# Keras自定义层要实现build方法、call方法和compute_output_shape(input_shape)方法 class GraphConvolution(Layer): """Basic graph convolution layer as in https://arxiv.org/abs/1609.02907""" # 构造函数 def __init__(self, units, support=1, activation=None, use_bias=True, kernel_initializer='gl...
class GCN(keras.Model): def __init__(self, input_dim, output_dim, num_features_nonzero, **kwargs): super(GCN, self).__init__(**kwargs) self.input_dim = input_dim # 1433 self.output_dim = output_dim print('input dim:', input_dim) print('output dim:', output_dim) print(...
Reshape from keras.models import Model from keras.regularizers import l2 import numpy as np from keras.optimizers import Adam from scipy.sparse import csr_matrix # 定义图卷积网络层 # 继承自TensorFlow Layer类 class GraphConvolution(Layer): def __init__(self, units, activation=tf.nn.relu, dropo...
from keras import activations, initializers, constraints from keras import regularizers from keras.engine import Layer import keras.backend as K # 定义基本的图卷积类 # Keras自定义层要实现build方法、call方法和compute_output_shape(input_shape)方法 class GraphConvolution(Layer): """Basic graph convoluti...
X = tf.cast(X, tf.float32)returnX# 定义图卷积层importtensorflowastffromtensorflow.kerasimportactivations, regularizers, constraints, initializersclassGCNConv(tf.keras.layers.Layer):#tf.keras.layers.Layer可用于创建神经网络中的层def__init__(self, ...
importtensorflowastffromtensorflow.kerasimportactivations,regularizers,constraints,initializersclassGCNConv(tf.keras.layers.Layer):def__init__(self,units,activation=lambdax:x,use_bias=True,kernel_initializer='glorot_uniform',bias_initializer='zeros',**kwargs):super(GCNConv,self).__init__()self.units...
y[mask] loss = loss_fn(logits, y) # + L2 regularization to the first layer only # for name, params in model.state_dict().items(): # if name.startswith("conv1"): # loss += 5e-4 * params.square().sum() / 2.0 acc = accuracy(preds, y) return loss.item(), acc 模型将整个...
classEncoder(tf.keras.layers.Layer): def__init__(self,embedding_dim=None,support=1,featureless=False,num_bases=-1): super(Encoder,self).__init__() self.embedding_dim=embedding_dim self.support=support# 邻接矩阵的个数 self.featureless=featureless# 是否不使用node本身的特征,值为【True】 ...
文本分类是自然语言处理过程中一个非常重要和经典的问题,在论文和实践过程中可以说经久不衰的任务。或多或少接触NLP的同学,应该比较清楚目前文本分类的模型众多,比如Text-RNN(LSTM),Text-CNN等,但是当时很少有关于将神经网络用于文本分类的任务中。 本文提出一种将图卷积网络模型用于文本分类的模型,主要思路为基于词语...
keras_layers存放一些常用的layer, conf存放项目数据、模型的地址, data存放数据和语料, data_preprocess为数据预处理模块, 模型与论文paper题与地址 参考/感谢 训练简单调用: fromkeras_textclassificationimporttraintrain(graph='TextCNN',# 必填, 算法名, 可选"ALBERT","BERT","XLNET","FASTTEXT","TEXTCNN","...