HomeMachine LearningMachine Learning DIYTransformer Encoder implementation in TensorFlow and Keras

Transformer Encoder implementation in TensorFlow and Keras

Implementing the entire Transformer Encoder from scratch in TensorFlow and Keras is a complex task that involves multiple, layers, and attention mechanisms. However, I can guide you through the essential steps and provide a basic implementation of the Transformer Encoder. Keep in mind that this implementation is simplified and not suitable for production-grade applications.

Before starting, make sure you have TensorFlow and Keras installed:

pip install tensorflow

Let’s proceed with the implementation:

Step 1: Import the necessary libraries:

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LayerNormalization, Dropout

Step 2: Implement the Multi-Head Self-Attention Layer:

class MultiHeadSelfAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads):
super(MultiHeadSelfAttention, 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 = Dense(d_model)
self.WK = Dense(d_model)
self.WV = Dense(d_model)

self.dense = Dense(d_model)

def split_heads(self, x, batch_size):
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=None):
batch_size = tf.shape(Q)[0]

Q = self.WQ(Q)
K = self.WK(K)
V = self.WV(V)

Q = self.split_heads(Q, batch_size)
K = self.split_heads(K, batch_size)
V = self.split_heads(V, batch_size)

scaled_attention, attention_weights = self.scaled_dot_product_attention(Q, K, V, mask)

scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

output = self.dense(concat_attention)
return output, attention_weights

def scaled_dot_product_attention(self, Q, K, V, mask=None):
matmul_qk = tf.matmul(Q, K, transpose_b=True)
dk = tf.cast(tf.shape(K)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

if mask is not None:
scaled_attention_logits += (mask * -1e9)

attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
output = tf.matmul(attention_weights, V)

return output, attention_weights

Step 3: Implement the Position-wise Feed-Forward Network Layer:

class PositionwiseFeedforward(tf.keras.layers.Layer):
def __init__(self, d_model, dff, dropout_rate=0.1):
super(PositionwiseFeedforward, self).__init__()
self.ffn = tf.keras.Sequential([
Dense(dff, activation='relu'),
self.dropout = Dropout(dropout_rate)

def call(self, x):
x = self.ffn(x)
x = self.dropout(x)
return x

Step 4: Implement the Transformer Encoder Layer:

class TransformerEncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(TransformerEncoderLayer, self).__init__()
self.mha = MultiHeadSelfAttention(d_model, num_heads)
self.ffn = PositionwiseFeedforward(d_model, dff)

self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)

self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)

def call(self, x, training, mask=None):
attn_output, _ = self.mha(x, x, x, mask)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output)

ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output)

return out2

Step 5: Implement the complete Transformer Encoder:

class TransformerEncoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(TransformerEncoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers

self.embedding = Embedding(input_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding, self.d_model)

self.enc_layers = [TransformerEncoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]

self.dropout = Dropout(rate)

def call(self, x, training, mask=None):
seq_len = tf.shape(x)[1]
x = self.embedding(x)
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

def positional_encoding(self, position, d_model):
angle_rads = self.get_angles(np.arange(position)[:, np.newaxis],
np.arange(d_model)[np.newaxis, :],
# 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)

def get_angles(self, pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
return pos * angle_rates

Now you have implemented a basic version of the Transformer Encoder. You can use this layer in a larger model or experiment with different configurations and hyperparameters to build more sophisticated models for specific tasks like natural language processing or time series prediction with cryptocurrencies.

Keep in mind that the complete Transformer architecture also includes the Decoder and the attention mask to handle sequential data. Implementing the entire Transformer model from scratch involves additional complexity. If you’re interested in building a complete Transformer model, you can refer to research papers and more comprehensive tutorials on the topic.

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