写一个基于tensorflow的gcnn预测代码
时间: 2024-01-23 10:03:33 浏览: 27
以下是一个基于TensorFlow的GCNN预测代码的示例:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, GlobalMaxPooling1D, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.regularizers import l2
class GraphConvolution(tf.keras.layers.Layer):
def __init__(self, output_dim, activation=None, use_bias=True, kernel_regularizer=None, **kwargs):
self.output_dim = output_dim
self.activation = activation
self.use_bias = use_bias
self.kernel_regularizer = kernel_regularizer
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', regularizer=self.kernel_regularizer,
trainable=True)
if self.use_bias:
self.bias = self.add_weight(name='bias', shape=(self.output_dim,),
initializer='zeros', trainable=True)
super(GraphConvolution, self).build(input_shape)
def call(self, x, adj):
output = tf.matmul(adj, x)
output = tf.matmul(output, self.kernel)
if self.use_bias:
output += self.bias
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self, input_shape):
return input_shape[0], self.output_dim
def create_gcnn_model(num_features, num_classes, gcnn_output_dim=16, dropout_rate=0.5, l2_reg=5e-4):
x_in = Input(shape=(num_features,))
adj_in = Input(shape=(None,))
gcnn_1 = GraphConvolution(gcnn_output_dim, activation='relu', kernel_regularizer=l2(l2_reg))([x_in, adj_in])
gcnn_2 = GraphConvolution(gcnn_output_dim, activation='relu', kernel_regularizer=l2(l2_reg))([gcnn_1, adj_in])
pool = GlobalMaxPooling1D()(gcnn_2)
dropout = Dropout(dropout_rate)(pool)
x_out = Dense(num_classes, activation='softmax')(dropout)
model = Model(inputs=[x_in, adj_in], outputs=x_out)
optimizer = Adam(lr=0.01)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
return model
```
这个代码创建了一个基于GCN的模型,其中使用了两个GraphConvolution层,具有指定数量的输出维度和ReLU激活函数。模型还包括全局最大池化层和一个具有指定数量的输出类的Dense层。模型使用Adam优化器和分类交叉熵损失函数进行编译。
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