一、Transformer代码块
Transformer是用于自然语言处理的深度学习模型,在对文本进行处理时表现出了非常出色的性能。在代码实现中,Transformer被划分为一个encoder和一个decoder的过程。
在encoder中,Transformer接收了一串序列化的输入,并对其进行编码。该过程中,Transformer首先使用了一层叫做Self-Attention的注意力机制,对输入进行密集计算,计算出了每个位置可以关注到哪些位置的上下文信息。Self-Attention机制在代码实现中,被定义为函数MultiHeadAttention。
def MultiHeadAttention(h, dropout_rate):
def layer(x, mask):
# ...
return output
return layer
在接下来的编码过程中,使用了两层前馈神经网络,并对每个位置的特征进行加权求和,最终得到了一组编码向量,这些向量被用于在decoder中进行后续解码处理。
def FeedForward(units, dropout_rate):
def layer(x):
# ...
return output
return layer
在decoder中,Transformer接收了一个编码向量的序列和对应的输出序列。Transformer的解码过程中,同样使用了Self-Attention机制,但不同的是在注意力的计算上,除了对已知的输入序列进行考虑,还要考虑已经生成的部分输出序列,因为这些输出序列也会对后期的输出序列产生影响。在代码实现中,这一过程被定义为函数DecoderSelfAttention。
def DecoderSelfAttention(h, dropout_rate):
def layer(x, mask):
# ...
return output
return layer
在解码的过程中,同样使用了两层前馈神经网络,并与已知的输入序列进行交互,以生成对应的输出序列。
def DecoderFeedForward(units, dropout_rate):
def layer(x):
# ...
return output
return layer
二、Transformer代码级详解
在Transformer的代码实现中,除了以上主要的encoder和decoder过程之外,还有一些对模型的优化和调参。其中,Dropout技术被广泛用于防止过拟合。在代码中,Dropout被定义为一个与输入大小无关的比率值,该比率值表示在训练过程中每一层神经元的dropout率。
class Dropout(tf.keras.layers.Layer):
def __init__(self, rate):
super(Dropout, self).__init__()
self.rate = rate
def call(self, x, training=None):
if training:
x = tf.nn.dropout(x, rate=self.rate)
return x
除了Dropout技术,还可以使用Layer Normalization技术,对每一层输入进行规范化,使得每一层的输出更加稳定。在代码中,Layer Normalization的实现非常简单,只需要对当前层的输出进行规范化即可。
class LayerNormalization(tf.keras.layers.Layer):
def __init__(self, epsilon=1e-6):
super(LayerNormalization, self).__init__()
self.epsilon = epsilon
def build(self, input_shape):
# ...
def call(self, x):
# ...
return output
三、Transformer代码调试
在编写Transformer代码时,可能会出现各种各样的调试问题。其中最常见的问题是维度的不匹配,这通常发生在self-attention层或feedforward层的实现上。如果出现这种问题,可以通过打印特征向量维度的方式来排查。
def MultiHeadAttention(h, dropout_rate):
def layer(x, mask):
# ...
print(x_shape)
print(W_Q_shape)
# ...
return layer
除此之外,还有一些其他的调试技巧,比如可以限制模型的步长或者减少少量样本集以排除其他问题。
四、Transformer模型
在完成Transformer代码的编写之后,需要对模型进行训练和评估。可以通过调整epoch、batch size、learning rate等参数来进一步优化模型性能。在训练过程中,可以使用TensorBoard来可视化训练过程,并监视模型的各项指标。
model.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
model.fit(train_dataset, epochs=EPOCHS, validation_steps=VAL_STEPS,
validation_data=val_dataset, steps_per_epoch=STEPS_PER_EPOCH,
callbacks=[tensorboard_callback])
五、Transformer神经网络
在Transformer中,Self-Attention机制是实现其高效性的关键。可以通过增加头数来提高模型的效率。在同时满足可扩展性和效率的前提下,提高键值对的维度被证明是另一种有效提高模型性能的做法之一。但是,需要注意维度的限制,因为这会对Transformer所需的计算资源产生影响。
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, h, d_model, dropout_rate=0.1):
super(MultiHeadAttention, self).__init__()
self.h = h
self.d_model = d_model
# ...
def call(self, inputs):
# ...
return output
六、Transformer代码机器翻译
Transformer的最初目的之一是机器翻译任务。可以使用Transformer代码对任意一种语言进行机器翻译。在实现-machine translation时,常规方法包括使用BLEU评估方法对模型进行评估,或者在训练数据中添加一些噪声,以产生更多的训练样本。
trainer = tf.keras.Model([encoder_input, decoder_input], decoder_output)
trainer.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
trainer.fit(train_data, epochs=EPOCHS, batch_size=BATCH_SIZE, validation_data=val_data)
七、Transformer代码实现
具体实现请参考下面的代码示例。这个示例实现将输入序列翻转并输出,以演示Transformer模型在手写数字识别任务上的应用。
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
class PositionalEncoding(layers.Layer):
def __init__(self):
super(PositionalEncoding, self).__init__()
def call(self, inputs):
seq_len = inputs.shape[1]
d_model = inputs.shape[2]
pos = np.repeat(np.arange(seq_len)[:, np.newaxis], d_model, axis=-1)
angle_rates = 1 / np.power(10000, (2 * (pos//2)) / np.float32(d_model))
angle_rads = np.zeros_like(pos, dtype=np.float32)
angle_rads[:, 0::2] = np.sin(angle_rates[:, 0::2])
angle_rads[:, 1::2] = np.cos(angle_rates[:, 1::2])
pos_encoding = angle_rads[np.newaxis, ...]
return inputs + tf.cast(pos_encoding, dtype=tf.float32)
def get_config(self):
return super().get_config()
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 create_padding_mask(seq):
seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
return seq[:, tf.newaxis, tf.newaxis, :]
def create_look_ahead_mask(size):
mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
return mask
class MultiHeadAttention(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 = layers.Dense(d_model)
self.WK = layers.Dense(d_model)
self.WV = layers.Dense(d_model)
self.dense = layers.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, inputs):
q, k, v, mask = inputs['query'], inputs['key'], inputs['value'], inputs['mask']
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 = 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
def get_config(self):
return super().get_config()
def point_wise_feed_forward_network(d_model, dff):
return keras.Sequential([
layers.Dense(dff, activation='relu'),
layers.Dense(d_model)
])
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = tf.matmul(q, k, transpose_b=True)
depth = tf.cast(tf.shape(k)[-1], tf.float32)
logits = matmul_qk / tf.math.sqrt(depth)
if mask is not None:
logits = logits + (mask * -1e9)
attention_weights = tf.nn.softmax(logits, axis=-1)
output = tf.matmul(attention_weights, v)
return output, attention_weights
class EncoderLayer(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.layer_norm1 = layers.LayerNormalization(epsilon=1e-6)
self.layer_norm2 = layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = layers.Dropout(rate)
self.dropout2 = layers.Dropout(rate)
def call(self, inputs, training):
x, mask = inputs['x'], inputs['mask']
attn_output = self.mha({'query': x, 'key': x, 'value': x, 'mask': mask})
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layer_norm1(inputs['x'] + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layer_norm2(out1 + ffn_output)
return out2
def get_config(self):
return super().get_config()
class DecoderLayer(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.layer_norm1 = layers.LayerNormalization(epsilon=1e-6)
self.layer_norm2 = layers.LayerNormalization(epsilon=1e-6)
self.layer_norm3 = layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = layers.Dropout(rate)
self.dropout2 = layers.Dropout(rate)
self.dropout3 = layers.Dropout(rate)
def call(self, inputs, training):
x, enc_output, look_ahead_mask, padding_mask = inputs['x'], inputs['enc_output'], inputs['look_ahead_mask'], inputs['padding_mask']
attn1, attn_weights_block1 = self.mha1({'query': x, 'key': x, 'value': x, 'mask': look_ahead_mask})
attn1 = self.dropout1(attn1, training