$ cat requirements.txt && pip install -r requirements.txt
tensorflow==2.3.0
numpy==1.18.5
nltk==3.5 # AKA natural language tokenizer
$ tensorboard --version
> 2.4.1
$ pip --version
> pip 21.0.1 from /Users/ays-dev/opt/anaconda3/envs/tf/lib/python3.7/site-packages/pip (python 3.7)
$ python --version
> Python 3.7.9
This model use three special tokens to control training and generation
0 <pad>
1 <start>
2 <stop>
For full list of tokens with their respective frequencies and indexes see : dic-en.txt and dic-fr.txt files
Train the model
python train.py
import tensorflow as tf
import numpy as np
from dataset import get_dataset, prepare_dataset
from model import get_model
dataset = get_dataset("fr-en")
print("Dataset loaded. Length:", len(dataset), "lines")
train_dataset = dataset[0:100000]
print("Train data loaded. Length:", len(train_dataset), "lines")
(encoder_input,
decoder_input,
decoder_output,
encoder_vocab,
decoder_vocab,
encoder_inverted_vocab,
decoder_inverted_vocab) = prepare_dataset(
train_dataset,
shuffle = False,
lowercase = True,
max_window_size = 20
)
transformer_model = get_model(
EMBEDDING_SIZE = 64,
ENCODER_VOCAB_SIZE = len(encoder_vocab),
DECODER_VOCAB_SIZE = len(decoder_vocab),
ENCODER_LAYERS = 2,
DECODER_LAYERS = 2,
NUMBER_HEADS = 4,
DENSE_LAYER_SIZE = 128
)
transformer_model.compile(
optimizer = "adam",
loss = [
"sparse_categorical_crossentropy"
],
metrics = [
"accuracy"
]
)
transformer_model.summary()
x = [np.array(encoder_input), np.array(decoder_input)]
y = np.array(decoder_output)
name = "transformer"
checkpoint_filepath = "./logs/transformer_ep-{epoch:02d}_loss-{loss:.2f}_acc-{accuracy:.2f}.ckpt"
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir = "logs/{}".format(name)
)
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath = checkpoint_filepath,
monitor = "val_accuracy",
mode = "max",
save_weights_only = True,
save_best_only = True,
verbose = True
)
early_stopping_callback = tf.keras.callbacks.EarlyStopping(
monitor = "val_accuracy",
mode = "max",
patience = 2,
min_delta = 0.001,
verbose = True
)
transformer_model.fit(
x,
y,
epochs = 15,
batch_size = 32,
validation_split = 0.1,
callbacks=[
model_checkpoint_callback,
tensorboard_callback,
early_stopping_callback
]
)Dataset loaded. Length: 185583 lines
Train data loaded. Length: 100000 lines
Model: "Transformer-Model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Encoder-Input (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
Decoder-Input (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
Encoder-Word-Embedding (WordEmb (None, None, 64) 1110016 Encoder-Input[0][0]
__________________________________________________________________________________________________
Decoder-Word-Embedding (WordEmb (None, None, 64) 579456 Decoder-Input[0][0]
__________________________________________________________________________________________________
Encoder-Positional-Embedding (P (None, None, 64) 0 Encoder-Word-Embedding[0][0]
__________________________________________________________________________________________________
Decoder-Positional-Embedding (P (None, None, 64) 0 Decoder-Word-Embedding[0][0]
__________________________________________________________________________________________________
Encoder (Encoder) (None, None, 64) 66944 Encoder-Positional-Embedding[0][0
__________________________________________________________________________________________________
Decoder (Decoder) (None, None, 64) 100480 Decoder-Positional-Embedding[0][0
Encoder[0][0]
__________________________________________________________________________________________________
Decoder-Output (Dense) (None, None, 9054) 588510 Decoder[0][0]
==================================================================================================
Total params: 2,445,406
Trainable params: 2,445,406
Non-trainable params: 0
__________________________________________________________________________________________________
Epoch 1/15
2813/2813 [==============================] - ETA: 0s - loss: 1.0506 - accuracy: 0.8396
Epoch 00001: val_accuracy improved from -inf to 0.84285, saving model to ./logs/transformer_ep-01_loss-1.05_acc-0.84.ckpt
2813/2813 [==============================] - 1010s 359ms/step - loss: 1.0506 - accuracy: 0.8396 - val_loss: 0.9235 - val_accuracy: 0.8429
Epoch 2/15
2813/2813 [==============================] - ETA: 0s - loss: 0.4577 - accuracy: 0.9142
Epoch 00002: val_accuracy improved from 0.84285 to 0.87188, saving model to ./logs/transformer_ep-02_loss-0.46_acc-0.91.ckpt
2813/2813 [==============================] - 962s 342ms/step - loss: 0.4577 - accuracy: 0.9142 - val_loss: 0.7430 - val_accuracy: 0.8719
Epoch 3/15
2813/2813 [==============================] - ETA: 0s - loss: 0.3159 - accuracy: 0.9340
Epoch 00003: val_accuracy improved from 0.87188 to 0.88687, saving model to ./logs/transformer_ep-03_loss-0.32_acc-0.93.ckpt
2813/2813 [==============================] - 959s 341ms/step - loss: 0.3159 - accuracy: 0.9340 - val_loss: 0.6626 - val_accuracy: 0.8869
...
Epoch 8/15
2813/2813 [==============================] - ETA: 0s - loss: 0.1545 - accuracy: 0.9600
Epoch 00008: val_accuracy improved from 0.89448 to 0.89624, saving model to ./logs/transformer_ep-08_loss-0.15_acc-0.96.ckpt
2813/2813 [==============================] - 910s 324ms/step - loss: 0.1545 - accuracy: 0.9600 - val_loss: 0.6334 - val_accuracy: 0.8962
Epoch 9/15
2813/2813 [==============================] - ETA: 0s - loss: 0.1441 - accuracy: 0.9621
Epoch 00009: val_accuracy improved from 0.89624 to 0.89654, saving model to ./logs/transformer_ep-09_loss-0.14_acc-0.96.ckpt
2813/2813 [==============================] - 903s 321ms/step - loss: 0.1441 - accuracy: 0.9621 - val_loss: 0.6387 - val_accuracy: 0.8965
Epoch 10/15
2813/2813 [==============================] - ETA: 0s - loss: 0.1346 - accuracy: 0.9640
Epoch 00010: val_accuracy did not improve from 0.89654
2813/2813 [==============================] - 913s 324ms/step - loss: 0.1346 - accuracy: 0.9640 - val_loss: 0.6730 - val_accuracy: 0.8933
Epoch 00010: early stopping
Do the prediction AKA do fr-en language translation here in this case
python predict.py
import tensorflow as tf
import numpy as np
from dataset import get_dataset, prepare_dataset
from model import get_model
from utils.make_translate import make_translate
dataset = get_dataset("fr-en")
print("Dataset loaded. Length:", len(dataset), "lines")
train_dataset = dataset[0:100000]
print("Train data loaded. Length:", len(train_dataset), "lines")
(encoder_input,
decoder_input,
decoder_output,
encoder_vocab,
decoder_vocab,
encoder_inverted_vocab,
decoder_inverted_vocab) = prepare_dataset(
train_dataset,
shuffle = False,
lowercase = True,
max_window_size = 20
)
transformer_model = get_model(
EMBEDDING_SIZE = 64,
ENCODER_VOCAB_SIZE = len(encoder_vocab),
DECODER_VOCAB_SIZE = len(decoder_vocab),
ENCODER_LAYERS = 2,
DECODER_LAYERS = 2,
NUMBER_HEADS = 4,
DENSE_LAYER_SIZE = 128
)
transformer_model.summary()
transformer_model.load_weights('./logs/transformer_ep-10_loss-0.14_acc-0.96.ckpt')
translate = make_translate(transformer_model, encoder_vocab, decoder_vocab, decoder_inverted_vocab)
translate("c'est une belle journée .")
translate("j'aime manger du gâteau .")
translate("c'est une bonne chose .")
translate("il faut faire à manger pour nourrir les gens .")
translate("tom a acheté un nouveau vélo .")Original: c'est une belle journée .
Traduction: it' s a beautiful day .
Original: j'aime manger du gâteau .
Traduction: i like to eat some cake .
Original: c'est une bonne chose .
Traduction: that' s a good thing .
Original: il faut faire à manger pour nourrir les gens .
Traduction: we have to feed the people .
Original: tom a acheté un nouveau vélo .
Traduction: tom bought a new bicycle .
Find best hyper-parameters combination using hparams plugin from tensorboard
python params.py
import tensorflow as tf
import numpy as np
from tensorboard.plugins.hparams import api as hp
from dataset import get_dataset, prepare_dataset
from model import get_model
dataset = get_dataset("fr-en")
train_dataset = dataset[0:150]
(encoder_input,
decoder_input,
decoder_output,
encoder_vocab,
decoder_vocab,
encoder_inverted_vocab,
decoder_inverted_vocab) = prepare_dataset(
train_dataset,
shuffle = True,
lowercase = True,
max_window_size = 20
)
x_train = [np.array(encoder_input[0:100]), np.array(decoder_input[0:100])]
y_train = np.array(decoder_output[0:100])
x_test = [np.array(encoder_input[100:150]), np.array(decoder_input[100:150])]
y_test = np.array(decoder_output[100:150])
BATCH_SIZE = hp.HParam("batch_num", hp.Discrete([32, 16]))
DENSE_NUM = hp.HParam("dense_num", hp.Discrete([512, 256]))
HEAD_NUM = hp.HParam("head_num", hp.Discrete([8, 4]))
EMBED_NUM = hp.HParam("embed_num", hp.Discrete([512, 256]))
LAYER_NUM = hp.HParam("layer_num", hp.Discrete([6, 4]))
with tf.summary.create_file_writer("logs/hparam_tuning").as_default():
hp.hparams_config(
hparams=[LAYER_NUM, HEAD_NUM, EMBED_NUM, DENSE_NUM, BATCH_SIZE],
metrics=[
hp.Metric("val_accuracy")
],
)
def train_test_model(hparams):
transformer_model = get_model(
EMBEDDING_SIZE = hparams[EMBED_NUM],
ENCODER_VOCAB_SIZE = len(encoder_vocab),
DECODER_VOCAB_SIZE = len(decoder_vocab),
ENCODER_LAYERS = hparams[LAYER_NUM],
DECODER_LAYERS = hparams[LAYER_NUM],
NUMBER_HEADS = hparams[HEAD_NUM],
DENSE_LAYER_SIZE = hparams[DENSE_NUM]
)
transformer_model.compile(
optimizer = "adam",
loss = ["sparse_categorical_crossentropy"],
metrics = ["accuracy"]
)
transformer_model.fit(x_train, y_train, epochs = 1, batch_size = hparams[BATCH_SIZE])
_, accuracy = transformer_model.evaluate(x_test, y_test)
return accuracy
def run(run_dir, hparams):
with tf.summary.create_file_writer(run_dir).as_default():
hp.hparams(hparams)
accuracy = train_test_model(hparams)
tf.summary.scalar("val_accuracy", accuracy, step = 1)
session_num = 0
for batch_num in BATCH_SIZE.domain.values:
for dense_num in DENSE_NUM.domain.values:
for num_heads in HEAD_NUM.domain.values:
for num_embed in EMBED_NUM.domain.values:
for num_units in LAYER_NUM.domain.values:
hparams = {
BATCH_SIZE: batch_num,
DENSE_NUM: dense_num,
HEAD_NUM: num_heads,
EMBED_NUM: num_embed,
LAYER_NUM: num_units
}
run_name = "run-%d" % session_num
print("--- Starting trial: %s" % run_name)
print({ h.name: hparams[h] for h in hparams })
run("logs/hparam_tuning/" + run_name, hparams)
session_num += 1--- Starting trial: run-0
{'batch_num': 16, 'dense_num': 256, 'head_num': 4, 'embed_num': 256, 'layer_num': 4}
7/7 [==============================] - 1s 136ms/step - loss: 2.3796 - accuracy: 0.5590
2/2 [==============================] - 0s 35ms/step - loss: 1.3560 - accuracy: 0.8240
--- Starting trial: run-1
{'batch_num': 16, 'dense_num': 256, 'head_num': 4, 'embed_num': 256, 'layer_num': 6}
7/7 [==============================] - 1s 203ms/step - loss: 2.9393 - accuracy: 0.5625
2/2 [==============================] - 0s 53ms/step - loss: 1.1636 - accuracy: 0.8240
...
--- Starting trial: run-29
{'batch_num': 32, 'dense_num': 512, 'head_num': 8, 'embed_num': 256, 'layer_num': 6}
4/4 [==============================] - 1s 323ms/step - loss: 6.8676 - accuracy: 0.2650
2/2 [==============================] - 0s 74ms/step - loss: 13.1447 - accuracy: 0.0500
--- Starting trial: run-30
{'batch_num': 32, 'dense_num': 512, 'head_num': 8, 'embed_num': 512, 'layer_num': 4}
4/4 [==============================] - 2s 513ms/step - loss: 7.4851 - accuracy: 0.2650
2/2 [==============================] - 0s 111ms/step - loss: 13.9821 - accuracy: 0.0310
--- Starting trial: run-31
{'batch_num': 32, 'dense_num': 512, 'head_num': 8, 'embed_num': 512, 'layer_num': 6}
4/4 [==============================] - 3s 761ms/step - loss: 7.1519 - accuracy: 0.2660
2/2 [==============================] - 0s 162ms/step - loss: 14.0015 - accuracy: 0.0500
Visualize your train, tests results, hyper-parameters optimizations, embedding and more with tensorboard !
For embedding visualization using PCA method for example - yellow dots are most frequent words
Blue and dark ones are less frequent words
Frequency is clipped at 60.000 max
$ ./tensorboard --logdir=./logs
Base (training on CPU) :
EMBEDDING_SIZE = 64
ENCODER_VOCAB_SIZE = 10000
DECODER_VOCAB_SIZE = 10000
ENCODER_LAYERS = 2
DECODER_LAYERS = 2
NUMBER_HEADS = 4
DENSE_LAYER_SIZE = 128
MAX_WINDOW_SIZE = 20
DATASET_SIZE = 100000
BATCH_SIZE = 32
-> 2.4 millions params (model size : ~28 Mo)
Big (training on GPU) :
EMBEDDING_SIZE = 512
ENCODER_VOCAB_SIZE = 30000
DECODER_VOCAB_SIZE = 30000
ENCODER_LAYERS = 6
DECODER_LAYERS = 6
NUMBER_HEADS = 8
DENSE_LAYER_SIZE = 1024
MAX_WINDOW_SIZE = 65
DATASET_SIZE = 200000
BATCH_SIZE = 32
-> 60 millions params (model size : ~600 Mo)
Coder un Transformer avec Tensorflow et Keras (LIVE) https://www.youtube.com/watch?v=mWA-PmxMBDk Thibault Neveu https://colab.research.google.com/drive/1akAsUAddF2-x57BJBA_gF-v4htyiR12y?usp=sharing
[TUTORIAL + CÓDIGO] Machine Translation usando redes TRANSFORMER (Python + Keras) https://www.youtube.com/watch?v=p2sTJYoIwj0 codificandobits https://github.com/codificandobits/Traductor_con_redes_Transformer/blob/master/machine-translation-transformers.ipynb
https://github.com/CyberZHG/keras-transformer CyberZHG
Dataset https://www.manythings.org/anki/
https://arxiv.org/abs/1706.03762 Attention Is All You Need (official paper)
https://github.com/Kyubyong/transformer/blob/master/model.py
https://github.com/pemywei/attention-is-all-you-need-tensorflow/blob/master/Transformer/model/nmt.py
https://github.com/tensorflow/models/blob/master/official/nlp/transformer/transformer.py
http://jalammar.github.io/illustrated-transformer/
https://nlp.seas.harvard.edu/2018/04/03/attention.html
http://statmt.org/wmt14/translation-task.html
https://huggingface.co/blog/encoder-decoder
http://vandergoten.ai/2018-09-18-attention-is-all-you-need/ (softmax implementation)
https://www.prhlt.upv.es/aigaion2/attachments/Transformer.pdf-54651af972703d4b1443fc03a8a9e9a6.pdf
https://github.com/lilianweng/transformer-tensorflow/blob/master/transformer.py
https://charon.me/posts/pytorch/pytorch_seq2seq_6/#training-the-seq2seq-model
https://trungtran.io/2019/04/29/create-the-transformer-with-tensorflow-2-0/
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