gcn-lstm tensorflow
时间: 2023-12-19 19:07:16 浏览: 375
GCN-LSTM是一种结合了图卷积网络(GCN)和长短时记忆网络(LSTM)的模型,用于处理图数据的时间序列预测问题。下面是使用TensorFlow实现GCN-LSTM的基本步骤:
1.导入必要的库和模块:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LSTM, Dropout, GRU, Bidirectional, Conv1D, MaxPooling1D, Flatten, Reshape, Lambda, Concatenate, Multiply, Add
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras import backend as K
```
2.定义GCN层:
```python
class GraphConvolution(tf.keras.layers.Layer):
def __init__(self, output_dim, adj_matrix, **kwargs):
self.output_dim = output_dim
self.adj_matrix = adj_matrix
super(GraphConvolution, self).__init__(**kwargs)
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='glorot_uniform',
trainable=True)
super(GraphConvolution, self).build(input_shape)
def call(self, x):
adj_matrix = tf.cast(self.adj_matrix, dtype=tf.float32)
output = tf.matmul(adj_matrix, x)
output = tf.matmul(output, self.kernel)
return output
def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_dim)
```
3.定义GCN-LSTM模型:
```python
def gcn_lstm_model(adj_matrix, num_nodes, num_features, num_timesteps_input, num_timesteps_output, num_filters, kernel_size, lstm_units, dropout_rate):
# Input layer
input_layer = Input(shape=(num_timesteps_input, num_nodes, num_features))
# GCN layer
gcn_layer = GraphConvolution(num_filters, adj_matrix)(input_layer)
gcn_layer = Lambda(lambda x: K.permute_dimensions(x, (0, 2, 1, 3)))(gcn_layer)
gcn_layer = Reshape((num_nodes, num_timesteps_input * num_filters))(gcn_layer)
# LSTM layer
lstm_layer = LSTM(lstm_units, return_sequences=True)(gcn_layer)
lstm_layer = Dropout(dropout_rate)(lstm_layer)
# Output layer
output_layer = TimeDistributed(Dense(num_timesteps_output))(lstm_layer)
# Model definition
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='mse', optimizer=Adam(lr=0.001))
return model
```
4.训练模型:
```python
# Define model hyperparameters
num_nodes = 10
num_features = 1
num_timesteps_input = 24
num_timesteps_output = 12
num_filters = 32
kernel_size = 3
lstm_units = 64
dropout_rate = 0.2
batch_size = 32
epochs = 100
# Load data
x_train, y_train, x_val, y_val = load_data()
# Define adjacency matrix
adj_matrix = get_adjacency_matrix(num_nodes)
# Define model
model = gcn_lstm_model(adj_matrix, num_nodes, num_features, num_timesteps_input, num_timesteps_output, num_filters, kernel_size, lstm_units, dropout_rate)
# Train model
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
model_checkpoint = ModelCheckpoint('gcn_lstm.h5', save_best_only=True, save_weights_only=True)
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val), callbacks=[early_stopping, model_checkpoint])
```
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