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This tutorial trains a Transformer model to translate Portuguese to English. This is an advanced example that assumes knowledge of text generation and attention.
The core idea behind the Transformer model is self-attention—the ability to attend to different positions of the input sequence to compute a representation of that sequence. Transformer creates stacks of self-attention layers and is explained below in the sections Scaled dot product attention and Multi-head attention.
A transformer model handles variable-sized input using stacks of self-attention layers instead of RNNs or CNNs. This general architecture has a number of advantages:
- It make no assumptions about the temporal/spatial relationships across the data. This is ideal for processing a set of objects (for example, StarCraft units).
- Layer outputs can be calculated in parallel, instead of a series like an RNN.
- Distant items can affect each other's output without passing through many RNN-steps, or convolution layers (see Scene Memory Transformer for example).
- It can learn long-range dependencies. This is a challenge in many sequence tasks.
The downsides of this architecture are:
- For a time-series, the output for a time-step is calculated from the entire history instead of only the inputs and current hidden-state. This may be less efficient.
- If the input does have a temporal/spatial relationship, like text, some positional encoding must be added or the model will effectively see a bag of words.
After training the model in this notebook, you will be able to input a Portuguese sentence and return the English translation.
!pip install -q tf-nightly
import tensorflow_datasets as tfds
import tensorflow as tf
import time
import numpy as np
import matplotlib.pyplot as plt
ERROR: tensorflow 2.1.0 has requirement gast==0.2.2, but you'll have gast 0.3.3 which is incompatible.
Setup input pipeline
Use TFDS to load the Portugese-English translation dataset from the TED Talks Open Translation Project.
This dataset contains approximately 50000 training examples, 1100 validation examples, and 2000 test examples.
examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True,
as_supervised=True)
train_examples, val_examples = examples['train'], examples['validation']
Downloading and preparing dataset ted_hrlr_translate/pt_to_en/1.0.0 (download: 124.94 MiB, generated: Unknown size, total: 124.94 MiB) to /home/kbuilder/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0... HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre… HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty… HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl… HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value=''))) Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteWFB69E/ted_hrlr_translate-train.tfrecord HBox(children=(FloatProgress(value=0.0, max=51785.0), HTML(value=''))) HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value=''))) Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteWFB69E/ted_hrlr_translate-validation.tfrecord HBox(children=(FloatProgress(value=0.0, max=1193.0), HTML(value=''))) HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value=''))) Shuffling and writing examples to /home/kbuilder/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteWFB69E/ted_hrlr_translate-test.tfrecord HBox(children=(FloatProgress(value=0.0, max=1803.0), HTML(value=''))) Dataset ted_hrlr_translate downloaded and prepared to /home/kbuilder/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0. Subsequent calls will reuse this data.
Create a custom subwords tokenizer from the training dataset.
tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus(
(en.numpy() for pt, en in train_examples), target_vocab_size=2**13)
tokenizer_pt = tfds.features.text.SubwordTextEncoder.build_from_corpus(
(pt.numpy() for pt, en in train_examples), target_vocab_size=2**13)
sample_string = 'Transformer is awesome.'
tokenized_string = tokenizer_en.encode(sample_string)
print ('Tokenized string is {}'.format(tokenized_string))
original_string = tokenizer_en.decode(tokenized_string)
print ('The original string: {}'.format(original_string))
assert original_string == sample_string
Tokenized string is [7915, 1248, 7946, 7194, 13, 2799, 7877] The original string: Transformer is awesome.
The tokenizer encodes the string by breaking it into subwords if the word is not in its dictionary.
for ts in tokenized_string:
print ('{} ----> {}'.format(ts, tokenizer_en.decode([ts])))
7915 ----> T 1248 ----> ran 7946 ----> s 7194 ----> former 13 ----> is 2799 ----> awesome 7877 ----> .
BUFFER_SIZE = 20000
BATCH_SIZE = 64
Add a start and end token to the input and target.
def encode(lang1, lang2):
lang1 = [tokenizer_pt.vocab_size] + tokenizer_pt.encode(
lang1.numpy()) + [tokenizer_pt.vocab_size+1]
lang2 = [tokenizer_en.vocab_size] + tokenizer_en.encode(
lang2.numpy()) + [tokenizer_en.vocab_size+1]
return lang1, lang2
You want to use Dataset.map to apply this function to each element of the dataset. Dataset.map runs in graph mode.
- Graph tensors do not have a value.
- In graph mode you can only use TensorFlow Ops and functions.
So you can't .map this function directly: You need to wrap it in a tf.py_function. The tf.py_function will pass regular tensors (with a value and a .numpy() method to access it), to the wrapped python function.
def tf_encode(pt, en):
result_pt, result_en = tf.py_function(encode, [pt, en], [tf.int64, tf.int64])
result_pt.set_shape([None])
result_en.set_shape([None])
return result_pt, result_en
MAX_LENGTH = 40
def filter_max_length(x, y, max_length=MAX_LENGTH):
return tf.logical_and(tf.size(x) <= max_length,
tf.size(y) <= max_length)
train_preprocessed = (
train_examples
.map(tf_encode)
.filter(filter_max_length)
# cache the dataset to memory to get a speedup while reading from it.
.cache()
.shuffle(BUFFER_SIZE))
val_preprocessed = (
val_examples
.map(tf_encode)
.filter(filter_max_length))
Pad and batch examples together:
train_dataset = (train_preprocessed
.padded_batch(BATCH_SIZE, padded_shapes=([None], [None]))
.prefetch(tf.data.experimental.AUTOTUNE))
val_dataset = (val_preprocessed
.padded_batch(BATCH_SIZE, padded_shapes=([None], [None])))
train_dataset = (train_preprocessed
.padded_batch(BATCH_SIZE)
.prefetch(tf.data.experimental.AUTOTUNE))
val_dataset = (val_preprocessed
.padded_batch(BATCH_SIZE))
Get an batch from the validation set to test the code on later:
pt_batch, en_batch = next(iter(val_dataset))
pt_batch, en_batch
(<tf.Tensor: shape=(64, 38), dtype=int64, numpy=
array([[8214, 342, 3032, ..., 0, 0, 0],
[8214, 95, 198, ..., 0, 0, 0],
[8214, 4479, 7990, ..., 0, 0, 0],
...,
[8214, 584, 12, ..., 0, 0, 0],
[8214, 59, 1548, ..., 0, 0, 0],
[8214, 118, 34, ..., 0, 0, 0]])>,
<tf.Tensor: shape=(64, 40), dtype=int64, numpy=
array([[8087, 98, 25, ..., 0, 0, 0],
[8087, 12, 20, ..., 0, 0, 0],
[8087, 12, 5453, ..., 0, 0, 0],
...,
[8087, 18, 2059, ..., 0, 0, 0],
[8087, 16, 1436, ..., 0, 0, 0],
[8087, 15, 57, ..., 0, 0, 0]])>)
Positional encoding
Since this model doesn't contain any recurrence or convolution, positional encoding is added to give the model some information about the relative position of the words in the sentence.
The positional encoding vector is added to the embedding vector. Embeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. But the embeddings do not encode the relative position of words in a sentence. So after adding the positional encoding, words will be closer to each other based on the similarity of their meaning and their position in the sentence, in the d-dimensional space.
See the notebook on positional encoding to learn more about it. The formula for calculating the positional encoding is as follows:
$$\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model} })} $$
$$\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model} })} $$
def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))
return pos * angle_rates
def positional_encoding(position, d_model):
angle_rads = get_angles(np.arange(position)[:, np.newaxis],
np.arange(d_model)[np.newaxis, :],
d_model)
# apply sin to even indices in the array; 2i
angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
# apply cos to odd indices in the array; 2i+1
angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
pos_encoding = angle_rads[np.newaxis, ...]
return tf.cast(pos_encoding, dtype=tf.float32)
pos_encoding = positional_encoding(50, 512)
print (pos_encoding.shape)
plt.pcolormesh(pos_encoding[0], cmap='RdBu')
plt.xlabel('Depth')
plt.xlim((0, 512))
plt.ylabel('Position')
plt.colorbar()
plt.show()
(1, 50, 512)
Masking
Mask all the pad tokens in the batch of sequence. It ensures that the model does not treat padding as the input. The mask indicates where pad value 0 is present: it outputs a 1 at those locations, and a 0 otherwise.
def create_padding_mask(seq):
seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
# add extra dimensions to add the padding
# to the attention logits.
return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)
x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
create_padding_mask(x)
<tf.Tensor: shape=(3, 1, 1, 5), dtype=float32, numpy=
array([[[[0., 0., 1., 1., 0.]]],
[[[0., 0., 0., 1., 1.]]],
[[[1., 1., 1., 0., 0.]]]], dtype=float32)>
The look-ahead mask is used to mask the future tokens in a sequence. In other words, the mask indicates which entries should not be used.
This means that to predict the third word, only the first and second word will be used. Similarly to predict the fourth word, only the first, second and the third word will be used and so on.
def create_look_ahead_mask(size):
mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
return mask # (seq_len, seq_len)
x = tf.random.uniform((1, 3))
temp = create_look_ahead_mask(x.shape[1])
temp
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[0., 1., 1.],
[0., 0., 1.],
[0., 0., 0.]], dtype=float32)>
Scaled dot product attention
The attention function used by the transformer takes three inputs: Q (query), K (key), V (value). The equation used to calculate the attention weights is:
$$\Large{Attention(Q, K, V) = softmax_k(\frac{QK^T}{\sqrt{d_k} }) V} $$
The dot-product attention is scaled by a factor of square root of the depth. This is done because for large values of depth, the dot product grows large in magnitude pushing the softmax function where it has small gradients resulting in a very hard softmax.
For example, consider that Q and K have a mean of 0 and variance of 1. Their matrix multiplication will have a mean of 0 and variance of dk. Hence, square root of dk is used for scaling (and not any other number) because the matmul of Q and K should have a mean of 0 and variance of 1, and you get a gentler softmax.
The mask is multiplied with -1e9 (close to negative infinity). This is done because the mask is summed with the scaled matrix multiplication of Q and K and is applied immediately before a softmax. The goal is to zero out these cells, and large negative inputs to softmax are near zero in the output.
def scaled_dot_product_attention(q, k, v, mask):
"""Calculate the attention weights.
q, k, v must have matching leading dimensions.
k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.
The mask has different shapes depending on its type(padding or look ahead)
but it must be broadcastable for addition.
Args:
q: query shape == (..., seq_len_q, depth)
k: key shape == (..., seq_len_k, depth)
v: value shape == (..., seq_len_v, depth_v)
mask: Float tensor with shape broadcastable
to (..., seq_len_q, seq_len_k). Defaults to None.
Returns:
output, attention_weights
"""
matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k)
# scale matmul_qk
dk = tf.cast(tf.shape(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
# add the mask to the scaled tensor.
if mask is not None:
scaled_attention_logits += (mask * -1e9)
# softmax is normalized on the last axis (seq_len_k) so that the scores
# add up to 1.
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k)
output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v)
return output, attention_weights
As the softmax normalization is done on K, its values decide the amount of importance given to Q.
The output represents the multiplication of the attention weights and the V (value) vector. This ensures that the words you want to focus on are kept as-is and the irrelevant words are flushed out.
def print_out(q, k, v):
temp_out, temp_attn = scaled_dot_product_attention(
q, k, v, None)
print ('Attention weights are:')
print (temp_attn)
print ('Output is:')
print (temp_out)
np.set_printoptions(suppress=True)
temp_k = tf.constant([[10,0,0],
[0,10,0],
[0,0,10],
[0,0,10]], dtype=tf.float32) # (4, 3)
temp_v = tf.constant([[ 1,0],
[ 10,0],
[ 100,5],
[1000,6]], dtype=tf.float32) # (4, 2)
# This `query` aligns with the second `key`,
# so the second `value` is returned.
temp_q = tf.constant([[0, 10, 0]], dtype=tf.float32) # (1, 3)
print_out(temp_q, temp_k, temp_v)
Attention weights are: tf.Tensor([[0. 1. 0. 0.]], shape=(1, 4), dtype=float32) Output is: tf.Tensor([[10. 0.]], shape=(1, 2), dtype=float32)
# This query aligns with a repeated key (third and fourth),
# so all associated values get averaged.
temp_q = tf.constant([[0, 0, 10]], dtype=tf.float32) # (1, 3)
print_out(temp_q, temp_k, temp_v)
Attention weights are: tf.Tensor([[0. 0. 0.5 0.5]], shape=(1, 4), dtype=float32) Output is: tf.Tensor([[550. 5.5]], shape=(1, 2), dtype=float32)
# This query aligns equally with the first and second key,
# so their values get averaged.
temp_q = tf.constant([[10, 10, 0]], dtype=tf.float32) # (1, 3)
print_out(temp_q, temp_k, temp_v)
Attention weights are: tf.Tensor([[0.5 0.5 0. 0. ]], shape=(1, 4), dtype=float32) Output is: tf.Tensor([[5.5 0. ]], shape=(1, 2), dtype=float32)
Pass all the queries together.
temp_q = tf.constant([[0, 0, 10], [0, 10, 0], [10, 10, 0]], dtype=tf.float32) # (3, 3)
print_out(temp_q, temp_k, temp_v)
Attention weights are: tf.Tensor( [[0. 0. 0.5 0.5] [0. 1. 0. 0. ] [0.5 0.5 0. 0. ]], shape=(3, 4), dtype=float32) Output is: tf.Tensor( [[550. 5.5] [ 10. 0. ] [ 5.5 0. ]], shape=(3, 2), dtype=float32)
Multi-head attention
Multi-head attention consists of four parts:
- Linear layers and split into heads.
- Scaled dot-product attention.
- Concatenation of heads.
- Final linear layer.
Each multi-head attention block gets three inputs; Q (query), K (key), V (value). These are put through linear (Dense) layers and split up into multiple heads.
The scaled_dot_product_attention defined above is applied to each head (broadcasted for efficiency). An appropriate mask must be used in the attention step. The attention output for each head is then concatenated (using tf.transpose, and tf.reshape) and put through a final Dense layer.
Instead of one single attention head, Q, K, and V are split into multiple heads because it allows the model to jointly attend to information at different positions from different representational spaces. After the split each head has a reduced dimensionality, so the total computation cost is the same as a single head attention with full dimensionality.
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.wq = tf.keras.layers.Dense(d_model)
self.wk = tf.keras.layers.Dense(d_model)
self.wv = tf.keras.layers.Dense(d_model)
self.dense = tf.keras.layers.Dense(d_model)
def split_heads(self, x, batch_size):
"""Split the last dimension into (num_heads, depth).
Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)
"""
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, v, k, q, mask):
batch_size = tf.shape(q)[0]
q = self.wq(q) # (batch_size, seq_len, d_model)
k = self.wk(k) # (batch_size, seq_len, d_model)
v = self.wv(v) # (batch_size, seq_len, d_model)
q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)
k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)
v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)
# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)
# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
scaled_attention, attention_weights = scaled_dot_product_attention(
q, k, v, mask)
scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth)
concat_attention = tf.reshape(scaled_attention,
(batch_size, -1, self.d_model)) # (batch_size, seq_len_q, d_model)
output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)
return output, attention_weights
Create a MultiHeadAttention layer to try out. At each location in the sequence, y, the MultiHeadAttention runs all 8 attention heads across all other locations in the sequence, returning a new vector of the same length at each location.
temp_mha = MultiHeadAttention(d_model=512, num_heads=8)
y = tf.random.uniform((1, 60, 512)) # (batch_size, encoder_sequence, d_model)
out, attn = temp_mha(y, k=y, q=y, mask=None)
out.shape, attn.shape
(TensorShape([1, 60, 512]), TensorShape([1, 8, 60, 60]))
Point wise feed forward network
Point wise feed forward network consists of two fully-connected layers with a ReLU activation in between.
def point_wise_feed_forward_network(d_model, dff):
return tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'), # (batch_size, seq_len, dff)
tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model)
])
sample_ffn = point_wise_feed_forward_network(512, 2048)
sample_ffn(tf.random.uniform((64, 50, 512))).shape
TensorShape([64, 50, 512])
Encoder and decoder
The transformer model follows the same general pattern as a standard sequence to sequence with attention model.
- The input sentence is passed through
Nencoder layers that generates an output for each word/token in the sequence. - The decoder attends on the encoder's output and its own input (self-attention) to predict the next word.
Encoder layer
Each encoder layer consists of sublayers:
- Multi-head attention (with padding mask)
- Point wise feed forward networks.
Each of these sublayers has a residual connection around it followed by a layer normalization. Residual connections help in avoiding the vanishing gradient problem in deep networks.
The output of each sublayer is LayerNorm(x + Sublayer(x)). The normalization is done on the d_model (last) axis. There are N encoder layers in the transformer.
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask):
attn_output, _ = self.mha(x, x, x, mask) # (batch_size, input_seq_len, d_model)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output) # (batch_size, input_seq_len, d_model)
ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)
return out2
sample_encoder_layer = EncoderLayer(512, 8, 2048)
sample_encoder_layer_output = sample_encoder_layer(
tf.random.uniform((64, 43, 512)), False, None)
sample_encoder_layer_output.shape # (batch_size, input_seq_len, d_model)
TensorShape([64, 43, 512])
Decoder layer
Each decoder layer consists of sublayers:
- Masked multi-head attention (with look ahead mask and padding mask)
- Multi-head attention (with padding mask). V (value) and K (key) receive the encoder output as inputs. Q (query) receives the output from the masked multi-head attention sublayer.
- Point wise feed forward networks
Each of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is LayerNorm(x + Sublayer(x)). The normalization is done on the d_model (last) axis.
There are N decoder layers in the transformer.
As Q receives the output from decoder's first attention block, and K receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next word by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section.
class DecoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(DecoderLayer, self).__init__()
self.mha1 = MultiHeadAttention(d_model, num_heads)
self.mha2 = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
self.dropout3 = tf.keras.layers.Dropout(rate)
def call(self, x, enc_output, training,
look_ahead_mask, padding_mask):
# enc_output.shape == (batch_size, input_seq_len, d_model)
attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask) # (batch_size, target_seq_len, d_model)
attn1 = self.dropout1(attn1, training=training)
out1 = self.layernorm1(attn1 + x)
attn2, attn_weights_block2 = self.mha2(
enc_output, enc_output, out1, padding_mask) # (batch_size, target_seq_len, d_model)
attn2 = self.dropout2(attn2, training=training)
out2 = self.layernorm2(attn2 + out1) # (batch_size, target_seq_len, d_model)
ffn_output = self.ffn(out2) # (batch_size, target_seq_len, d_model)
ffn_output = self.dropout3(ffn_output, training=training)
out3 = self.layernorm3(ffn_output + out2) # (batch_size, target_seq_len, d_model)
return out3, attn_weights_block1, attn_weights_block2
sample_decoder_layer = DecoderLayer(512, 8, 2048)
sample_decoder_layer_output, _, _ = sample_decoder_layer(
tf.random.uniform((64, 50, 512)), sample_encoder_layer_output,
False, None, None)
sample_decoder_layer_output.shape # (batch_size, target_seq_len, d_model)
TensorShape([64, 50, 512])
Encoder
The Encoder consists of:
- Input Embedding
- Positional Encoding
- N encoder layers
The input is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder.
class Encoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding,
self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask):
seq_len = tf.shape(x)[1]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x = self.enc_layers[i](x, training, mask)
return x # (batch_size, input_seq_len, d_model)
sample_encoder = Encoder(num_layers=2, d_model=512, num_heads=8,
dff=2048, input_vocab_size=8500,
maximum_position_encoding=10000)
temp_input = tf.random.uniform((64, 62), dtype=tf.int64, minval=0, maxval=200)
sample_encoder_output = sample_encoder(temp_input, training=False, mask=None)
print (sample_encoder_output.shape) # (batch_size, input_seq_len, d_model)
(64, 62, 512)
Decoder
The Decoder consists of:
- Output Embedding
- Positional Encoding
- N decoder layers
The target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer.
class Decoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size,
maximum_position_encoding, rate=0.1):
super(Decoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)
self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, enc_output, training,
look_ahead_mask, padding_mask):
seq_len = tf.shape(x)[1]
attention_weights = {}
x = self.embedding(x) # (batch_size, target_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x, block1, block2 = self.dec_layers[i](x, enc_output, training,
look_ahead_mask, padding_mask)
attention_weights['decoder_layer{}_block1'.format(i+1)] = block1
attention_weights['decoder_layer{}_block2'.format(i+1)] = block2
# x.shape == (batch_size, target_seq_len, d_model)
return x, attention_weights
sample_decoder = Decoder(num_layers=2, d_model=512, num_heads=8,
dff=2048, target_vocab_size=8000,
maximum_position_encoding=5000)
temp_input = tf.random.uniform((64, 26), dtype=tf.int64, minval=0, maxval=200)
output, attn = sample_decoder(temp_input,
enc_output=sample_encoder_output,
training=False,
look_ahead_mask=None,
padding_mask=None)
output.shape, attn['decoder_layer2_block2'].shape
(TensorShape([64, 26, 512]), TensorShape([64, 8, 26, 62]))
Create the Transformer
Transformer consists of the encoder, decoder and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned.
class Transformer(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
target_vocab_size, pe_input, pe_target, rate=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(num_layers, d_model, num_heads, dff,
input_vocab_size, pe_input, rate)
self.decoder = Decoder(num_layers, d_model, num_heads, dff,
target_vocab_size, pe_target, rate)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def call(self, inp, tar, training, enc_padding_mask,
look_ahead_mask, dec_padding_mask):
enc_output = self.encoder(inp, training, enc_padding_mask) # (batch_size, inp_seq_len, d_model)
# dec_output.shape == (batch_size, tar_seq_len, d_model)
dec_output, attention_weights = self.decoder(
tar, enc_output, training, look_ahead_mask, dec_padding_mask)
final_output = self.final_layer(dec_output) # (batch_size, tar_seq_len, target_vocab_size)
return final_output, attention_weights
sample_transformer = Transformer(
num_layers=2, d_model=512, num_heads=8, dff=2048,
input_vocab_size=8500, target_vocab_size=8000,
pe_input=10000, pe_target=6000)
temp_input = tf.random.uniform((64, 38), dtype=tf.int64, minval=0, maxval=200)
temp_target = tf.random.uniform((64, 36), dtype=tf.int64, minval=0, maxval=200)
fn_out, _ = sample_transformer(temp_input, temp_target, training=False,
enc_padding_mask=None,
look_ahead_mask=None,
dec_padding_mask=None)
fn_out.shape # (batch_size, tar_seq_len, target_vocab_size)
TensorShape([64, 36, 8000])
Set hyperparameters
To keep this example small and relatively fast, the values for num_layers, d_model, and dff have been reduced.
The values used in the base model of transformer were; num_layers=6, d_model = 512, dff = 2048. See the paper for all the other versions of the transformer.
num_layers = 4
d_model = 128
dff = 512
num_heads = 8
input_vocab_size = tokenizer_pt.vocab_size + 2
target_vocab_size = tokenizer_en.vocab_size + 2
dropout_rate = 0.1
Optimizer
Use the Adam optimizer with a custom learning rate scheduler according to the formula in the paper.
$$\Large{lrate = d_{model}^{-0.5} * min(step{\_}num^{-0.5}, step{\_}num * warmup{\_}steps^{-1.5})}$$
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, d_model, warmup_steps=4000):
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def __call__(self, step):
arg1 = tf.math.rsqrt(step)
arg2 = step * (self.warmup_steps ** -1.5)
return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
learning_rate = CustomSchedule(d_model)
optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98,
epsilon=1e-9)
temp_learning_rate_schedule = CustomSchedule(d_model)
plt.plot(temp_learning_rate_schedule(tf.range(40000, dtype=tf.float32)))
plt.ylabel("Learning Rate")
plt.xlabel("Train Step")
Text(0.5, 0, 'Train Step')
Loss and metrics
Since the target sequences are padded, it is important to apply a padding mask when calculating the loss.
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')
def loss_function(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_sum(loss_)/tf.reduce_sum(mask)
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='train_accuracy')
Training and checkpointing
transformer = Transformer(num_layers, d_model, num_heads, dff,
input_vocab_size, target_vocab_size,
pe_input=input_vocab_size,
pe_target=target_vocab_size,
rate=dropout_rate)
def create_masks(inp, tar):
# Encoder padding mask
enc_padding_mask = create_padding_mask(inp)
# Used in the 2nd attention block in the decoder.
# This padding mask is used to mask the encoder outputs.
dec_padding_mask = create_padding_mask(inp)
# Used in the 1st attention block in the decoder.
# It is used to pad and mask future tokens in the input received by
# the decoder.
look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])
dec_target_padding_mask = create_padding_mask(tar)
combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)
return enc_padding_mask, combined_mask, dec_padding_mask
Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every n epochs.
checkpoint_path = "./checkpoints/train"
ckpt = tf.train.Checkpoint(transformer=transformer,
optimizer=optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
# if a checkpoint exists, restore the latest checkpoint.
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint)
print ('Latest checkpoint restored!!')
The target is divided into tar_inp and tar_real. tar_inp is passed as an input to the decoder. tar_real is that same input shifted by 1: At each location in tar_input, tar_real contains the next token that should be predicted.
For example, sentence = "SOS A lion in the jungle is sleeping EOS"
tar_inp = "SOS A lion in the jungle is sleeping"
tar_real = "A lion in the jungle is sleeping EOS"
The transformer is an auto-regressive model: it makes predictions one part at a time, and uses its output so far to decide what to do next.
During training this example uses teacher-forcing (like in the text generation tutorial). Teacher forcing is passing the true output to the next time step regardless of what the model predicts at the current time step.
As the transformer predicts each word, self-attention allows it to look at the previous words in the input sequence to better predict the next word.
To prevent the model from peaking at the expected output the model uses a look-ahead mask.
EPOCHS = 20
# The @tf.function trace-compiles train_step into a TF graph for faster
# execution. The function specializes to the precise shape of the argument
# tensors. To avoid re-tracing due to the variable sequence lengths or variable
# batch sizes (the last batch is smaller), use input_signature to specify
# more generic shapes.
train_step_signature = [
tf.TensorSpec(shape=(None, None), dtype=tf.int64),
tf.TensorSpec(shape=(None, None), dtype=tf.int64),
]
@tf.function(input_signature=train_step_signature)
def train_step(inp, tar):
tar_inp = tar[:, :-1]
tar_real = tar[:, 1:]
enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)
with tf.GradientTape() as tape:
predictions, _ = transformer(inp, tar_inp,
True,
enc_padding_mask,
combined_mask,
dec_padding_mask)
loss = loss_function(tar_real, predictions)
gradients = tape.gradient(loss, transformer.trainable_variables)
optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))
train_loss(loss)
train_accuracy(tar_real, predictions)
Portuguese is used as the input language and English is the target language.
for epoch in range(EPOCHS):
start = time.time()
train_loss.reset_states()
train_accuracy.reset_states()
# inp -> portuguese, tar -> english
for (batch, (inp, tar)) in enumerate(train_dataset):
train_step(inp, tar)
if batch % 50 == 0:
print ('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(
epoch + 1, batch, train_loss.result(), train_accuracy.result()))
if (epoch + 1) % 5 == 0:
ckpt_save_path = ckpt_manager.save()
print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,
ckpt_save_path))
print ('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1,
train_loss.result(),
train_accuracy.result()))
print ('Time taken for 1 epoch: {} secs\n'.format(time.time() - start))
Epoch 1 Batch 0 Loss 8.9693 Accuracy 0.0000 Epoch 1 Batch 50 Loss 8.9249 Accuracy 0.0028 Epoch 1 Batch 100 Loss 8.8319 Accuracy 0.0135 Epoch 1 Batch 150 Loss 8.7294 Accuracy 0.0183 Epoch 1 Batch 200 Loss 8.6033 Accuracy 0.0214 Epoch 1 Batch 250 Loss 8.4510 Accuracy 0.0239 Epoch 1 Batch 300 Loss 8.2765 Accuracy 0.0265 Epoch 1 Batch 350 Loss 8.0914 Accuracy 0.0305 Epoch 1 Batch 400 Loss 7.9123 Accuracy 0.0344 Epoch 1 Batch 450 Loss 7.7490 Accuracy 0.0375 Epoch 1 Batch 500 Loss 7.6066 Accuracy 0.0402 Epoch 1 Batch 550 Loss 7.4756 Accuracy 0.0430 Epoch 1 Batch 600 Loss 7.3511 Accuracy 0.0461 Epoch 1 Batch 650 Loss 7.2317 Accuracy 0.0494 Epoch 1 Batch 700 Loss 7.1208 Accuracy 0.0526 Epoch 1 Loss 7.1159 Accuracy 0.0528 Time taken for 1 epoch: 57.4546058177948 secs Epoch 2 Batch 0 Loss 5.3954 Accuracy 0.0913 Epoch 2 Batch 50 Loss 5.4812 Accuracy 0.1044 Epoch 2 Batch 100 Loss 5.4329 Accuracy 0.1046 Epoch 2 Batch 150 Loss 5.3819 Accuracy 0.1066 Epoch 2 Batch 200 Loss 5.3329 Accuracy 0.1085 Epoch 2 Batch 250 Loss 5.2898 Accuracy 0.1105 Epoch 2 Batch 300 Loss 5.2498 Accuracy 0.1124 Epoch 2 Batch 350 Loss 5.2140 Accuracy 0.1139 Epoch 2 Batch 400 Loss 5.1831 Accuracy 0.1154 Epoch 2 Batch 450 Loss 5.1499 Accuracy 0.1169 Epoch 2 Batch 500 Loss 5.1168 Accuracy 0.1187 Epoch 2 Batch 550 Loss 5.0891 Accuracy 0.1200 Epoch 2 Batch 600 Loss 5.0645 Accuracy 0.1212 Epoch 2 Batch 650 Loss 5.0391 Accuracy 0.1225 Epoch 2 Batch 700 Loss 5.0148 Accuracy 0.1237 Epoch 2 Loss 5.0137 Accuracy 0.1237 Time taken for 1 epoch: 30.13740348815918 secs Epoch 3 Batch 0 Loss 4.7045 Accuracy 0.1427 Epoch 3 Batch 50 Loss 4.6139 Accuracy 0.1422 Epoch 3 Batch 100 Loss 4.5965 Accuracy 0.1431 Epoch 3 Batch 150 Loss 4.5926 Accuracy 0.1440 Epoch 3 Batch 200 Loss 4.5721 Accuracy 0.1443 Epoch 3 Batch 250 Loss 4.5569 Accuracy 0.1453 Epoch 3 Batch 300 Loss 4.5445 Accuracy 0.1459 Epoch 3 Batch 350 Loss 4.5343 Accuracy 0.1467 Epoch 3 Batch 400 Loss 4.5198 Accuracy 0.1475 Epoch 3 Batch 450 Loss 4.5028 Accuracy 0.1484 Epoch 3 Batch 500 Loss 4.4875 Accuracy 0.1492 Epoch 3 Batch 550 Loss 4.4728 Accuracy 0.1502 Epoch 3 Batch 600 Loss 4.4584 Accuracy 0.1511 Epoch 3 Batch 650 Loss 4.4431 Accuracy 0.1520 Epoch 3 Batch 700 Loss 4.4298 Accuracy 0.1527 Epoch 3 Loss 4.4287 Accuracy 0.1527 Time taken for 1 epoch: 30.38029909133911 secs Epoch 4 Batch 0 Loss 4.2693 Accuracy 0.1737 Epoch 4 Batch 50 Loss 4.0819 Accuracy 0.1685 Epoch 4 Batch 100 Loss 4.0721 Accuracy 0.1693 Epoch 4 Batch 150 Loss 4.0644 Accuracy 0.1702 Epoch 4 Batch 200 Loss 4.0486 Accuracy 0.1710 Epoch 4 Batch 250 Loss 4.0403 Accuracy 0.1720 Epoch 4 Batch 300 Loss 4.0232 Accuracy 0.1737 Epoch 4 Batch 350 Loss 4.0046 Accuracy 0.1747 Epoch 4 Batch 400 Loss 3.9903 Accuracy 0.1761 Epoch 4 Batch 450 Loss 3.9729 Accuracy 0.1772 Epoch 4 Batch 500 Loss 3.9584 Accuracy 0.1782 Epoch 4 Batch 550 Loss 3.9440 Accuracy 0.1793 Epoch 4 Batch 600 Loss 3.9295 Accuracy 0.1805 Epoch 4 Batch 650 Loss 3.9155 Accuracy 0.1816 Epoch 4 Batch 700 Loss 3.9041 Accuracy 0.1826 Epoch 4 Loss 3.9037 Accuracy 0.1826 Time taken for 1 epoch: 29.962489128112793 secs Epoch 5 Batch 0 Loss 3.3547 Accuracy 0.2107 Epoch 5 Batch 50 Loss 3.5440 Accuracy 0.2020 Epoch 5 Batch 100 Loss 3.5559 Accuracy 0.2000 Epoch 5 Batch 150 Loss 3.5478 Accuracy 0.2010 Epoch 5 Batch 200 Loss 3.5470 Accuracy 0.2014 Epoch 5 Batch 250 Loss 3.5351 Accuracy 0.2021 Epoch 5 Batch 300 Loss 3.5261 Accuracy 0.2024 Epoch 5 Batch 350 Loss 3.5140 Accuracy 0.2030 Epoch 5 Batch 400 Loss 3.5035 Accuracy 0.2036 Epoch 5 Batch 450 Loss 3.4968 Accuracy 0.2043 Epoch 5 Batch 500 Loss 3.4871 Accuracy 0.2048 Epoch 5 Batch 550 Loss 3.4813 Accuracy 0.2054 Epoch 5 Batch 600 Loss 3.4716 Accuracy 0.2063 Epoch 5 Batch 650 Loss 3.4618 Accuracy 0.2069 Epoch 5 Batch 700 Loss 3.4523 Accuracy 0.2078 Saving checkpoint for epoch 5 at ./checkpoints/train/ckpt-1 Epoch 5 Loss 3.4514 Accuracy 0.2078 Time taken for 1 epoch: 30.065120697021484 secs Epoch 6 Batch 0 Loss 3.0450 Accuracy 0.2554 Epoch 6 Batch 50 Loss 3.1264 Accuracy 0.2245 Epoch 6 Batch 100 Loss 3.1388 Accuracy 0.2226 Epoch 6 Batch 150 Loss 3.1364 Accuracy 0.2236 Epoch 6 Batch 200 Loss 3.1346 Accuracy 0.2231 Epoch 6 Batch 250 Loss 3.1321 Accuracy 0.2228 Epoch 6 Batch 300 Loss 3.1221 Accuracy 0.2232 Epoch 6 Batch 350 Loss 3.1148 Accuracy 0.2238 Epoch 6 Batch 400 Loss 3.1079 Accuracy 0.2243 Epoch 6 Batch 450 Loss 3.1043 Accuracy 0.2250 Epoch 6 Batch 500 Loss 3.0999 Accuracy 0.2255 Epoch 6 Batch 550 Loss 3.0947 Accuracy 0.2260 Epoch 6 Batch 600 Loss 3.0884 Accuracy 0.2263 Epoch 6 Batch 650 Loss 3.0850 Accuracy 0.2265 Epoch 6 Batch 700 Loss 3.0767 Accuracy 0.2271 Epoch 6 Loss 3.0763 Accuracy 0.2271 Time taken for 1 epoch: 29.689212799072266 secs Epoch 7 Batch 0 Loss 2.8275 Accuracy 0.2442 Epoch 7 Batch 50 Loss 2.7701 Accuracy 0.2413 Epoch 7 Batch 100 Loss 2.7581 Accuracy 0.2425 Epoch 7 Batch 150 Loss 2.7659 Accuracy 0.2435 Epoch 7 Batch 200 Loss 2.7535 Accuracy 0.2444 Epoch 7 Batch 250 Loss 2.7472 Accuracy 0.2445 Epoch 7 Batch 300 Loss 2.7424 Accuracy 0.2451 Epoch 7 Batch 350 Loss 2.7348 Accuracy 0.2458 Epoch 7 Batch 400 Loss 2.7293 Accuracy 0.2462 Epoch 7 Batch 450 Loss 2.7257 Accuracy 0.2465 Epoch 7 Batch 500 Loss 2.7190 Accuracy 0.2470 Epoch 7 Batch 550 Loss 2.7133 Accuracy 0.2471 Epoch 7 Batch 600 Loss 2.7068 Accuracy 0.2474 Epoch 7 Batch 650 Loss 2.7042 Accuracy 0.2477 Epoch 7 Batch 700 Loss 2.7003 Accuracy 0.2479 Epoch 7 Loss 2.7004 Accuracy 0.2479 Time taken for 1 epoch: 29.77017307281494 secs Epoch 8 Batch 0 Loss 2.3910 Accuracy 0.2765 Epoch 8 Batch 50 Loss 2.3681 Accuracy 0.2663 Epoch 8 Batch 100 Loss 2.3706 Accuracy 0.2654 Epoch 8 Batch 150 Loss 2.3742 Accuracy 0.2653 Epoch 8 Batch 200 Loss 2.3817 Accuracy 0.2638 Epoch 8 Batch 250 Loss 2.3814 Accuracy 0.2645 Epoch 8 Batch 300 Loss 2.3845 Accuracy 0.2649 Epoch 8 Batch 350 Loss 2.3838 Accuracy 0.2651 Epoch 8 Batch 400 Loss 2.3847 Accuracy 0.2647 Epoch 8 Batch 450 Loss 2.3858 Accuracy 0.2648 Epoch 8 Batch 500 Loss 2.3857 Accuracy 0.2650 Epoch 8 Batch 550 Loss 2.3863 Accuracy 0.2649 Epoch 8 Batch 600 Loss 2.3856 Accuracy 0.2649 Epoch 8 Batch 650 Loss 2.3842 Accuracy 0.2652 Epoch 8 Batch 700 Loss 2.3830 Accuracy 0.2654 Epoch 8 Loss 2.3833 Accuracy 0.2654 Time taken for 1 epoch: 29.767849445343018 secs Epoch 9 Batch 0 Loss 2.3601 Accuracy 0.2693 Epoch 9 Batch 50 Loss 2.1056 Accuracy 0.2813 Epoch 9 Batch 100 Loss 2.1193 Accuracy 0.2799 Epoch 9 Batch 150 Loss 2.1259 Accuracy 0.2803 Epoch 9 Batch 200 Loss 2.1312 Accuracy 0.2797 Epoch 9 Batch 250 Loss 2.1239 Accuracy 0.2804 Epoch 9 Batch 300 Loss 2.1267 Accuracy 0.2801 Epoch 9 Batch 350 Loss 2.1317 Accuracy 0.2792 Epoch 9 Batch 400 Loss 2.1332 Accuracy 0.2793 Epoch 9 Batch 450 Loss 2.1346 Accuracy 0.2794 Epoch 9 Batch 500 Loss 2.1391 Accuracy 0.2795 Epoch 9 Batch 550 Loss 2.1413 Accuracy 0.2797 Epoch 9 Batch 600 Loss 2.1439 Accuracy 0.2796 Epoch 9 Batch 650 Loss 2.1464 Accuracy 0.2795 Epoch 9 Batch 700 Loss 2.1481 Accuracy 0.2791 Epoch 9 Loss 2.1484 Accuracy 0.2791 Time taken for 1 epoch: 29.88327431678772 secs Epoch 10 Batch 0 Loss 1.9942 Accuracy 0.2998 Epoch 10 Batch 50 Loss 1.9290 Accuracy 0.2896 Epoch 10 Batch 100 Loss 1.9396 Accuracy 0.2896 Epoch 10 Batch 150 Loss 1.9400 Accuracy 0.2923 Epoch 10 Batch 200 Loss 1.9485 Accuracy 0.2911 Epoch 10 Batch 250 Loss 1.9486 Accuracy 0.2899 Epoch 10 Batch 300 Loss 1.9466 Accuracy 0.2901 Epoch 10 Batch 350 Loss 1.9515 Accuracy 0.2898 Epoch 10 Batch 400 Loss 1.9531 Accuracy 0.2902 Epoch 10 Batch 450 Loss 1.9566 Accuracy 0.2900 Epoch 10 Batch 500 Loss 1.9581 Accuracy 0.2901 Epoch 10 Batch 550 Loss 1.9619 Accuracy 0.2898 Epoch 10 Batch 600 Loss 1.9657 Accuracy 0.2896 Epoch 10 Batch 650 Loss 1.9681 Accuracy 0.2895 Epoch 10 Batch 700 Loss 1.9726 Accuracy 0.2898 Saving checkpoint for epoch 10 at ./checkpoints/train/ckpt-2 Epoch 10 Loss 1.9732 Accuracy 0.2898 Time taken for 1 epoch: 30.821648597717285 secs Epoch 11 Batch 0 Loss 1.8280 Accuracy 0.3086 Epoch 11 Batch 50 Loss 1.7646 Accuracy 0.3026 Epoch 11 Batch 100 Loss 1.7716 Accuracy 0.3015 Epoch 11 Batch 150 Loss 1.7799 Accuracy 0.3010 Epoch 11 Batch 200 Loss 1.7881 Accuracy 0.3016 Epoch 11 Batch 250 Loss 1.7906 Accuracy 0.3009 Epoch 11 Batch 300 Loss 1.7974 Accuracy 0.3009 Epoch 11 Batch 350 Loss 1.8025 Accuracy 0.3005 Epoch 11 Batch 400 Loss 1.8078 Accuracy 0.3003 Epoch 11 Batch 450 Loss 1.8130 Accuracy 0.2999 Epoch 11 Batch 500 Loss 1.8159 Accuracy 0.2999 Epoch 11 Batch 550 Loss 1.8196 Accuracy 0.2998 Epoch 11 Batch 600 Loss 1.8232 Accuracy 0.2994 Epoch 11 Batch 650 Loss 1.8265 Accuracy 0.2997 Epoch 11 Batch 700 Loss 1.8300 Accuracy 0.2995 Epoch 11 Loss 1.8297 Accuracy 0.2995 Time taken for 1 epoch: 29.80078434944153 secs Epoch 12 Batch 0 Loss 1.4630 Accuracy 0.3129 Epoch 12 Batch 50 Loss 1.6439 Accuracy 0.3125 Epoch 12 Batch 100 Loss 1.6535 Accuracy 0.3115 Epoch 12 Batch 150 Loss 1.6606 Accuracy 0.3103 Epoch 12 Batch 200 Loss 1.6643 Accuracy 0.3095 Epoch 12 Batch 250 Loss 1.6741 Accuracy 0.3091 Epoch 12 Batch 300 Loss 1.6781 Accuracy 0.3089 Epoch 12 Batch 350 Loss 1.6830 Accuracy 0.3085 Epoch 12 Batch 400 Loss 1.6865 Accuracy 0.3084 Epoch 12 Batch 450 Loss 1.6879 Accuracy 0.3079 Epoch 12 Batch 500 Loss 1.6922 Accuracy 0.3073 Epoch 12 Batch 550 Loss 1.6991 Accuracy 0.3071 Epoch 12 Batch 600 Loss 1.7045 Accuracy 0.3069 Epoch 12 Batch 650 Loss 1.7075 Accuracy 0.3067 Epoch 12 Batch 700 Loss 1.7104 Accuracy 0.3064 Epoch 12 Loss 1.7104 Accuracy 0.3064 Time taken for 1 epoch: 30.202271699905396 secs Epoch 13 Batch 0 Loss 1.5802 Accuracy 0.3404 Epoch 13 Batch 50 Loss 1.5486 Accuracy 0.3176 Epoch 13 Batch 100 Loss 1.5445 Accuracy 0.3168 Epoch 13 Batch 150 Loss 1.5535 Accuracy 0.3168 Epoch 13 Batch 200 Loss 1.5596 Accuracy 0.3158 Epoch 13 Batch 250 Loss 1.5612 Accuracy 0.3158 Epoch 13 Batch 300 Loss 1.5686 Accuracy 0.3154 Epoch 13 Batch 350 Loss 1.5747 Accuracy 0.3149 Epoch 13 Batch 400 Loss 1.5821 Accuracy 0.3149 Epoch 13 Batch 450 Loss 1.5849 Accuracy 0.3153 Epoch 13 Batch 500 Loss 1.5895 Accuracy 0.3144 Epoch 13 Batch 550 Loss 1.5970 Accuracy 0.3140 Epoch 13 Batch 600 Loss 1.6031 Accuracy 0.3138 Epoch 13 Batch 650 Loss 1.6076 Accuracy 0.3136 Epoch 13 Batch 700 Loss 1.6133 Accuracy 0.3127 Epoch 13 Loss 1.6138 Accuracy 0.3128 Time taken for 1 epoch: 29.971633195877075 secs Epoch 14 Batch 0 Loss 1.4708 Accuracy 0.3061 Epoch 14 Batch 50 Loss 1.4440 Accuracy 0.3232 Epoch 14 Batch 100 Loss 1.4477 Accuracy 0.3247 Epoch 14 Batch 150 Loss 1.4490 Accuracy 0.3254 Epoch 14 Batch 200 Loss 1.4644 Accuracy 0.3222 Epoch 14 Batch 250 Loss 1.4737 Accuracy 0.3227 Epoch 14 Batch 300 Loss 1.4850 Accuracy 0.3225 Epoch 14 Batch 350 Loss 1.4933 Accuracy 0.3219 Epoch 14 Batch 400 Loss 1.4958 Accuracy 0.3216 Epoch 14 Batch 450 Loss 1.5012 Accuracy 0.3201 Epoch 14 Batch 500 Loss 1.5072 Accuracy 0.3200 Epoch 14 Batch 550 Loss 1.5136 Accuracy 0.3199 Epoch 14 Batch 600 Loss 1.5188 Accuracy 0.3196 Epoch 14 Batch 650 Loss 1.5235 Accuracy 0.3193 Epoch 14 Batch 700 Loss 1.5274 Accuracy 0.3185 Epoch 14 Loss 1.5275 Accuracy 0.3185 Time taken for 1 epoch: 30.049386739730835 secs Epoch 15 Batch 0 Loss 1.3753 Accuracy 0.3058 Epoch 15 Batch 50 Loss 1.3753 Accuracy 0.3305 Epoch 15 Batch 100 Loss 1.3691 Accuracy 0.3284 Epoch 15 Batch 150 Loss 1.3798 Accuracy 0.3281 Epoch 15 Batch 200 Loss 1.3969 Accuracy 0.3282 Epoch 15 Batch 250 Loss 1.4021 Accuracy 0.3281 Epoch 15 Batch 300 Loss 1.4051 Accuracy 0.3274 Epoch 15 Batch 350 Loss 1.4131 Accuracy 0.3263 Epoch 15 Batch 400 Loss 1.4204 Accuracy 0.3259 Epoch 15 Batch 450 Loss 1.4277 Accuracy 0.3258 Epoch 15 Batch 500 Loss 1.4307 Accuracy 0.3253 Epoch 15 Batch 550 Loss 1.4355 Accuracy 0.3248 Epoch 15 Batch 600 Loss 1.4424 Accuracy 0.3243 Epoch 15 Batch 650 Loss 1.4467 Accuracy 0.3244 Epoch 15 Batch 700 Loss 1.4518 Accuracy 0.3241 Saving checkpoint for epoch 15 at ./checkpoints/train/ckpt-3 Epoch 15 Loss 1.4518 Accuracy 0.3241 Time taken for 1 epoch: 30.050633430480957 secs Epoch 16 Batch 0 Loss 1.4383 Accuracy 0.3318 Epoch 16 Batch 50 Loss 1.2965 Accuracy 0.3345 Epoch 16 Batch 100 Loss 1.3111 Accuracy 0.3348 Epoch 16 Batch 150 Loss 1.3184 Accuracy 0.3343 Epoch 16 Batch 200 Loss 1.3257 Accuracy 0.3323 Epoch 16 Batch 250 Loss 1.3313 Accuracy 0.3324 Epoch 16 Batch 300 Loss 1.3379 Accuracy 0.3321 Epoch 16 Batch 350 Loss 1.3406 Accuracy 0.3316 Epoch 16 Batch 400 Loss 1.3455 Accuracy 0.3313 Epoch 16 Batch 450 Loss 1.3527 Accuracy 0.3305 Epoch 16 Batch 500 Loss 1.3606 Accuracy 0.3305 Epoch 16 Batch 550 Loss 1.3688 Accuracy 0.3295 Epoch 16 Batch 600 Loss 1.3744 Accuracy 0.3292 Epoch 16 Batch 650 Loss 1.3810 Accuracy 0.3290 Epoch 16 Batch 700 Loss 1.3851 Accuracy 0.3287 Epoch 16 Loss 1.3858 Accuracy 0.3287 Time taken for 1 epoch: 30.129558563232422 secs Epoch 17 Batch 0 Loss 1.2039 Accuracy 0.3409 Epoch 17 Batch 50 Loss 1.2337 Accuracy 0.3383 Epoch 17 Batch 100 Loss 1.2467 Accuracy 0.3382 Epoch 17 Batch 150 Loss 1.2557 Accuracy 0.3355 Epoch 17 Batch 200 Loss 1.2631 Accuracy 0.3338 Epoch 17 Batch 250 Loss 1.2711 Accuracy 0.3346 Epoch 17 Batch 300 Loss 1.2822 Accuracy 0.3338 Epoch 17 Batch 350 Loss 1.2872 Accuracy 0.3333 Epoch 17 Batch 400 Loss 1.2939 Accuracy 0.3332 Epoch 17 Batch 450 Loss 1.3012 Accuracy 0.3324 Epoch 17 Batch 500 Loss 1.3060 Accuracy 0.3321 Epoch 17 Batch 550 Loss 1.3118 Accuracy 0.3323 Epoch 17 Batch 600 Loss 1.3164 Accuracy 0.3324 Epoch 17 Batch 650 Loss 1.3224 Accuracy 0.3320 Epoch 17 Batch 700 Loss 1.3263 Accuracy 0.3316 Epoch 17 Loss 1.3262 Accuracy 0.3316 Time taken for 1 epoch: 30.630080223083496 secs Epoch 18 Batch 0 Loss 1.3030 Accuracy 0.3364 Epoch 18 Batch 50 Loss 1.1891 Accuracy 0.3443 Epoch 18 Batch 100 Loss 1.1949 Accuracy 0.3437 Epoch 18 Batch 150 Loss 1.2023 Accuracy 0.3414 Epoch 18 Batch 200 Loss 1.2068 Accuracy 0.3419 Epoch 18 Batch 250 Loss 1.2145 Accuracy 0.3400 Epoch 18 Batch 300 Loss 1.2220 Accuracy 0.3392 Epoch 18 Batch 350 Loss 1.2283 Accuracy 0.3381 Epoch 18 Batch 400 Loss 1.2345 Accuracy 0.3379 Epoch 18 Batch 450 Loss 1.2409 Accuracy 0.3378 Epoch 18 Batch 500 Loss 1.2479 Accuracy 0.3378 Epoch 18 Batch 550 Loss 1.2544 Accuracy 0.3372 Epoch 18 Batch 600 Loss 1.2605 Accuracy 0.3369 Epoch 18 Batch 650 Loss 1.2677 Accuracy 0.3368 Epoch 18 Batch 700 Loss 1.2725 Accuracy 0.3363 Epoch 18 Loss 1.2727 Accuracy 0.3364 Time taken for 1 epoch: 30.03978443145752 secs Epoch 19 Batch 0 Loss 1.0818 Accuracy 0.3332 Epoch 19 Batch 50 Loss 1.1244 Accuracy 0.3521 Epoch 19 Batch 100 Loss 1.1323 Accuracy 0.3498 Epoch 19 Batch 150 Loss 1.1461 Accuracy 0.3464 Epoch 19 Batch 200 Loss 1.1553 Accuracy 0.3457 Epoch 19 Batch 250 Loss 1.1651 Accuracy 0.3442 Epoch 19 Batch 300 Loss 1.1766 Accuracy 0.3431 Epoch 19 Batch 350 Loss 1.1827 Accuracy 0.3422 Epoch 19 Batch 400 Loss 1.1876 Accuracy 0.3431 Epoch 19 Batch 450 Loss 1.1921 Accuracy 0.3428 Epoch 19 Batch 500 Loss 1.1985 Accuracy 0.3419 Epoch 19 Batch 550 Loss 1.2058 Accuracy 0.3416 Epoch 19 Batch 600 Loss 1.2107 Accuracy 0.3410 Epoch 19 Batch 650 Loss 1.2173 Accuracy 0.3405 Epoch 19 Batch 700 Loss 1.2240 Accuracy 0.3401 Epoch 19 Loss 1.2237 Accuracy 0.3401 Time taken for 1 epoch: 29.99256443977356 secs Epoch 20 Batch 0 Loss 1.0747 Accuracy 0.3333 Epoch 20 Batch 50 Loss 1.0935 Accuracy 0.3551 Epoch 20 Batch 100 Loss 1.1021 Accuracy 0.3504 Epoch 20 Batch 150 Loss 1.1090 Accuracy 0.3481 Epoch 20 Batch 200 Loss 1.1173 Accuracy 0.3479 Epoch 20 Batch 250 Loss 1.1262 Accuracy 0.3464 Epoch 20 Batch 300 Loss 1.1313 Accuracy 0.3463 Epoch 20 Batch 350 Loss 1.1373 Accuracy 0.3462 Epoch 20 Batch 400 Loss 1.1435 Accuracy 0.3454 Epoch 20 Batch 450 Loss 1.1487 Accuracy 0.3455 Epoch 20 Batch 500 Loss 1.1540 Accuracy 0.3455 Epoch 20 Batch 550 Loss 1.1610 Accuracy 0.3444 Epoch 20 Batch 600 Loss 1.1679 Accuracy 0.3440 Epoch 20 Batch 650 Loss 1.1732 Accuracy 0.3436 Epoch 20 Batch 700 Loss 1.1806 Accuracy 0.3432 Saving checkpoint for epoch 20 at ./checkpoints/train/ckpt-4 Epoch 20 Loss 1.1809 Accuracy 0.3432 Time taken for 1 epoch: 31.863187551498413 secs
Evaluate
The following steps are used for evaluation:
- Encode the input sentence using the Portuguese tokenizer (
tokenizer_pt). Moreover, add the start and end token so the input is equivalent to what the model is trained with. This is the encoder input. - The decoder input is the
start token == tokenizer_en.vocab_size. - Calculate the padding masks and the look ahead masks.
- The
decoderthen outputs the predictions by looking at theencoder outputand its own output (self-attention). - Select the last word and calculate the argmax of that.
- Concatentate the predicted word to the decoder input as pass it to the decoder.
- In this approach, the decoder predicts the next word based on the previous words it predicted.
def evaluate(inp_sentence):
start_token = [tokenizer_pt.vocab_size]
end_token = [tokenizer_pt.vocab_size + 1]
# inp sentence is portuguese, hence adding the start and end token
inp_sentence = start_token + tokenizer_pt.encode(inp_sentence) + end_token
encoder_input = tf.expand_dims(inp_sentence, 0)
# as the target is english, the first word to the transformer should be the
# english start token.
decoder_input = [tokenizer_en.vocab_size]
output = tf.expand_dims(decoder_input, 0)
for i in range(MAX_LENGTH):
enc_padding_mask, combined_mask, dec_padding_mask = create_masks(
encoder_input, output)
# predictions.shape == (batch_size, seq_len, vocab_size)
predictions, attention_weights = transformer(encoder_input,
output,
False,
enc_padding_mask,
combined_mask,
dec_padding_mask)
# select the last word from the seq_len dimension
predictions = predictions[: ,-1:, :] # (batch_size, 1, vocab_size)
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
# return the result if the predicted_id is equal to the end token
if predicted_id == tokenizer_en.vocab_size+1:
return tf.squeeze(output, axis=0), attention_weights
# concatentate the predicted_id to the output which is given to the decoder
# as its input.
output = tf.concat([output, predicted_id], axis=-1)
return tf.squeeze(output, axis=0), attention_weights
def plot_attention_weights(attention, sentence, result, layer):
fig = plt.figure(figsize=(16, 8))
sentence = tokenizer_pt.encode(sentence)
attention = tf.squeeze(attention[layer], axis=0)
for head in range(attention.shape[0]):
ax = fig.add_subplot(2, 4, head+1)
# plot the attention weights
ax.matshow(attention[head][:-1, :], cmap='viridis')
fontdict = {'fontsize': 10}
ax.set_xticks(range(len(sentence)+2))
ax.set_yticks(range(len(result)))
ax.set_ylim(len(result)-1.5, -0.5)
ax.set_xticklabels(
['<start>']+[tokenizer_pt.decode([i]) for i in sentence]+['<end>'],
fontdict=fontdict, rotation=90)
ax.set_yticklabels([tokenizer_en.decode([i]) for i in result
if i < tokenizer_en.vocab_size],
fontdict=fontdict)
ax.set_xlabel('Head {}'.format(head+1))
plt.tight_layout()
plt.show()
def translate(sentence, plot=''):
result, attention_weights = evaluate(sentence)
predicted_sentence = tokenizer_en.decode([i for i in result
if i < tokenizer_en.vocab_size])
print('Input: {}'.format(sentence))
print('Predicted translation: {}'.format(predicted_sentence))
if plot:
plot_attention_weights(attention_weights, sentence, result, plot)
translate("este é um problema que temos que resolver.")
print ("Real translation: this is a problem we have to solve .")
Input: este é um problema que temos que resolver. Predicted translation: this is a problem that we have to deal with addressing this . Real translation: this is a problem we have to solve .
translate("os meus vizinhos ouviram sobre esta ideia.")
print ("Real translation: and my neighboring homes heard about this idea .")
Input: os meus vizinhos ouviram sobre esta ideia. Predicted translation: my neighbors heard about this idea . Real translation: and my neighboring homes heard about this idea .
translate("vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram.")
print ("Real translation: so i 'll just share with you some stories very quickly of some magical things that have happened .")
Input: vou então muito rapidamente partilhar convosco algumas histórias de algumas coisas mágicas que aconteceram. Predicted translation: so i 'm going to share with you a few stories of a few magic things that happened to happen.com . Real translation: so i 'll just share with you some stories very quickly of some magical things that have happened .
You can pass different layers and attention blocks of the decoder to the plot parameter.
translate("este é o primeiro livro que eu fiz.", plot='decoder_layer4_block2')
print ("Real translation: this is the first book i've ever done.")
Input: este é o primeiro livro que eu fiz. Predicted translation: this is the first book that i did .
Real translation: this is the first book i've ever done.
Summary
In this tutorial, you learned about positional encoding, multi-head attention, the importance of masking and how to create a transformer.
Try using a different dataset to train the transformer. You can also create the base transformer or transformer XL by changing the hyperparameters above. You can also use the layers defined here to create BERT and train state of the art models. Futhermore, you can implement beam search to get better predictions.