keras.models 实现transformer回归模型
时间: 2023-10-04 11:06:49 浏览: 207
使用Keras实现简单线性回归模型操作
在Keras中实现Transformer回归模型的步骤如下:
1. 导入所需的库和模块:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
```
2. 定义Transformer的编码器层和解码器层:
```python
class TransformerEncoder(layers.Layer):
def __init__(self, num_heads, d_model, dff, rate=0.1):
super(TransformerEncoder, self).__init__()
self.multihead_attention = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
self.ffn = keras.Sequential([layers.Dense(dff, activation="relu"), layers.Dense(d_model)])
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=False):
attention_output = self.multihead_attention(inputs, inputs)
attention_output = self.dropout1(attention_output, training=training)
attention_output = self.layernorm1(inputs + attention_output)
ffn_output = self.ffn(attention_output)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(attention_output + ffn_output)
class TransformerDecoder(layers.Layer):
def __init__(self, num_heads, d_model, dff, rate=0.1):
super(TransformerDecoder, self).__init__()
self.multihead_attention1 = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
self.multihead_attention2 = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
self.ffn = keras.Sequential([layers.Dense(dff, activation="relu"), layers.Dense(d_model)])
self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
self.layernorm3 = layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = layers.Dropout(rate)
self.dropout2 = layers.Dropout(rate)
self.dropout3 = layers.Dropout(rate)
def call(self, inputs, encoder_output, training=False):
attention1 = self.multihead_attention1(inputs, inputs)
attention1 = self.dropout1(attention1, training=training)
attention1 = self.layernorm1(inputs + attention1)
attention2 = self.multihead_attention2(attention1, encoder_output)
attention2 = self.dropout2(attention2, training=training)
attention2 = self.layernorm2(attention1 + attention2)
ffn_output = self.ffn(attention2)
ffn_output = self.dropout3(ffn_output, training=training)
return self.layernorm3(attention2 + ffn_output)
```
3. 定义Transformer模型:
```python
class TransformerModel(tf.keras.Model):
def __init__(self, num_layers, num_heads, d_model, dff, target_vocab_size, pe_input, pe_target, rate=0.1):
super(TransformerModel, self).__init__()
self.d_model = d_model
self.embedding = layers.Embedding(target_vocab_size, d_model)
self.pos_encoding = position_encoding(pe_input, self.d_model)
self.transformer_encoders = [TransformerEncoder(num_heads, d_model, dff, rate) for _ in range(num_layers)]
self.transformer_decoders = [TransformerDecoder(num_heads, d_model, dff, rate) for _ in range(num_layers)]
self.dense = layers.Dense(target_vocab_size)
def call(self, inputs, targets, training=False):
inputs = self.embedding(inputs)
inputs *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
inputs += self.pos_encoding[:, :tf.shape(inputs)[1], :]
for encoder in self.transformer_encoders:
inputs = encoder(inputs, training=training)
targets = self.embedding(targets)
targets *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
targets += self.pos_encoding[:, :tf.shape(targets)[1], :]
for decoder in self.transformer_decoders:
targets = decoder(targets, inputs, training=training)
outputs = self.dense(targets)
return outputs
```
4. 构建和编译模型:
```python
num_layers = 4
num_heads = 8
d_model = 128
dff = 512
target_vocab_size = 10000
dropout_rate = 0.1
model = TransformerModel(num_layers, num_heads, d_model, dff, target_vocab_size, pe_input, pe_target, dropout_rate)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.MeanSquaredError())
```
5. 训练模型:
```python
model.fit(train_dataset, validation_data=val_dataset, epochs=num_epochs)
```
6. 进行预测:
```python
predictions = model.predict(test_dataset)
```
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