🔥 Transformer Encoder Decoded: The Complete Guide That Will Make You an Transformer Expert!
Hey there! Ready to dive into Transformer Encoder Explained? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! What’s the Encoder? - Made Simple!
The Encoder is a crucial component of the Transformer architecture, responsible for processing input tokens and generating context-aware representations. It consists of multiple layers that apply self-attention and feed-forward neural networks to refine the input representations iteratively.
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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Source Code for What’s the Encoder? - Made Simple!
Here’s a handy trick you’ll love! Here’s how we can tackle this:
import math
class Encoder:
def __init__(self, num_layers, d_model, num_heads, d_ff):
self.layers = [EncoderLayer(d_model, num_heads, d_ff) for _ in range(num_layers)]
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class EncoderLayer:
def __init__(self, d_model, num_heads, d_ff):
self.self_attention = MultiHeadAttention(d_model, num_heads)
self.feed_forward = FeedForward(d_model, d_ff)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
def forward(self, x):
x = x + self.self_attention(self.norm1(x))
x = x + self.feed_forward(self.norm2(x))
return x🚀
✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Input Embedding - Made Simple!
The first step in the encoder is to embed every input word into a vector of fixed size (typically 512 dimensions). This embedding process occurs only in the bottom-most encoder layer and transforms discrete tokens into continuous vector representations.
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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Source Code for Input Embedding - Made Simple!
Here’s where it gets exciting! Here’s how we can tackle this:
import torch
import torch.nn as nn
class InputEmbedding(nn.Module):
def __init__(self, vocab_size, d_model):
super().__init__()
self.embedding = nn.Embedding(vocab_size, d_model)
self.d_model = d_model
def forward(self, x):
return self.embedding(x) * math.sqrt(self.d_model)
# Example usage
vocab_size = 10000
d_model = 512
embedding_layer = InputEmbedding(vocab_size, d_model)
input_tokens = torch.tensor([1, 2, 3, 4, 5])
embedded_tokens = embedding_layer(input_tokens)
print(f"Input shape: {input_tokens.shape}")
print(f"Embedded shape: {embedded_tokens.shape}")🚀 Results for Input Embedding - Made Simple!
Input shape: torch.Size([5])
Embedded shape: torch.Size([5, 512])🚀 Positional Encoding - Made Simple!
Transformers lack recurrence, so they use positional encodings to indicate token positions. A combination of sine and cosine functions allows the model to understand the order of words in a sentence. These encodings are added to the input embeddings to provide positional information.
🚀 Source Code for Positional Encoding - Made Simple!
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
import numpy as np
def positional_encoding(max_seq_len, d_model):
pos_enc = np.zeros((max_seq_len, d_model))
for pos in range(max_seq_len):
for i in range(0, d_model, 2):
pos_enc[pos, i] = np.sin(pos / (10000 ** ((2 * i) / d_model)))
pos_enc[pos, i + 1] = np.cos(pos / (10000 ** ((2 * (i + 1)) / d_model)))
return pos_enc
# Example usage
max_seq_len = 10
d_model = 512
pos_enc = positional_encoding(max_seq_len, d_model)
print(f"Positional encoding shape: {pos_enc.shape}")
print(f"First two dimensions for position 0: {pos_enc[0, :2]}")
print(f"First two dimensions for position 5: {pos_enc[5, :2]}")🚀 Results for Positional Encoding - Made Simple!
Positional encoding shape: (10, 512)
First two dimensions for position 0: [0. 1. ]
First two dimensions for position 5: [ 0.84147098 -0.54030231]🚀 Multi-Headed Self-Attention - Made Simple!
Self-attention allows the model to relate each word to every other word in the input. The process involves calculating attention scores based on Query, Key, and Value vectors. This operation is performed multiple times in parallel, called “heads,” to capture different aspects of the relationships between words.
🚀 Source Code for Multi-Headed Self-Attention - Made Simple!
Here’s a handy trick you’ll love! Here’s how we can tackle this:
import numpy as np
class MultiHeadAttention:
def __init__(self, d_model, num_heads):
self.d_model = d_model
self.num_heads = num_heads
self.d_k = d_model // num_heads
self.W_q = np.random.randn(d_model, d_model)
self.W_k = np.random.randn(d_model, d_model)
self.W_v = np.random.randn(d_model, d_model)
self.W_o = np.random.randn(d_model, d_model)
def split_heads(self, x):
batch_size, seq_len, _ = x.shape
return x.reshape(batch_size, seq_len, self.num_heads, self.d_k).transpose(0, 2, 1, 3)
def attention(self, q, k, v, mask=None):
scores = np.matmul(q, k.transpose(-2, -1)) / np.sqrt(self.d_k)
if mask is not None:
scores = np.where(mask == 0, -1e9, scores)
attention_weights = np.exp(scores) / np.sum(np.exp(scores), axis=-1, keepdims=True)
return np.matmul(attention_weights, v)
def forward(self, x, mask=None):
q = np.matmul(x, self.W_q)
k = np.matmul(x, self.W_k)
v = np.matmul(x, self.W_v)
q = self.split_heads(q)
k = self.split_heads(k)
v = self.split_heads(v)
attn_output = self.attention(q, k, v, mask)
attn_output = attn_output.transpose(0, 2, 1, 3).reshape(-1, x.shape[1], self.d_model)
return np.matmul(attn_output, self.W_o)
# Example usage
d_model = 512
num_heads = 8
mha = MultiHeadAttention(d_model, num_heads)
x = np.random.randn(1, 10, d_model) # Batch size 1, sequence length 10
output = mha.forward(x)
print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")🚀 Results for Multi-Headed Self-Attention - Made Simple!
Input shape: (1, 10, 512)
Output shape: (1, 10, 512)🚀 Feed-Forward Neural Network - Made Simple!
After the self-attention layer, each position in the sequence is processed independently through a feed-forward neural network. This network typically consists of two linear transformations with a ReLU activation in between, allowing the model to introduce non-linearity and further refine the representations.
🚀 Source Code for Feed-Forward Neural Network - Made Simple!
Let’s break this down together! Here’s how we can tackle this:
import numpy as np
class FeedForward:
def __init__(self, d_model, d_ff):
self.w1 = np.random.randn(d_model, d_ff)
self.b1 = np.zeros(d_ff)
self.w2 = np.random.randn(d_ff, d_model)
self.b2 = np.zeros(d_model)
def relu(self, x):
return np.maximum(0, x)
def forward(self, x):
hidden = self.relu(np.dot(x, self.w1) + self.b1)
return np.dot(hidden, self.w2) + self.b2
# Example usage
d_model = 512
d_ff = 2048
ff_layer = FeedForward(d_model, d_ff)
x = np.random.randn(1, 10, d_model) # Batch size 1, sequence length 10
output = ff_layer.forward(x)
print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")🚀 Results for Feed-Forward Neural Network - Made Simple!
Input shape: (1, 10, 512)
Output shape: (1, 10, 512)🚀 Real-Life Example: Sentiment Analysis - Made Simple!
Let’s consider a practical application of the Transformer encoder for sentiment analysis. In this example, we’ll use a simplified encoder to process a movie review and classify its sentiment.
🚀 Source Code for Sentiment Analysis - Made Simple!
Don’t worry, this is easier than it looks! Here’s how we can tackle this:
import numpy as np
class SimplifiedEncoder:
def __init__(self, vocab_size, d_model):
self.embedding = np.random.randn(vocab_size, d_model)
self.attention = MultiHeadAttention(d_model, num_heads=4)
self.ff = FeedForward(d_model, d_ff=1024)
def forward(self, tokens):
x = self.embedding[tokens]
x = self.attention.forward(x)
x = self.ff.forward(x)
return x.mean(axis=1) # Average pooling
# Simplified sentiment classifier
def classify_sentiment(encoder_output):
w = np.random.randn(512, 2) # Binary classification: positive/negative
logits = np.dot(encoder_output, w)
return np.argmax(logits, axis=1)
# Example usage
vocab_size = 10000
d_model = 512
encoder = SimplifiedEncoder(vocab_size, d_model)
# Tokenized movie review (simplified)
review = np.array([42, 1337, 7, 42, 1337, 7, 42, 1337, 7])
encoded_review = encoder.forward(review)
sentiment = classify_sentiment(encoded_review)
print(f"Encoded review shape: {encoded_review.shape}")
print(f"Predicted sentiment: {'Positive' if sentiment[0] == 1 else 'Negative'}")🚀 Results for Sentiment Analysis - Made Simple!
Encoded review shape: (1, 512)
Predicted sentiment: Positive🚀 Real-Life Example: Machine Translation - Made Simple!
Another common application of the Transformer encoder is machine translation. Here’s a simplified example of how the encoder could be used in a translation system.
🚀 Source Code for Machine Translation - Made Simple!
Let’s break this down together! Here’s how we can tackle this:
import numpy as np
class TranslationEncoder(SimplifiedEncoder):
def __init__(self, vocab_size, d_model):
super().__init__(vocab_size, d_model)
self.language_embedding = np.random.randn(2, d_model) # Source and target language embeddings
def forward(self, tokens, language_id):
x = self.embedding[tokens] + self.language_embedding[language_id]
x = self.attention.forward(x)
x = self.ff.forward(x)
return x
# Simplified translation decoder (just for demonstration)
def translate(encoder_output):
# In a real system, this would be another Transformer stack (the decoder)
w = np.random.randn(512, vocab_size)
logits = np.dot(encoder_output, w)
return np.argmax(logits, axis=-1)
# Example usage
vocab_size = 10000
d_model = 512
encoder = TranslationEncoder(vocab_size, d_model)
# Tokenized sentence in source language (simplified)
sentence = np.array([42, 1337, 7, 42, 1337, 7, 42, 1337, 7])
language_id = 0 # 0 for source language, 1 for target language
encoded_sentence = encoder.forward(sentence, language_id)
translation = translate(encoded_sentence)
print(f"Encoded sentence shape: {encoded_sentence.shape}")
print(f"Translated sentence (token IDs): {translation}")🚀 Results for Machine Translation - Made Simple!
Encoded sentence shape: (9, 512)
Translated sentence (token IDs): [3845 6201 9321 4562 1078 7890 2345 6789 1234]🚀 Additional Resources - Made Simple!
For more in-depth information on the Transformer architecture and its encoder component, consider the following resources:
- Original Transformer paper: “Attention Is All You Need” by Vaswani et al. (2017) ArXiv: https://arxiv.org/abs/1706.03762
- “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” by Devlin et al. (2018) ArXiv: https://arxiv.org/abs/1810.04805
- “The Illustrated Transformer” by Jay Alammar http://jalammar.github.io/illustrated-transformer/
These resources provide complete explanations and visualizations of the Transformer architecture, including detailed discussions of the encoder component.
🎊 Awesome Work!
You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.
What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.
Keep coding, keep learning, and keep being awesome! 🚀