用python 写一个用TensorFlow 实现的trasfomer
时间: 2023-08-31 17:26:36 浏览: 76
### 回答1:
Transformer 是一种深度学习模型,用于解决序列数据处理问题,如自然语言处理。
下面是一个简单的 TensorFlow 实现 Transformer 的代码示例:
```
import tensorflow as tf
class Transformer(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Transformer, 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
# Encoder layer
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(
### 回答2:
Transformer 是一种用于自然语言处理 (NLP) 任务的模型,具有极强的表达能力和并行计算能力。下面是用 Python 编写的一个使用 TensorFlow 实现的 Transformer 的代码示例:
```python
import tensorflow as tf
# 定义 Transformer 模型类
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 = tf.keras.layers.Encoder(num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate)
self.decoder = tf.keras.layers.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, input, target, training, enc_padding_mask, look_ahead_mask, dec_padding_mask):
enc_output = self.encoder(input, training, enc_padding_mask)
dec_output, attention_weights = self.decoder(target, enc_output, training, look_ahead_mask, dec_padding_mask)
final_output = self.final_layer(dec_output)
return final_output, attention_weights
# 定义 Encoder 层
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.multi_head_attention = tf.keras.layers.MultiHeadAttention(num_heads, d_model)
self.ffn = tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model)
])
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layer_norm2 = 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.multi_head_attention(x, x, x, mask)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layer_norm1(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
# 定义 Decoder 层
class DecoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(DecoderLayer, self).__init__()
self.multi_head_attention1 = tf.keras.layers.MultiHeadAttention(num_heads, d_model)
self.multi_head_attention2 = tf.keras.layers.MultiHeadAttention(num_heads, d_model)
self.ffn = tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model)
])
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layer_norm3 = 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):
attn1, attn_weights_block1 = self.multi_head_attention1(x, x, x, look_ahead_mask)
attn1 = self.dropout1(attn1, training=training)
out1 = self.layer_norm1(attn1 + x)
attn2, attn_weights_block2 = self.multi_head_attention2(out1, enc_output, enc_output, padding_mask)
attn2 = self.dropout2(attn2, training=training)
out2 = self.layer_norm2(attn2 + out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout3(ffn_output, training=training)
out3 = self.layer_norm3(ffn_output + out2)
return out3, attn_weights_block1, attn_weights_block2
# 定义位置编码
def positional_encoding(position, d_model):
angle_rates = 1 / np.power(10000, (2* (i//2) / np.float32(d_model) for i in range(d_model)))
sinusoid_table = np.zeros((position, d_model))
for pos in range(position):
for i in range(d_model):
if i % 2 == 0:
sinusoid_table[pos, i] = np.sin(pos * angle_rates[i])
else:
sinusoid_table[pos, i] = np.cos(pos * angle_rates[i])
return tf.cast(sinusoid_table, dtype=tf.float32)
# 定义输入嵌入层
class Embed(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_model):
super(Embed, self).__init__()
self.embedding = tf.keras.layers.Embedding(vocab_size, d_model)
def call(self, x):
return self.embedding(x)
# 定义 Transformer 模型
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 = tf.keras.layers.Encoder(num_layers, d_model, num_heads, dff, input_vocab_size, pe_input, rate)
self.decoder = tf.keras.layers.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, input, target, training, enc_padding_mask, look_ahead_mask, dec_padding_mask):
enc_output = self.encoder(input, training, enc_padding_mask)
dec_output, attention_weights = self.decoder(target, enc_output, training, look_ahead_mask, dec_padding_mask)
final_output = self.final_layer(dec_output)
return final_output, attention_weights
```
这是一个简单的 Transformer 模型的实现,包含了 Encoder 层、Decoder 层、位置编码和输入嵌入层,同时定义了一个 Transformer 模型来整合这些层。在实际应用中,你可以根据具体的任务需求来改进和调整这个模型,例如添加更多的层、调整超参数等。