举一个使用tensorflow中MultiHeadAttention包的例子
时间: 2023-05-15 07:05:42 浏览: 51
可以参考以下代码:
```python
import tensorflow as tf
# 定义输入
query = tf.random.normal([4, 10, 8]) # [batch_size, seq_len, d_model]
key = tf.random.normal([4, 12, 8]) # [batch_size, seq_len, d_model]
value = tf.random.normal([4, 12, 8]) # [batch_size, seq_len, d_model]
# 定义MultiHeadAttention层
multi_head_attn = tf.keras.layers.MultiHeadAttention(
num_heads=2, key_dim=4, dropout=0.2)
# 使用MultiHeadAttention层
output = multi_head_attn(query, key, value)
print(output.shape) # (4, 10, 8)
```
这里使用了`tf.keras.layers.MultiHeadAttention`类来定义MultiHeadAttention层,并将`query`、`key`、`value`作为输入传入该层。最后输出的`output`的形状为`(batch_size, seq_len, d_model)`。
相关问题
编写基于TensorFlow的transformer模型代码例子
当然,我很乐意回答你的问题!以下是基于TensorFlow的transformer模型代码例子:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Define vocab size
vocab_size = 5000
# Define input sequence length and output sequence length
max_len = 100
# Define number of attention heads and transformer layers
num_heads = 8
num_layers = 4
# Define embedding size and feedforward size
embedding_size = 256
ffn_size = 512
# Define input token with shape (batch_size, max_len)
input_token = layers.Input(shape=(max_len,))
# Define embedding layer with shape (batch_size, max_len, embedding_size)
embedding_layer = layers.Embedding(vocab_size, embedding_size)(input_token)
# Define position embedding layer with shape (batch_size, max_len, embedding_size)
position_embedding_layer = layers.Embedding(
input_dim=max_len, output_dim=embedding_size
)(tf.range(start=0, limit=max_len, delta=1))
# Combine embedding layer with position embedding layer
combined_embedding_layer = embedding_layer + position_embedding_layer
# Define transformer layers
for i in range(num_layers):
# Define multi-head attention layer with input shape (batch_size, seq_length, embedding_size)
attention_layer = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embedding_size
)
# Define normalization layers for multi-head attention layer output
norm_layer_1 = layers.LayerNormalization(
epsilon=1e-6
)
add_and_norm_layer_1 = layers.Add()([combined_embedding_layer, attention_layer(combined_embedding_layer)])
attention_output_layer = norm_layer_1(add_and_norm_layer_1)
# Define feedforward layer with input shape (batch_size, seq_length, ffn_size)
ffn_layer = keras.Sequential(
[
layers.Dense(ffn_size, activation="relu"),
layers.Dense(embedding_size),
]
)
# Define normalization layers for feedforward layer output
norm_layer_2 = layers.LayerNormalization(
epsilon=1e-6
)
add_and_norm_layer_2 = layers.Add()([attention_output_layer, ffn_layer(attention_output_layer)])
ffn_output_layer = norm_layer_2(add_and_norm_layer_2)
# If this is not the last layer, set the combined_embedding_layer to be the current ffn_output_layer
if i != num_layers-1:
combined_embedding_layer = ffn_output_layer
# Define output token with shape (batch_size, max_len, vocab_size)
output_token = layers.Dense(vocab_size)(ffn_output_layer)
# Define model with input layer of shape (batch_size, max_len) and output layer of shape (batch_size, max_len, vocab_size)
model = keras.models.Model(inputs=[input_token], outputs=[output_token])
```
希望对你有所帮助!
提高一个时间序列预测的transformer模型,keras
可以使用Keras实现一个Transformer模型来进行时间序列预测。下面是一个简单的实现示例,它使用了Keras的API来构建一个具有多头注意力机制的Transformer模型。
首先,我们需要导入必要的库和数据集:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
# 加载时间序列数据
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
# 调整数据形状
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
```
然后,我们可以使用Keras的API来创建Transformer模型:
```python
class TransformerEncoder(layers.Layer):
def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1):
super().__init__()
self.multi_head_attention = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
self.dropout1 = layers.Dropout(dropout)
self.layer_norm1 = layers.LayerNormalization(epsilon=1e-6)
self.ffn = keras.Sequential(
[
layers.Dense(ff_dim, activation="relu"),
layers.Dense(embed_dim),
]
)
self.dropout2 = layers.Dropout(dropout)
self.layer_norm2 = layers.LayerNormalization(epsilon=1e-6)
def call(self, inputs, training):
attn_output = self.multi_head_attention(inputs, inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layer_norm1(inputs + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layer_norm2(out1 + ffn_output)
class Transformer(keras.Model):
def __init__(self, num_layers, embed_dim, num_heads, ff_dim, input_shape, output_dim, dropout=0.1):
super().__init__()
self.input_layer = layers.InputLayer(input_shape=input_shape)
self.flatten = layers.Flatten()
self.encoder_layers = [TransformerEncoder(embed_dim, num_heads, ff_dim, dropout) for _ in range(num_layers)]
self.dense = layers.Dense(output_dim, activation="linear")
def call(self, inputs, training):
x = self.input_layer(inputs)
x = self.flatten(x)
for layer in self.encoder_layers:
x = layer(x, training)
x = self.dense(x)
return x
```
在这个实现中,我们定义了一个TransformerEncoder层,并使用它来堆叠多个Transformer层来构建整个模型。我们还添加了一个Dense层来进行最终的预测。
现在,我们可以使用这个模型来进行时间序列预测:
```python
num_layers = 4
embed_dim = 32
num_heads = 4
ff_dim = 64
input_shape = (28, 28, 1)
output_dim = 1
dropout = 0.1
model = Transformer(num_layers, embed_dim, num_heads, ff_dim, input_shape, output_dim, dropout)
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
history = model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_test, y_test))
```
在这个例子中,我们使用了MNIST数据集来进行时间序列预测。这个模型可以轻松地改变输入输出的形状来适应不同的时间序列预测任务。