | "\n",
455 | " mask = tf.cast(mask, dtype=loss_.dtype)\n",
456 | " loss_ *= mask\n",
457 | "\n",
458 | " return tf.reduce_mean(loss_), mask\n",
459 | " \n",
460 | " # 3、metrics to track loss and accuracy\n",
461 | " train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
462 | " train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
463 | " \n",
464 | " # 4、model config\n",
465 | " transformer = Transformer(num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, pm.plot_pos_embedding, dropout_rate)\n",
466 | " \n",
467 | " learning_rate = LRSchedule(d_model)\n",
468 | " optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=pm.beta_1, beta_2=pm.beta_2, epsilon=pm.epsilon)\n",
469 | " \n",
470 | " checkpoint_path = pm.ckpt_path\n",
471 | "\n",
472 | " ckpt = tf.train.Checkpoint(transformer=transformer, optimizer=optimizer)\n",
473 | " ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
474 | "\n",
475 | " if ckpt_manager.latest_checkpoint:\n",
476 | " ckpt.restore(ckpt_manager.latest_checkpoint)\n",
477 | " print ('Latest checkpoint restored!!')\n",
478 | " \n",
479 | " current_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
480 | " log_dir = pm.logdir + '/gradient_tape/' + current_time\n",
481 | " summary_writer = tf.summary.create_file_writer(log_dir)\n",
482 | " \n",
483 | " # 5、train step\n",
484 | " def train_step(inp, tar):\n",
485 | " tar_inp = tar[:, :-1]\n",
486 | " tar_real = tar[:, 1:]\n",
487 | "\n",
488 | " enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)\n",
489 | "\n",
490 | " with tf.GradientTape() as tape:\n",
491 | " predictions, _ = transformer(inp, \n",
492 | " tar_inp, \n",
493 | " True, \n",
494 | " enc_padding_mask, \n",
495 | " combined_mask, \n",
496 | " dec_padding_mask)\n",
497 | "\n",
498 | " loss, istarget = loss_function(tar_real, predictions)\n",
499 | "\n",
500 | " gradients = tape.gradient(loss, transformer.trainable_variables) \n",
501 | " optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\n",
502 | "\n",
503 | " train_accuracy(tar_real, predictions, sample_weight=istarget)\n",
504 | " \n",
505 | " return loss\n",
506 | " \n",
507 | " @tf.function\n",
508 | " def distributed_train_step(inp, tar):\n",
509 | " per_replica_losses = strategy.experimental_run_v2(train_step, \n",
510 | " args=(inp, tar, ))\n",
511 | " return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)\n",
512 | " \n",
513 | " # 6、for loop\n",
514 | " total_steps = 0\n",
515 | " for epoch in range(EPOCHS):\n",
516 | " start = time.time()\n",
517 | "\n",
518 | " train_loss.reset_states()\n",
519 | " train_accuracy.reset_states()\n",
520 | " \n",
521 | " total_loss = 0.0\n",
522 | " num_batches = 0\n",
523 | " for (batch, (inp, tar)) in enumerate(train_dataset):\n",
524 | " total_loss += distributed_train_step(inp, tar)\n",
525 | " num_batches += 1\n",
526 | " total_steps += 1\n",
527 | "\n",
528 | " if batch % 500 == 0:\n",
529 | " with summary_writer.as_default():\n",
530 | " tf.summary.scalar('loss', total_loss / num_batches, step=total_steps)\n",
531 | " tf.summary.scalar('accuracy', train_accuracy.result() * 100, step=total_steps)\n",
532 | " \n",
533 | " print ('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n",
534 | " epoch + 1, batch, total_loss / num_batches, train_accuracy.result() * 100))\n",
535 | " \n",
536 | " train_loss(total_loss / num_batches)\n",
537 | "\n",
538 | " if (epoch + 1) % 5 == 0:\n",
539 | " ckpt_save_path = ckpt_manager.save()\n",
540 | " print ('Saving checkpoint for epoch {} at {}'.format(epoch + 1, ckpt_save_path))\n",
541 | "\n",
542 | " print ('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1, train_loss.result(), train_accuracy.result() * 100))\n",
543 | " print ('Time taken for 1 epoch: {} secs\\n'.format(time.time() - start))"
544 | ]
545 | },
546 | {
547 | "cell_type": "markdown",
548 | "metadata": {},
549 | "source": [
550 | "---"
551 | ]
552 | },
553 | {
554 | "cell_type": "code",
555 | "execution_count": null,
556 | "metadata": {},
557 | "outputs": [],
558 | "source": [
559 | "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)"
560 | ]
561 | },
562 | {
563 | "cell_type": "code",
564 | "execution_count": null,
565 | "metadata": {},
566 | "outputs": [],
567 | "source": [
568 | "def evaluate(inp_sentence):\n",
569 | " encoder_input = inp_sentence\n",
570 | " \n",
571 | " decoder_input = [2]\n",
572 | " output = tf.expand_dims(decoder_input, 0)\n",
573 | " output = tf.tile(output, [tf.shape(encoder_input)[0], 1])\n",
574 | "\n",
575 | " for i in range(pm.maxlen):\n",
576 | " enc_padding_mask, combined_mask, dec_padding_mask = create_masks(encoder_input, output)\n",
577 | "\n",
578 | " predictions, attention_weights = transformer(encoder_input, \n",
579 | " output,\n",
580 | " False,\n",
581 | " enc_padding_mask,\n",
582 | " combined_mask,\n",
583 | " dec_padding_mask)\n",
584 | "\n",
585 | " predictions = predictions[: ,-1:, :]\n",
586 | " predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\n",
587 | "\n",
588 | " output = tf.concat([output, predicted_id], axis=-1)\n",
589 | "\n",
590 | " return output, attention_weights"
591 | ]
592 | },
593 | {
594 | "cell_type": "code",
595 | "execution_count": null,
596 | "metadata": {},
597 | "outputs": [],
598 | "source": [
599 | "def cut_by_end(samples):\n",
600 | " output_list = np.zeros(tf.shape(samples))\n",
601 | " for i, sample in enumerate(samples):\n",
602 | " dtype = sample.dtype\n",
603 | " idx = tf.where(tf.equal(sample, 3))\n",
604 | " \n",
605 | " flag = tf.where(tf.equal(tf.size(idx), 0), 1, 0)\n",
606 | " if flag:\n",
607 | " output_list[i] = sample\n",
608 | " else:\n",
609 | " indices = tf.cast(idx[0, 0], dtype)\n",
610 | " output_list[i] = tf.concat([sample[:indices], tf.zeros(tf.shape(sample)[0] - indices, dtype=dtype)], axis=0)\n",
611 | "\n",
612 | " return tf.cast(output_list, dtype)"
613 | ]
614 | },
615 | {
616 | "cell_type": "code",
617 | "execution_count": null,
618 | "metadata": {},
619 | "outputs": [],
620 | "source": [
621 | "eval_log = os.path.join(pm.eval_log_path, '{}_eval.tsv'.format(pm.project_name))\n",
622 | "if not os.path.exists(pm.eval_log_path):\n",
623 | " os.makedirs(pm.eval_log_path)\n",
624 | "eval_file = open(eval_log, 'w', encoding='utf-8')\n",
625 | "\n",
626 | "start = time.time()\n",
627 | "count, scores = 0, 0\n",
628 | "for (batch, (inp, tar)) in enumerate(val_dataset):\n",
629 | " prediction, attention_weights = evaluate(inp)\n",
630 | " prediction = cut_by_end(prediction)\n",
631 | " \n",
632 | " preds, tars = [], []\n",
633 | " for source, real_tar, pred in zip(inp, tar, prediction):\n",
634 | " s = \" \".join([idx2en.get(i, 1) for i in source.numpy() if i < len(idx2en) and i not in [0, 2, 3]])\n",
635 | " t = \"\".join([idx2de.get(i, 1) for i in real_tar.numpy() if i < len(idx2de) and i not in [0, 2, 3]])\n",
636 | " p = \"\".join([idx2de.get(i, 1) for i in pred.numpy() if i < len(idx2de) and i not in [0, 2, 3]])\n",
637 | " \n",
638 | " preds.append(p)\n",
639 | " tars.append([t])\n",
640 | " \n",
641 | " eval_file.write('-Source : {}\\n-Target : {}\\n-Pred : {}\\n\\n'.format(s, t, p))\n",
642 | " eval_file.flush()\n",
643 | " \n",
644 | " scores += bleu_metrics(tars, preds, False, 3, True)\n",
645 | " count += 1\n",
646 | "\n",
647 | "eval_file.write('-BLEU Score : {:.4f}'.format(scores / count))\n",
648 | "eval_file.close()\n",
649 | "\n",
650 | "print(\"MSG : Done for evalutation ... Totolly {:.2f} sec.\".format(time.time() - start))"
651 | ]
652 | },
653 | {
654 | "cell_type": "code",
655 | "execution_count": null,
656 | "metadata": {},
657 | "outputs": [],
658 | "source": [
659 | "def predict(inp_sentence):\n",
660 | " start_token = [2]\n",
661 | " end_token = [3]\n",
662 | "\n",
663 | " inp_sentence = start_token + [en2idx.get(word, 1) for word in inp_sentence.split()] + end_token\n",
664 | " encoder_input = tf.expand_dims(inp_sentence, 0)\n",
665 | " \n",
666 | " decoder_input = [2]\n",
667 | " output = tf.expand_dims(decoder_input, 0)\n",
668 | "\n",
669 | " for i in range(pm.maxlen):\n",
670 | " enc_padding_mask, combined_mask, dec_padding_mask = create_masks(encoder_input, output)\n",
671 | "\n",
672 | " predictions, attention_weights = transformer(encoder_input, \n",
673 | " output,\n",
674 | " False,\n",
675 | " enc_padding_mask,\n",
676 | " combined_mask,\n",
677 | " dec_padding_mask)\n",
678 | "\n",
679 | " predictions = predictions[: ,-1:, :]\n",
680 | " predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\n",
681 | "\n",
682 | " if tf.equal(predicted_id, 3):\n",
683 | " return tf.squeeze(output, axis=0), attention_weights\n",
684 | "\n",
685 | " output = tf.concat([output, predicted_id], axis=-1)\n",
686 | "\n",
687 | " return tf.squeeze(output, axis=0), attention_weights"
688 | ]
689 | },
690 | {
691 | "cell_type": "code",
692 | "execution_count": null,
693 | "metadata": {},
694 | "outputs": [],
695 | "source": [
696 | "def translate(sentence, plot=''):\n",
697 | " result, attention_weights = predict(sentence)\n",
698 | " \n",
699 | " predicted_sentence = [idx2de.get(i, 1) for i in result.numpy() if i < len(idx2de) and i not in [0, 2, 3]]\n",
700 | "\n",
701 | " print('Input: {}'.format(sentence))\n",
702 | " print('Predicted translation: {}'.format(\" \".join(predicted_sentence)))\n",
703 | "\n",
704 | " if plot:\n",
705 | " plot_attention_weights(attention_weights, sentence, result, plot)"
706 | ]
707 | },
708 | {
709 | "cell_type": "code",
710 | "execution_count": null,
711 | "metadata": {},
712 | "outputs": [],
713 | "source": [
714 | "translate(\"明 天 就 要 上 班 了\", plot='decoder_layer4_block')\n",
715 | "print(\"Real translation: 還好我沒工作QQ\")"
716 | ]
717 | },
718 | {
719 | "cell_type": "code",
720 | "execution_count": null,
721 | "metadata": {},
722 | "outputs": [],
723 | "source": []
724 | }
725 | ],
726 | "metadata": {
727 | "kernelspec": {
728 | "display_name": "Python 3",
729 | "language": "python",
730 | "name": "python3"
731 | },
732 | "language_info": {
733 | "codemirror_mode": {
734 | "name": "ipython",
735 | "version": 3
736 | },
737 | "file_extension": ".py",
738 | "mimetype": "text/x-python",
739 | "name": "python",
740 | "nbconvert_exporter": "python",
741 | "pygments_lexer": "ipython3",
742 | "version": "3.7.1"
743 | }
744 | },
745 | "nbformat": 4,
746 | "nbformat_minor": 2
747 | }
748 |
--------------------------------------------------------------------------------
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/README.md:
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1 | An Implementation of Attention is all you need with Chinese Corpus
2 | ===
3 | 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.
4 | **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.**
5 |
6 | # Documents
7 | ```
8 | |-- root/
9 | |-- data/
10 | |-- src-train.csv
11 | |-- src-val.csv
12 | |-- tgt-train.csv
13 | `-- tgt-val.csv
14 | |-- old_version/
15 | |-- data_loader.py
16 | |-- eval.py
17 | |-- make_dic.py
18 | |-- modules.py
19 | |-- params.py
20 | |-- requirements.txt
21 | `-- train.py
22 | |-- tf1.12.0-eager/
23 | |-- bleu.py
24 | |-- main.ipynb
25 | |-- modules.py
26 | |-- params.py
27 | |-- requirements.txt
28 | `-- utils.py
29 | |-- images/
30 | |-- bleu.py
31 | |-- main-v2.ipynb
32 | |-- modules-v2.py
33 | |-- params.py
34 | |-- requirements.txt
35 | `-- utils-v2.py
36 | ```
37 |
38 | # Requirements
39 | - Numpy >= 1.13.1
40 | - Tensorflow-gpu == 1.12.0
41 | - **Tensorflow-gpu == 2.0.0-beta1**
42 | - cudatoolkit >= 10.0
43 | - cudnn >= 7.4
44 | - nvidia cuda driver version >= 410.x
45 | - tqdm
46 | - nltk
47 | - jupyter notebook
48 |
49 | # Construction
50 | 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?
51 |
52 |
53 |
54 |
77 |
} = sin(pos / 10000^{2i / d_{model}})})
78 |
81 |
} = cos(pos / 10000^{2i / d_{model}})})
82 |
91 |
 = softmax_k(\frac{QK^T}{\sqrt{d_k}}) V})
92 |
95 |
96 |
102 |
103 |
112 |
})
113 |
116 |
117 |
207 |
208 |
213 |
214 |
221 |
222 |
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228 |