写一个基于keras的gcnn预测代码
时间: 2024-01-23 08:03:33 浏览: 76
以下是一个基于Keras的GCNN预测代码示例,其中使用了Cora数据集:
```python
import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Flatten, Reshape, Embedding
from keras.layers.convolutional import Convolution1D
from keras.layers.pooling import MaxPooling1D
from keras.layers.merge import concatenate
from keras.optimizers import Adam
from keras.regularizers import l2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
def load_data():
# 加载Cora数据集,包括特征和标签
data = np.genfromtxt('cora.content', dtype=np.dtype(str))
features = np.array(data[:, 1:-1], dtype=np.float32)
labels = data[:, -1]
# 将标签编码为数字
label_encoder = LabelEncoder()
labels = label_encoder.fit_transform(labels)
# 对标签进行one-hot编码
labels = np_utils.to_categorical(labels)
# 加载邻接矩阵
adj_matrix = np.zeros((len(labels), len(labels)), dtype=np.int)
edges = np.genfromtxt('cora.cites', dtype=np.str)
for edge in edges:
idx1 = int(edge.split('\t')[0])
idx2 = int(edge.split('\t')[1])
adj_matrix[idx1][idx2] = 1
adj_matrix[idx2][idx1] = 1
return features, adj_matrix, labels
def build_model(feature_dim, output_dim, adj_matrix, num_filters=16, filter_size=3, hidden_dim=16, dropout_rate=0.5, l2_reg=0.01):
# 输入层
input_x = Input(shape=(feature_dim,))
# Embedding层
embedding = Embedding(input_dim=feature_dim, output_dim=num_filters, input_length=feature_dim)(input_x)
# GCNN层
conv = Convolution1D(filters=num_filters,
kernel_size=filter_size,
padding='same',
activation='relu',
kernel_regularizer=l2(l2_reg))(embedding)
conv = MaxPooling1D(pool_size=2)(conv)
# 将GCNN输出的特征矩阵转换为适合全连接层输入的形状
conv = Flatten()(conv)
# 全连接层
hidden = Dense(hidden_dim, activation='relu', kernel_regularizer=l2(l2_reg))(conv)
hidden = Dropout(dropout_rate)(hidden)
# 输出层
output = Dense(output_dim, activation='softmax')(hidden)
# 定义模型
model = Model(inputs=input_x, outputs=output)
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.01), metrics=['accuracy'])
return model
if __name__ == '__main__':
# 加载数据
features, adj_matrix, labels = load_data()
# 划分训练集和测试集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2, random_state=42)
# 构建模型
model = build_model(feature_dim=features.shape[1], output_dim=labels.shape[1], adj_matrix=adj_matrix)
# 训练模型
model.fit(train_features, train_labels, validation_data=(test_features, test_labels), epochs=10, batch_size=64)
# 评估模型
score = model.evaluate(test_features, test_labels, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
值得注意的是,这里使用了Cora数据集作为示例,如果想要使用其他数据集,需要根据实际情况进行相应的修改。
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