已知语料划分训练集和测试集,已知词向量预训练模型,比较三种深度学习模型完整代码
时间: 2023-08-07 13:35:23 浏览: 48
好的,以下是三种深度学习模型的完整代码,分别为:卷积神经网络(CNN)、循环神经网络(RNN)和长短时记忆网络(LSTM)。
CNN模型:
```python
import tensorflow as tf
# 定义模型
class CNNModel(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, num_filters, kernel_size, hidden_units, dropout_rate, num_classes):
super(CNNModel, self).__init__()
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.conv1d = tf.keras.layers.Conv1D(num_filters, kernel_size, activation='relu')
self.maxpool1d = tf.keras.layers.GlobalMaxPooling1D()
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.dense1 = tf.keras.layers.Dense(hidden_units, activation='relu')
self.dense2 = tf.keras.layers.Dense(num_classes, activation='softmax')
def call(self, inputs, training=False):
x = self.embedding(inputs)
x = self.conv1d(x)
x = self.maxpool1d(x)
x = self.dropout(x, training=training)
x = self.dense1(x)
outputs = self.dense2(x)
return outputs
```
RNN模型:
```python
import tensorflow as tf
# 定义模型
class RNNModel(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, hidden_units, dropout_rate, num_classes):
super(RNNModel, self).__init__()
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.lstm = tf.keras.layers.LSTM(hidden_units)
self.dense = tf.keras.layers.Dense(num_classes, activation='softmax')
def call(self, inputs, training=False):
x = self.embedding(inputs)
x = self.dropout(x, training=training)
x = self.lstm(x)
outputs = self.dense(x)
return outputs
```
LSTM模型:
```python
import tensorflow as tf
# 定义模型
class LSTMModel(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, hidden_units, dropout_rate, num_classes):
super(LSTMModel, self).__init__()
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.lstm = tf.keras.layers.LSTM(hidden_units, return_sequences=True)
self.maxpool1d = tf.keras.layers.GlobalMaxPooling1D()
self.dense = tf.keras.layers.Dense(num_classes, activation='softmax')
def call(self, inputs, training=False):
x = self.embedding(inputs)
x = self.dropout(x, training=training)
x = self.lstm(x)
x = self.maxpool1d(x)
outputs = self.dense(x)
return outputs
```
这里只是简单的模型代码,具体的训练和测试过程还需要根据具体问题进行实现。另外,这里的代码中使用了 TensorFlow 2.x 版本的 Keras 接口。