【从官方案例学框架Tensorflow/Keras】搭建Transformer模型解决文本分类问题
【从官方案例学框架Keras】搭建Transformer模型解决文本分类问题
Keras官方案例链接
Tensorflow官方案例链接
Paddle官方案例链接
Pytorch官方案例链接
注：本系列仅帮助大家快速理解、学习并能独立使用相关框架进行深度学习的研究，理论部分还请自行学习补充，每个框架的官方经典案例写的都非常好，很值得进行学习使用。可以说在完全理解官方经典案例后加以修改便可以解决大多数常见的相关任务。
摘要：搭建Transformer模型解决文本分类问题
目录
- 【从官方案例学框架Keras】搭建Transformer模型解决文本分类问题
- 1 Description
- 2 Setup
- 3 Implement multi head self attention as a Keras layer
- 4 Implement a Transformer block as a layer
- 5 Implement embedding layer
- 6 Download and prepare dataset
- 7 Create classifier model using transformer layer
- 8 Train and Evaluate
- 9 Summary
1 Description
简介：搭建Transformer作为Keras的一个层，并用它解决文本分类问题
2 Setup
导入所需包
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
3 Implement multi head self attention as a Keras layer
实现Multi-head self-attention作为Keras的一个层
在此，Fig1 给出Transformer整体结构图，Fig2 给出了一个示例图，对于Transformer忘记或者不熟悉的读者可以学习理论部分，在此给大家推荐一个笔者读过或听过的Transformer理论讲解最清楚的一节课
李宏毅老师的机器学习——Transformer模型
听完这节课，再配合食用代码更佳
- Fig1 Transformer整体结构图
- Fig2 Transformer示例图
在下面代码中值得注意的是：
MultiHeadSelfAttention：所谓多头attention就是多个self-attention，而self-attention= softmax(QK)V / np.sqrt(V)
tf.transpose( attention, perm=[0, 2, 1, 3] ):在下面代码中有诸多transpose操作，是为了之后的矩阵乘法形状匹配
class MultiHeadSelfAttention(layers.Layer):
def __init__(self, embed_dim, num_heads=8):
super(MultiHeadSelfAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
if embed_dim % num_heads != 0:
raise ValueError(
f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
)
self.projection_dim = embed_dim // num_heads
self.query_dense = layers.Dense(embed_dim)
self.key_dense = layers.Dense(embed_dim)
self.value_dense = layers.Dense(embed_dim)
self.combine_heads = layers.Dense(embed_dim)
def attention(self, query, key, value):
score = tf.matmul(query, key, transpose_b=True)
dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
scaled_score = score / tf.math.sqrt(dim_key)
weights = tf.nn.softmax(scaled_score, axis=-1)
output = tf.matmul(weights, value)
return output, weights
def separate_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs):
# x.shape = [batch_size, seq_len, embedding_dim]
batch_size = tf.shape(inputs)[0]
query = self.query_dense(inputs) # (batch_size, seq_len, embed_dim)
key = self.key_dense(inputs) # (batch_size, seq_len, embed_dim)
value = self.value_dense(inputs) # (batch_size, seq_len, embed_dim)
query = self.separate_heads(
query, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
key = self.separate_heads(
key, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
value = self.separate_heads(
value, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
attention, weights = self.attention(query, key, value)
attention = tf.transpose(
attention, perm=[0, 2, 1, 3]
) # (batch_size, seq_len, num_heads, projection_dim)
concat_attention = tf.reshape(
attention, (batch_size, -1, self.embed_dim)
) # (batch_size, seq_len, embed_dim)
output = self.combine_heads(
concat_attention
) # (batch_size, seq_len, embed_dim)
return output
4 Implement a Transformer block as a layer
TransformerBlock：实现了Fig1左侧部分，即Transformer的Encoder端，代码中值得注意的是
call中的laynorm，注意在Transformer中是没有用BatchNormalization的，而都是LayerNormalization，Batch是axis=0，即标准化一批数据的一部分，如位置0；Layer是axis=1，即标准化一批数据中的一个个数据；call中的inputs + attn_output和out1 + ffn_output，这便是Add残差连接
class TransformerBlock(layers.Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = MultiHeadSelfAttention(embed_dim, num_heads)
self.ffn = keras.Sequential(
[layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
)
self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = layers.Dropout(rate)
self.dropout2 = layers.Dropout(rate)
def call(self, inputs, training):
attn_output = self.att(inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(inputs + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(out1 + ffn_output)
5 Implement embedding layer
搭建两个独立的embedding层，一个用于tokens，另一个用于token index(positions)
TokenAndPositionEmbedding：这里与原始论文中的Transformer不同的是pos-embedding的不同，原始使用的sincos构造的，本例中直接使用索引
class TokenAndPositionEmbedding(layers.Layer):
def __init__(self, maxlen, vocab_size, embed_dim):
super(TokenAndPositionEmbedding, self).__init__()
self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)
def call(self, x):
maxlen = tf.shape(x)[-1]
positions = tf.range(start=0, limit=maxlen, delta=1)
positions = self.pos_emb(positions)
x = self.token_emb(x)
return x + positions
6 Download and prepare dataset
下载数据集
vocab_size = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review
(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(num_words=vocab_size)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)
7 Create classifier model using transformer layer
Transformer层对我们输入的序列每个timestamp都会有一个向量输出。此处我们取所有timestamp的平均值，将它喂给前向网络来进行文本分类
embed_dim = 32 # Embedding size for each token
num_heads = 2 # Number of attention heads
ff_dim = 32 # Hidden layer size in feed forward network inside transformer
inputs = layers.Input(shape=(maxlen,))
embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
x = embedding_layer(inputs)
transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)
x = transformer_block(x)
x = layers.GlobalAveragePooling1D()(x)
x = layers.Dropout(0.1)(x)
x = layers.Dense(20, activation="relu")(x)
x = layers.Dropout(0.1)(x)
outputs = layers.Dense(2, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.summary()
8 Train and Evaluate
训练和评估效果
model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
history = model.fit(
x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val)
)
9 Summary
Transformer模型解决文本分类问题完整代码如下
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
'''多头Attention'''
class MultiHeadSelfAttention(layers.Layer):
def __init__(self, embed_dim, num_heads=8):
super(MultiHeadSelfAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
if embed_dim % num_heads != 0:
raise ValueError(
f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
)
self.projection_dim = embed_dim // num_heads
self.query_dense = layers.Dense(embed_dim)
self.key_dense = layers.Dense(embed_dim)
self.value_dense = layers.Dense(embed_dim)
self.combine_heads = layers.Dense(embed_dim)
def attention(self, query, key, value):
score = tf.matmul(query, key, transpose_b=True)
dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
scaled_score = score / tf.math.sqrt(dim_key)
weights = tf.nn.softmax(scaled_score, axis=-1)
output = tf.matmul(weights, value)
return output, weights
def separate_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs):
# x.shape = [batch_size, seq_len, embedding_dim]
batch_size = tf.shape(inputs)[0]
query = self.query_dense(inputs) # (batch_size, seq_len, embed_dim)
key = self.key_dense(inputs) # (batch_size, seq_len, embed_dim)
value = self.value_dense(inputs) # (batch_size, seq_len, embed_dim)
query = self.separate_heads(
query, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
key = self.separate_heads(
key, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
value = self.separate_heads(
value, batch_size
) # (batch_size, num_heads, seq_len, projection_dim)
attention, weights = self.attention(query, key, value)
attention = tf.transpose(
attention, perm=[0, 2, 1, 3]
) # (batch_size, seq_len, num_heads, projection_dim)
concat_attention = tf.reshape(
attention, (batch_size, -1, self.embed_dim)
) # (batch_size, seq_len, embed_dim)
output = self.combine_heads(
concat_attention
) # (batch_size, seq_len, embed_dim)
return output
'''Transformer的Encoder部分'''
class TransformerBlock(layers.Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = MultiHeadSelfAttention(embed_dim, num_heads)
self.ffn = keras.Sequential(
[layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),]
)
self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = layers.Dropout(rate)
self.dropout2 = layers.Dropout(rate)
def call(self, inputs, training):
attn_output = self.att(inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(inputs + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(out1 + ffn_output)
'''Transformer输入的编码层'''
class TokenAndPositionEmbedding(layers.Layer):
def __init__(self, maxlen, vocab_size, embed_dim):
super(TokenAndPositionEmbedding, self).__init__()
self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)
def call(self, x):
maxlen = tf.shape(x)[-1]
positions = tf.range(start=0, limit=maxlen, delta=1)
positions = self.pos_emb(positions)
x = self.token_emb(x)
return x + positions
'''读取数据'''
vocab_size = 20000 # Only consider the top 20k words
maxlen = 200 # Only consider the first 200 words of each movie review
(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(num_words=vocab_size)
print(len(x_train), "Training sequences")
print(len(x_val), "Validation sequences")
x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)
'''搭建模型'''
embed_dim = 32 # Embedding size for each token
num_heads = 2 # Number of attention heads
ff_dim = 32 # Hidden layer size in feed forward network inside transformer
inputs = layers.Input(shape=(maxlen,))
embedding_layer = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
x = embedding_layer(inputs)
transformer_block = TransformerBlock(embed_dim, num_heads, ff_dim)
x = transformer_block(x)
x = layers.GlobalAveragePooling1D()(x)
x = layers.Dropout(0.1)(x)
x = layers.Dense(20, activation="relu")(x)
x = layers.Dropout(0.1)(x)
outputs = layers.Dense(2, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
'''模型训练'''
model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
history = model.fit(
x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val)
)