对一个图数组进行训练,使用VGAE,代码示例
时间: 2024-02-07 14:02:35 浏览: 28
好的,下面是一个使用VGAE进行图像数组训练的Python示例代码:
```python
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# 加载图像数组
graph = np.load('graph.npy')
# 构建模型
class GraphEncoder(layers.Layer):
def __init__(self, hidden_dim):
super(GraphEncoder, self).__init__()
self.hidden_dim = hidden_dim
self.dense1 = layers.Dense(hidden_dim, activation=tf.nn.relu)
self.dense2 = layers.Dense(hidden_dim//2, activation=tf.nn.relu)
def call(self, inputs):
x = self.dense1(inputs)
x = self.dense2(x)
return x
class VGAE(tf.keras.Model):
def __init__(self, hidden_dim, latent_dim):
super(VGAE, self).__init__()
self.hidden_dim = hidden_dim
self.latent_dim = latent_dim
self.encoder = GraphEncoder(hidden_dim)
self.z_mean = layers.Dense(latent_dim)
self.z_log_var = layers.Dense(latent_dim)
def encode(self, x):
h = self.encoder(x)
z_mean = self.z_mean(h)
z_log_var = self.z_log_var(h)
return z_mean, z_log_var
def reparameterize(self, z_mean, z_log_var):
eps = tf.random.normal(shape=z_mean.shape)
return z_mean + tf.exp(0.5 * z_log_var) * eps
def call(self, inputs):
z_mean, z_log_var = self.encode(inputs)
z = self.reparameterize(z_mean, z_log_var)
return z_mean, z_log_var, z
# 定义损失函数
def vgae_loss(adj, z_mean, z_log_var, z):
reconstruction_loss = tf.reduce_sum(tf.multiply(adj, -tf.math.log_sigmoid(tf.matmul(z, tf.transpose(z)))), axis=None)
kl_loss = -0.5 * tf.reduce_sum(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var), axis=None)
return reconstruction_loss + kl_loss
# 定义优化器
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
# 初始化模型
vgae = VGAE(hidden_dim=32, latent_dim=16)
# 训练模型
epochs = 100
for epoch in range(epochs):
with tf.GradientTape() as tape:
z_mean, z_log_var, z = vgae(graph)
loss = vgae_loss(graph, z_mean, z_log_var, z)
gradients = tape.gradient(loss, vgae.trainable_variables)
optimizer.apply_gradients(zip(gradients, vgae.trainable_variables))
print("Epoch:", epoch, "Loss:", loss.numpy())
# 保存模型
vgae.save_weights('vgae.h5')
```
在这个代码示例中,我们首先加载了图像数组,然后定义了一个VGAE模型。VGAE模型包括一个图编码器和一个使用重参数技巧的变分自编码器。我们还定义了一个损失函数,该损失函数包括重构损失和KL散度损失。最后,我们使用Adam优化器对模型进行训练,并保存了训练好的模型权重。