Spektral：使用TF2实现经典GNN的开源库

简介

Spektral工具还发表了论文： 《Graph Neural Networks in TensorFlow and Keras with Spektral》 https://arxiv.org/abs/2006.12138

github地址：https://github.com/danielegrattarola/spektral/

在本文中，我们介绍了 Spektral，这是一个开源 Python 库，用于使用 TensorFlow 和 Keras 应用程序编程接口构建图神经网络。Spektral 实现了大量的图深度学习方法，包括消息传递和池化运算符，以及用于处理图和加载流行基准数据集的实用程序。这个库的目的是为创建图神经网络提供基本的构建块，重点是 Keras 所基于的用户友好性和快速原型设计的指导原则。因此，Spektral 适合绝对的初学者和专业的深度学习从业者。

主要网络

Spektral 实现了一些主流的图深度学习层，包括：

• Graph Convolutional Networks (GCN)
• Chebyshev convolutions
• GraphSAGE
• ARMA convolutions
• Edge-Conditioned Convolutions (ECC)
• Graph attention networks (GAT)
• Approximated Personalized Propagation of Neural Predictions (APPNP)
• Graph Isomorphism Networks (GIN)
• Diffusional Convolutions 还有很多池化层，包括：
• MinCut pooling
• DiffPool
• Top-K pooling
• Self-Attention Graph (SAG) pooling
• Global pooling
• Global gated attention pooling
• SortPool

安装

pip安装：

代码语言：javascript复制

pip install spektral

源码安装:

代码语言：javascript复制

git clone https://github.com/danielegrattarola/spektral.git
cd spektral
python setup.py install  # Or 'pip install .'

Spektral实现GCN

对于TF爱好者很友好：

代码语言：javascript复制

import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.losses import CategoricalCrossentropy
from tensorflow.keras.optimizers import Adam

from spektral.data.loaders import SingleLoader
from spektral.datasets.citation import Citation
from spektral.layers import GCNConv
from spektral.models.gcn import GCN
from spektral.transforms import AdjToSpTensor, LayerPreprocess

learning_rate = 1e-2
seed = 0
epochs = 200
patience = 10
data = "cora"

tf.random.set_seed(seed=seed)  # make weight initialization reproducible

# Load data
dataset = Citation(
    data, normalize_x=True, transforms=[LayerPreprocess(GCNConv), AdjToSpTensor()]
)


# We convert the binary masks to sample weights so that we can compute the
# average loss over the nodes (following original implementation by
# Kipf & Welling)
def mask_to_weights(mask):
    return mask.astype(np.float32) / np.count_nonzero(mask)


weights_tr, weights_va, weights_te = (
    mask_to_weights(mask)
    for mask in (dataset.mask_tr, dataset.mask_va, dataset.mask_te)
)

model = GCN(n_labels=dataset.n_labels, n_input_channels=dataset.n_node_features)
model.compile(
    optimizer=Adam(learning_rate),
    loss=CategoricalCrossentropy(reduction="sum"),
    weighted_metrics=["acc"],
)

# Train model
loader_tr = SingleLoader(dataset, sample_weights=weights_tr)
loader_va = SingleLoader(dataset, sample_weights=weights_va)
model.fit(
    loader_tr.load(),
    steps_per_epoch=loader_tr.steps_per_epoch,
    validation_data=loader_va.load(),
    validation_steps=loader_va.steps_per_epoch,
    epochs=epochs,
    callbacks=[EarlyStopping(patience=patience, restore_best_weights=True)],
)

# Evaluate model
print("Evaluating model.")
loader_te = SingleLoader(dataset, sample_weights=weights_te)
eval_results = model.evaluate(loader_te.load(), steps=loader_te.steps_per_epoch)
print("Done.n" "Test loss: {}n" "Test accuracy: {}".format(*eval_results))

数据结构 深度学习 keras github tensorflow

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