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Visão geral
Gráfico regularização é uma técnica específica sob o paradigma mais amplo de Neural gráfico de aprendizagem ( Bui et al., 2018 ). A ideia central é treinar modelos de rede neural com um objetivo regularizado por gráfico, aproveitando dados rotulados e não rotulados.
Neste tutorial, exploraremos o uso de regularização de gráfico para classificar documentos que formam um gráfico natural (orgânico).
A receita geral para a criação de um modelo regularizado por gráfico usando o framework Neural Structured Learning (NSL) é a seguinte:
- Gere dados de treinamento a partir do gráfico de entrada e recursos de amostra. Os nós no gráfico correspondem a amostras e as arestas no gráfico correspondem à similaridade entre pares de amostras. Os dados de treinamento resultantes conterão recursos vizinhos, além dos recursos do nó original.
- Criar uma rede neural como um modelo básico usando o
Kerassequencial, funcional ou subclasse API. - Enrole o modelo base com o
GraphRegularizationclasse wrapper, que é fornecido pela estrutura NSL, para criar um novo gráficoKerasmodelo. Este novo modelo incluirá uma perda de regularização de gráfico como termo de regularização em seu objetivo de treinamento. - Treinar e avaliar o gráfico
Kerasmodelo.
Configurar
Instale o pacote Neural Structured Learning.
pip install --quiet neural-structured-learning
Dependências e importações
import neural_structured_learning as nsl
import tensorflow as tf
# Resets notebook state
tf.keras.backend.clear_session()
print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print(
"GPU is",
"available" if tf.config.list_physical_devices("GPU") else "NOT AVAILABLE")
Version: 2.8.0-rc0 Eager mode: True GPU is NOT AVAILABLE 2022-01-05 12:39:27.704660: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
Conjunto de dados Cora
O conjunto de dados Cora é um gráfico citação onde nós representam papéis aprendizado de máquina e arestas representam citações entre pares de papéis. A tarefa envolvida é a classificação de documentos, onde o objetivo é categorizar cada artigo em uma das 7 categorias. Em outras palavras, este é um problema de classificação multiclasse com 7 classes.
Gráfico
O gráfico original é direcionado. No entanto, para o propósito deste exemplo, consideramos a versão não direcionada deste gráfico. Portanto, se o artigo A cita o artigo B, também consideramos o artigo B como tendo citado A. Embora isso não seja necessariamente verdade, neste exemplo, consideramos as citações como um proxy de similaridade, que geralmente é uma propriedade comutativa.
Recursos
Cada artigo na entrada contém efetivamente 2 recursos:
Palavras: Uma densa, multi-quente representação bag-of-palavras do texto no papel. O vocabulário para o conjunto de dados Cora contém 1433 palavras exclusivas. Portanto, o comprimento desse recurso é 1433, e o valor na posição 'i' é 0/1, indicando se a palavra 'i' no vocabulário existe no artigo fornecido ou não.
Etiqueta: Um único número inteiro que representa a identificação de classe (categoria) do papel.
Baixe o conjunto de dados Cora
wget --quiet -P /tmp https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgztar -C /tmp -xvzf /tmp/cora.tgz
cora/ cora/README cora/cora.cites cora/cora.content
Converta os dados Cora para o formato NSL
A fim de pré-processar o conjunto de dados Cora e convertê-lo para o formato exigido pela aprendizagem estruturada Neural, vamos executar o script 'preprocess_cora_dataset.py', que está incluído no repositório de NSL github. Este script faz o seguinte:
- Gere recursos vizinhos usando os recursos do nó original e o gráfico.
- Gerar trem e dados de teste splits contendo
tf.train.Examplecasos. - Persistir os dados de teste de trem e resultando na
TFRecordformato.
!wget https://raw.githubusercontent.com/tensorflow/neural-structured-learning/master/neural_structured_learning/examples/preprocess/cora/preprocess_cora_dataset.py
!python preprocess_cora_dataset.py \
--input_cora_content=/tmp/cora/cora.content \
--input_cora_graph=/tmp/cora/cora.cites \
--max_nbrs=5 \
--output_train_data=/tmp/cora/train_merged_examples.tfr \
--output_test_data=/tmp/cora/test_examples.tfr
--2022-01-05 12:39:28-- https://raw.githubusercontent.com/tensorflow/neural-structured-learning/master/neural_structured_learning/examples/preprocess/cora/preprocess_cora_dataset.py Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 11640 (11K) [text/plain] Saving to: ‘preprocess_cora_dataset.py’ preprocess_cora_dat 100%[===================>] 11.37K --.-KB/s in 0s 2022-01-05 12:39:28 (78.9 MB/s) - ‘preprocess_cora_dataset.py’ saved [11640/11640] 2022-01-05 12:39:31.378912: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected Reading graph file: /tmp/cora/cora.cites... Done reading 5429 edges from: /tmp/cora/cora.cites (0.01 seconds). Making all edges bi-directional... Done (0.01 seconds). Total graph nodes: 2708 Joining seed and neighbor tf.train.Examples with graph edges... Done creating and writing 2155 merged tf.train.Examples (1.36 seconds). Out-degree histogram: [(1, 386), (2, 468), (3, 452), (4, 309), (5, 540)] Output training data written to TFRecord file: /tmp/cora/train_merged_examples.tfr. Output test data written to TFRecord file: /tmp/cora/test_examples.tfr. Total running time: 0.04 minutes.
Variáveis globais
Os caminhos de arquivo para os dados de comboio e de teste são baseados nos valores de sinalizadores de linha de comando usados para invocar o script 'preprocess_cora_dataset.py' acima.
### Experiment dataset
TRAIN_DATA_PATH = '/tmp/cora/train_merged_examples.tfr'
TEST_DATA_PATH = '/tmp/cora/test_examples.tfr'
### Constants used to identify neighbor features in the input.
NBR_FEATURE_PREFIX = 'NL_nbr_'
NBR_WEIGHT_SUFFIX = '_weight'
Hiperparâmetros
Vamos usar um exemplo de HParams para incluir vários hiperparâmetros e constantes utilizadas para treinamento e avaliação. Descrevemos resumidamente cada um deles abaixo:
num_classes: Há um total de 7 classes diferentes
max_seq_length: Este é o tamanho do vocabulário e todos os exemplos na entrada tem um multi-quente, representação densa saco-de-palavras. Em outras palavras, um valor de 1 para uma palavra indica que a palavra está presente na entrada e um valor de 0 indica que não está.
distance_type: Esta é a distância métrica utilizada para regularizar a amostra com seus vizinhos.
graph_regularization_multiplier: Este controla o peso relativo do termo gráfico regularização na função global de perda.
num_neighbors: O número de vizinhos utilizados para regularização gráfico. Este valor tem de ser inferior ou igual aos
max_nbrscomando da linha de argumento usado acima durante a execuçãopreprocess_cora_dataset.py.num_fc_units: O número de camadas totalmente conectados em nossa rede neural.
train_epochs: O número de épocas de treinamento.
Tamanho do lote usado para treinamento e avaliação: batch_size.
dropout_rate: controla a taxa de abandono a seguir a cada camada totalmente ligado
eval_steps: O número de lotes para processo antes julgando avaliação for concluída. Se definido para
None, todas as instâncias do conjunto de teste são avaliados.
class HParams(object):
"""Hyperparameters used for training."""
def __init__(self):
### dataset parameters
self.num_classes = 7
self.max_seq_length = 1433
### neural graph learning parameters
self.distance_type = nsl.configs.DistanceType.L2
self.graph_regularization_multiplier = 0.1
self.num_neighbors = 1
### model architecture
self.num_fc_units = [50, 50]
### training parameters
self.train_epochs = 100
self.batch_size = 128
self.dropout_rate = 0.5
### eval parameters
self.eval_steps = None # All instances in the test set are evaluated.
HPARAMS = HParams()
Carregar dados de trem e teste
Conforme descrito anteriormente neste caderno, os dados de treinamento de entrada e de teste foram criadas pela 'preprocess_cora_dataset.py'. Vamos carregá-los em dois tf.data.Dataset objetos - um para trem e um para teste.
Na camada de entrada do nosso modelo, vamos extrair não apenas as 'palavras' e o 'rótulo' apresenta de cada amostra, mas vizinho também correspondente recursos com base na hparams.num_neighbors valor. Instâncias com poucos vizinhos que hparams.num_neighbors será atribuído manequim valores para as características vizinho inexistentes.
def make_dataset(file_path, training=False):
"""Creates a `tf.data.TFRecordDataset`.
Args:
file_path: Name of the file in the `.tfrecord` format containing
`tf.train.Example` objects.
training: Boolean indicating if we are in training mode.
Returns:
An instance of `tf.data.TFRecordDataset` containing the `tf.train.Example`
objects.
"""
def parse_example(example_proto):
"""Extracts relevant fields from the `example_proto`.
Args:
example_proto: An instance of `tf.train.Example`.
Returns:
A pair whose first value is a dictionary containing relevant features
and whose second value contains the ground truth label.
"""
# The 'words' feature is a multi-hot, bag-of-words representation of the
# original raw text. A default value is required for examples that don't
# have the feature.
feature_spec = {
'words':
tf.io.FixedLenFeature([HPARAMS.max_seq_length],
tf.int64,
default_value=tf.constant(
0,
dtype=tf.int64,
shape=[HPARAMS.max_seq_length])),
'label':
tf.io.FixedLenFeature((), tf.int64, default_value=-1),
}
# We also extract corresponding neighbor features in a similar manner to
# the features above during training.
if training:
for i in range(HPARAMS.num_neighbors):
nbr_feature_key = '{}{}_{}'.format(NBR_FEATURE_PREFIX, i, 'words')
nbr_weight_key = '{}{}{}'.format(NBR_FEATURE_PREFIX, i,
NBR_WEIGHT_SUFFIX)
feature_spec[nbr_feature_key] = tf.io.FixedLenFeature(
[HPARAMS.max_seq_length],
tf.int64,
default_value=tf.constant(
0, dtype=tf.int64, shape=[HPARAMS.max_seq_length]))
# We assign a default value of 0.0 for the neighbor weight so that
# graph regularization is done on samples based on their exact number
# of neighbors. In other words, non-existent neighbors are discounted.
feature_spec[nbr_weight_key] = tf.io.FixedLenFeature(
[1], tf.float32, default_value=tf.constant([0.0]))
features = tf.io.parse_single_example(example_proto, feature_spec)
label = features.pop('label')
return features, label
dataset = tf.data.TFRecordDataset([file_path])
if training:
dataset = dataset.shuffle(10000)
dataset = dataset.map(parse_example)
dataset = dataset.batch(HPARAMS.batch_size)
return dataset
train_dataset = make_dataset(TRAIN_DATA_PATH, training=True)
test_dataset = make_dataset(TEST_DATA_PATH)
Vamos dar uma olhada no conjunto de dados do trem para ver seu conteúdo.
for feature_batch, label_batch in train_dataset.take(1):
print('Feature list:', list(feature_batch.keys()))
print('Batch of inputs:', feature_batch['words'])
nbr_feature_key = '{}{}_{}'.format(NBR_FEATURE_PREFIX, 0, 'words')
nbr_weight_key = '{}{}{}'.format(NBR_FEATURE_PREFIX, 0, NBR_WEIGHT_SUFFIX)
print('Batch of neighbor inputs:', feature_batch[nbr_feature_key])
print('Batch of neighbor weights:',
tf.reshape(feature_batch[nbr_weight_key], [-1]))
print('Batch of labels:', label_batch)
Feature list: ['NL_nbr_0_weight', 'NL_nbr_0_words', 'words'] Batch of inputs: tf.Tensor( [[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 1 0 0] [0 0 0 ... 0 0 0]], shape=(128, 1433), dtype=int64) Batch of neighbor inputs: tf.Tensor( [[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]], shape=(128, 1433), dtype=int64) Batch of neighbor weights: tf.Tensor( [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.], shape=(128,), dtype=float32) Batch of labels: tf.Tensor( [2 2 6 2 0 6 1 3 5 0 1 2 3 6 1 1 0 3 5 2 3 1 4 1 6 1 3 2 2 2 0 3 2 1 3 3 2 3 3 2 3 2 2 0 2 2 6 0 2 1 1 0 5 2 1 4 2 1 2 4 0 2 5 4 3 6 3 2 1 6 2 4 2 2 6 4 6 4 3 5 2 2 2 4 2 2 2 1 2 2 2 4 2 3 6 2 0 6 6 0 2 6 2 1 2 0 1 1 3 2 0 2 0 2 1 1 3 5 2 1 2 5 1 6 2 4 6 4], shape=(128,), dtype=int64)
Vamos dar uma olhada no conjunto de dados de teste para ver seu conteúdo.
for feature_batch, label_batch in test_dataset.take(1):
print('Feature list:', list(feature_batch.keys()))
print('Batch of inputs:', feature_batch['words'])
print('Batch of labels:', label_batch)
Feature list: ['words'] Batch of inputs: tf.Tensor( [[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]], shape=(128, 1433), dtype=int64) Batch of labels: tf.Tensor( [5 2 2 2 1 2 6 3 2 3 6 1 3 6 4 4 2 3 3 0 2 0 5 2 1 0 6 3 6 4 2 2 3 0 4 2 2 2 2 3 2 2 2 0 2 2 2 2 4 2 3 4 0 2 6 2 1 4 2 0 0 1 4 2 6 0 5 2 2 3 2 5 2 5 2 3 2 2 2 2 2 6 6 3 2 4 2 6 3 2 2 6 2 4 2 2 1 3 4 6 0 0 2 4 2 1 3 6 6 2 6 6 6 1 4 6 4 3 6 6 0 0 2 6 2 4 0 0], shape=(128,), dtype=int64)
Definição de modelo
Para demonstrar o uso da regularização de grafos, construímos primeiro um modelo básico para esse problema. Usaremos uma rede neural feed-forward simples com 2 camadas ocultas e dropout entre elas. Nós ilustrar a criação do modelo de base usando todos os tipos de modelo apoiados pelo tf.Keras quadro - sequencial, funcional e subclasse.
Modelo de base sequencial
def make_mlp_sequential_model(hparams):
"""Creates a sequential multi-layer perceptron model."""
model = tf.keras.Sequential()
model.add(
tf.keras.layers.InputLayer(
input_shape=(hparams.max_seq_length,), name='words'))
# Input is already one-hot encoded in the integer format. We cast it to
# floating point format here.
model.add(
tf.keras.layers.Lambda(lambda x: tf.keras.backend.cast(x, tf.float32)))
for num_units in hparams.num_fc_units:
model.add(tf.keras.layers.Dense(num_units, activation='relu'))
# For sequential models, by default, Keras ensures that the 'dropout' layer
# is invoked only during training.
model.add(tf.keras.layers.Dropout(hparams.dropout_rate))
model.add(tf.keras.layers.Dense(hparams.num_classes))
return model
Modelo de base funcional
def make_mlp_functional_model(hparams):
"""Creates a functional API-based multi-layer perceptron model."""
inputs = tf.keras.Input(
shape=(hparams.max_seq_length,), dtype='int64', name='words')
# Input is already one-hot encoded in the integer format. We cast it to
# floating point format here.
cur_layer = tf.keras.layers.Lambda(
lambda x: tf.keras.backend.cast(x, tf.float32))(
inputs)
for num_units in hparams.num_fc_units:
cur_layer = tf.keras.layers.Dense(num_units, activation='relu')(cur_layer)
# For functional models, by default, Keras ensures that the 'dropout' layer
# is invoked only during training.
cur_layer = tf.keras.layers.Dropout(hparams.dropout_rate)(cur_layer)
outputs = tf.keras.layers.Dense(hparams.num_classes)(cur_layer)
model = tf.keras.Model(inputs, outputs=outputs)
return model
Modelo básico de subclasse
def make_mlp_subclass_model(hparams):
"""Creates a multi-layer perceptron subclass model in Keras."""
class MLP(tf.keras.Model):
"""Subclass model defining a multi-layer perceptron."""
def __init__(self):
super(MLP, self).__init__()
# Input is already one-hot encoded in the integer format. We create a
# layer to cast it to floating point format here.
self.cast_to_float_layer = tf.keras.layers.Lambda(
lambda x: tf.keras.backend.cast(x, tf.float32))
self.dense_layers = [
tf.keras.layers.Dense(num_units, activation='relu')
for num_units in hparams.num_fc_units
]
self.dropout_layer = tf.keras.layers.Dropout(hparams.dropout_rate)
self.output_layer = tf.keras.layers.Dense(hparams.num_classes)
def call(self, inputs, training=False):
cur_layer = self.cast_to_float_layer(inputs['words'])
for dense_layer in self.dense_layers:
cur_layer = dense_layer(cur_layer)
cur_layer = self.dropout_layer(cur_layer, training=training)
outputs = self.output_layer(cur_layer)
return outputs
return MLP()
Criar modelo (s) de base
# Create a base MLP model using the functional API.
# Alternatively, you can also create a sequential or subclass base model using
# the make_mlp_sequential_model() or make_mlp_subclass_model() functions
# respectively, defined above. Note that if a subclass model is used, its
# summary cannot be generated until it is built.
base_model_tag, base_model = 'FUNCTIONAL', make_mlp_functional_model(HPARAMS)
base_model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
words (InputLayer) [(None, 1433)] 0
lambda (Lambda) (None, 1433) 0
dense (Dense) (None, 50) 71700
dropout (Dropout) (None, 50) 0
dense_1 (Dense) (None, 50) 2550
dropout_1 (Dropout) (None, 50) 0
dense_2 (Dense) (None, 7) 357
=================================================================
Total params: 74,607
Trainable params: 74,607
Non-trainable params: 0
_________________________________________________________________
Modelo MLP de base de treinamento
# Compile and train the base MLP model
base_model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
base_model.fit(train_dataset, epochs=HPARAMS.train_epochs, verbose=1)
Epoch 1/100 /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/functional.py:559: UserWarning: Input dict contained keys ['NL_nbr_0_weight', 'NL_nbr_0_words'] which did not match any model input. They will be ignored by the model. inputs = self._flatten_to_reference_inputs(inputs) 17/17 [==============================] - 1s 18ms/step - loss: 1.9521 - accuracy: 0.1838 Epoch 2/100 17/17 [==============================] - 0s 3ms/step - loss: 1.8590 - accuracy: 0.3044 Epoch 3/100 17/17 [==============================] - 0s 3ms/step - loss: 1.7770 - accuracy: 0.3601 Epoch 4/100 17/17 [==============================] - 0s 3ms/step - loss: 1.6655 - accuracy: 0.3898 Epoch 5/100 17/17 [==============================] - 0s 3ms/step - loss: 1.5386 - accuracy: 0.4543 Epoch 6/100 17/17 [==============================] - 0s 3ms/step - loss: 1.3856 - accuracy: 0.5077 Epoch 7/100 17/17 [==============================] - 0s 3ms/step - loss: 1.2736 - accuracy: 0.5531 Epoch 8/100 17/17 [==============================] - 0s 3ms/step - loss: 1.1636 - accuracy: 0.5889 Epoch 9/100 17/17 [==============================] - 0s 3ms/step - loss: 1.0654 - accuracy: 0.6385 Epoch 10/100 17/17 [==============================] - 0s 3ms/step - loss: 0.9703 - accuracy: 0.6761 Epoch 11/100 17/17 [==============================] - 0s 3ms/step - loss: 0.8689 - accuracy: 0.7104 Epoch 12/100 17/17 [==============================] - 0s 3ms/step - loss: 0.7704 - accuracy: 0.7494 Epoch 13/100 17/17 [==============================] - 0s 3ms/step - loss: 0.7157 - accuracy: 0.7810 Epoch 14/100 17/17 [==============================] - 0s 3ms/step - loss: 0.6296 - accuracy: 0.8186 Epoch 15/100 17/17 [==============================] - 0s 3ms/step - loss: 0.5932 - accuracy: 0.8167 Epoch 16/100 17/17 [==============================] - 0s 3ms/step - loss: 0.5526 - accuracy: 0.8464 Epoch 17/100 17/17 [==============================] - 0s 3ms/step - loss: 0.5112 - accuracy: 0.8445 Epoch 18/100 17/17 [==============================] - 0s 3ms/step - loss: 0.4624 - accuracy: 0.8613 Epoch 19/100 17/17 [==============================] - 0s 3ms/step - loss: 0.4163 - accuracy: 0.8696 Epoch 20/100 17/17 [==============================] - 0s 3ms/step - loss: 0.3808 - accuracy: 0.8849 Epoch 21/100 17/17 [==============================] - 0s 3ms/step - loss: 0.3564 - accuracy: 0.8933 Epoch 22/100 17/17 [==============================] - 0s 3ms/step - loss: 0.3453 - accuracy: 0.9002 Epoch 23/100 17/17 [==============================] - 0s 3ms/step - loss: 0.3226 - accuracy: 0.9114 Epoch 24/100 17/17 [==============================] - 0s 3ms/step - loss: 0.3058 - accuracy: 0.9151 Epoch 25/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2798 - accuracy: 0.9146 Epoch 26/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2638 - accuracy: 0.9248 Epoch 27/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2538 - accuracy: 0.9290 Epoch 28/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2356 - accuracy: 0.9411 Epoch 29/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2080 - accuracy: 0.9425 Epoch 30/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2172 - accuracy: 0.9364 Epoch 31/100 17/17 [==============================] - 0s 3ms/step - loss: 0.2259 - accuracy: 0.9225 Epoch 32/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1944 - accuracy: 0.9480 Epoch 33/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1892 - accuracy: 0.9434 Epoch 34/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1718 - accuracy: 0.9592 Epoch 35/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1826 - accuracy: 0.9508 Epoch 36/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1585 - accuracy: 0.9559 Epoch 37/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1605 - accuracy: 0.9545 Epoch 38/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1529 - accuracy: 0.9550 Epoch 39/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1411 - accuracy: 0.9615 Epoch 40/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1366 - accuracy: 0.9624 Epoch 41/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1431 - accuracy: 0.9578 Epoch 42/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1241 - accuracy: 0.9619 Epoch 43/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1310 - accuracy: 0.9661 Epoch 44/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1284 - accuracy: 0.9652 Epoch 45/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1215 - accuracy: 0.9633 Epoch 46/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1130 - accuracy: 0.9722 Epoch 47/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1074 - accuracy: 0.9722 Epoch 48/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1143 - accuracy: 0.9694 Epoch 49/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1015 - accuracy: 0.9740 Epoch 50/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1077 - accuracy: 0.9698 Epoch 51/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1035 - accuracy: 0.9684 Epoch 52/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1076 - accuracy: 0.9694 Epoch 53/100 17/17 [==============================] - 0s 3ms/step - loss: 0.1000 - accuracy: 0.9689 Epoch 54/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0967 - accuracy: 0.9749 Epoch 55/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0994 - accuracy: 0.9703 Epoch 56/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0943 - accuracy: 0.9740 Epoch 57/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.9735 Epoch 58/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0848 - accuracy: 0.9800 Epoch 59/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0836 - accuracy: 0.9782 Epoch 60/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.9735 Epoch 61/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0823 - accuracy: 0.9773 Epoch 62/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0753 - accuracy: 0.9810 Epoch 63/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0746 - accuracy: 0.9777 Epoch 64/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0861 - accuracy: 0.9731 Epoch 65/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0765 - accuracy: 0.9787 Epoch 66/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0750 - accuracy: 0.9791 Epoch 67/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0725 - accuracy: 0.9814 Epoch 68/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 0.9791 Epoch 69/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0645 - accuracy: 0.9842 Epoch 70/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0606 - accuracy: 0.9861 Epoch 71/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0775 - accuracy: 0.9805 Epoch 72/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0655 - accuracy: 0.9800 Epoch 73/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0629 - accuracy: 0.9833 Epoch 74/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0625 - accuracy: 0.9824 Epoch 75/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0607 - accuracy: 0.9838 Epoch 76/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0578 - accuracy: 0.9824 Epoch 77/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0568 - accuracy: 0.9842 Epoch 78/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0595 - accuracy: 0.9833 Epoch 79/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9842 Epoch 80/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0555 - accuracy: 0.9852 Epoch 81/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0517 - accuracy: 0.9870 Epoch 82/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0541 - accuracy: 0.9856 Epoch 83/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0533 - accuracy: 0.9884 Epoch 84/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0509 - accuracy: 0.9838 Epoch 85/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0600 - accuracy: 0.9828 Epoch 86/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0617 - accuracy: 0.9800 Epoch 87/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0599 - accuracy: 0.9800 Epoch 88/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0502 - accuracy: 0.9870 Epoch 89/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0416 - accuracy: 0.9907 Epoch 90/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0542 - accuracy: 0.9842 Epoch 91/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0490 - accuracy: 0.9847 Epoch 92/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0374 - accuracy: 0.9916 Epoch 93/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0467 - accuracy: 0.9893 Epoch 94/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0426 - accuracy: 0.9879 Epoch 95/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0543 - accuracy: 0.9861 Epoch 96/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0420 - accuracy: 0.9870 Epoch 97/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0461 - accuracy: 0.9861 Epoch 98/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 0.9898 Epoch 99/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0406 - accuracy: 0.9907 Epoch 100/100 17/17 [==============================] - 0s 3ms/step - loss: 0.0486 - accuracy: 0.9847 <keras.callbacks.History at 0x7f6f9d5eacd0>
Avalie o modelo básico de MLP
# Helper function to print evaluation metrics.
def print_metrics(model_desc, eval_metrics):
"""Prints evaluation metrics.
Args:
model_desc: A description of the model.
eval_metrics: A dictionary mapping metric names to corresponding values. It
must contain the loss and accuracy metrics.
"""
print('\n')
print('Eval accuracy for ', model_desc, ': ', eval_metrics['accuracy'])
print('Eval loss for ', model_desc, ': ', eval_metrics['loss'])
if 'graph_loss' in eval_metrics:
print('Eval graph loss for ', model_desc, ': ', eval_metrics['graph_loss'])
eval_results = dict(
zip(base_model.metrics_names,
base_model.evaluate(test_dataset, steps=HPARAMS.eval_steps)))
print_metrics('Base MLP model', eval_results)
5/5 [==============================] - 0s 5ms/step - loss: 1.4192 - accuracy: 0.7939 Eval accuracy for Base MLP model : 0.7938517332077026 Eval loss for Base MLP model : 1.4192423820495605
Treine o modelo MLP com regularização de gráfico
Incorporando gráfico regularização no prazo perda de um já existente tf.Keras.Model requer apenas algumas linhas de código. O modelo de base é ajustada para criar uma nova tf.Keras subclasse modelo, cuja perda inclui gráfico regularização.
Para avaliar o benefício incremental da regularização do gráfico, criaremos uma nova instância do modelo base. Isso ocorre porque base_model já foi treinado por algumas iterações, e reutilizar este modelo treinados para criar um modelo regularizado-gráfico não será uma comparação justa para base_model .
# Build a new base MLP model.
base_reg_model_tag, base_reg_model = 'FUNCTIONAL', make_mlp_functional_model(
HPARAMS)
# Wrap the base MLP model with graph regularization.
graph_reg_config = nsl.configs.make_graph_reg_config(
max_neighbors=HPARAMS.num_neighbors,
multiplier=HPARAMS.graph_regularization_multiplier,
distance_type=HPARAMS.distance_type,
sum_over_axis=-1)
graph_reg_model = nsl.keras.GraphRegularization(base_reg_model,
graph_reg_config)
graph_reg_model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
graph_reg_model.fit(train_dataset, epochs=HPARAMS.train_epochs, verbose=1)
Epoch 1/100
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/framework/indexed_slices.py:446: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/GraphRegularization/graph_loss/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/GraphRegularization/graph_loss/Reshape:0", shape=(None, 7), dtype=float32), dense_shape=Tensor("gradient_tape/GraphRegularization/graph_loss/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
"shape. This may consume a large amount of memory." % value)
17/17 [==============================] - 2s 4ms/step - loss: 1.9798 - accuracy: 0.1601 - scaled_graph_loss: 0.0373
Epoch 2/100
17/17 [==============================] - 0s 3ms/step - loss: 1.9024 - accuracy: 0.2979 - scaled_graph_loss: 0.0254
Epoch 3/100
17/17 [==============================] - 0s 3ms/step - loss: 1.8623 - accuracy: 0.3160 - scaled_graph_loss: 0.0317
Epoch 4/100
17/17 [==============================] - 0s 3ms/step - loss: 1.8042 - accuracy: 0.3443 - scaled_graph_loss: 0.0498
Epoch 5/100
17/17 [==============================] - 0s 3ms/step - loss: 1.7552 - accuracy: 0.3582 - scaled_graph_loss: 0.0696
Epoch 6/100
17/17 [==============================] - 0s 3ms/step - loss: 1.7012 - accuracy: 0.4084 - scaled_graph_loss: 0.0866
Epoch 7/100
17/17 [==============================] - 0s 3ms/step - loss: 1.6578 - accuracy: 0.4515 - scaled_graph_loss: 0.1114
Epoch 8/100
17/17 [==============================] - 0s 3ms/step - loss: 1.6058 - accuracy: 0.5039 - scaled_graph_loss: 0.1300
Epoch 9/100
17/17 [==============================] - 0s 3ms/step - loss: 1.5498 - accuracy: 0.5434 - scaled_graph_loss: 0.1508
Epoch 10/100
17/17 [==============================] - 0s 3ms/step - loss: 1.5098 - accuracy: 0.6019 - scaled_graph_loss: 0.1651
Epoch 11/100
17/17 [==============================] - 0s 3ms/step - loss: 1.4746 - accuracy: 0.6302 - scaled_graph_loss: 0.1844
Epoch 12/100
17/17 [==============================] - 0s 3ms/step - loss: 1.4315 - accuracy: 0.6520 - scaled_graph_loss: 0.1917
Epoch 13/100
17/17 [==============================] - 0s 3ms/step - loss: 1.3932 - accuracy: 0.6770 - scaled_graph_loss: 0.2024
Epoch 14/100
17/17 [==============================] - 0s 3ms/step - loss: 1.3645 - accuracy: 0.7183 - scaled_graph_loss: 0.2145
Epoch 15/100
17/17 [==============================] - 0s 3ms/step - loss: 1.3265 - accuracy: 0.7369 - scaled_graph_loss: 0.2324
Epoch 16/100
17/17 [==============================] - 0s 3ms/step - loss: 1.3045 - accuracy: 0.7555 - scaled_graph_loss: 0.2358
Epoch 17/100
17/17 [==============================] - 0s 3ms/step - loss: 1.2836 - accuracy: 0.7652 - scaled_graph_loss: 0.2404
Epoch 18/100
17/17 [==============================] - 0s 3ms/step - loss: 1.2456 - accuracy: 0.7898 - scaled_graph_loss: 0.2469
Epoch 19/100
17/17 [==============================] - 0s 3ms/step - loss: 1.2348 - accuracy: 0.8074 - scaled_graph_loss: 0.2615
Epoch 20/100
17/17 [==============================] - 0s 3ms/step - loss: 1.2000 - accuracy: 0.8074 - scaled_graph_loss: 0.2542
Epoch 21/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1994 - accuracy: 0.8260 - scaled_graph_loss: 0.2729
Epoch 22/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1825 - accuracy: 0.8269 - scaled_graph_loss: 0.2676
Epoch 23/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1598 - accuracy: 0.8455 - scaled_graph_loss: 0.2742
Epoch 24/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1543 - accuracy: 0.8534 - scaled_graph_loss: 0.2797
Epoch 25/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1456 - accuracy: 0.8552 - scaled_graph_loss: 0.2714
Epoch 26/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1154 - accuracy: 0.8566 - scaled_graph_loss: 0.2796
Epoch 27/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1150 - accuracy: 0.8687 - scaled_graph_loss: 0.2850
Epoch 28/100
17/17 [==============================] - 0s 3ms/step - loss: 1.1154 - accuracy: 0.8626 - scaled_graph_loss: 0.2772
Epoch 29/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0806 - accuracy: 0.8733 - scaled_graph_loss: 0.2756
Epoch 30/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0828 - accuracy: 0.8626 - scaled_graph_loss: 0.2907
Epoch 31/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0724 - accuracy: 0.8886 - scaled_graph_loss: 0.2834
Epoch 32/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0589 - accuracy: 0.8826 - scaled_graph_loss: 0.2881
Epoch 33/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0490 - accuracy: 0.8872 - scaled_graph_loss: 0.2972
Epoch 34/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0550 - accuracy: 0.8923 - scaled_graph_loss: 0.2935
Epoch 35/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0397 - accuracy: 0.8840 - scaled_graph_loss: 0.2795
Epoch 36/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0360 - accuracy: 0.8891 - scaled_graph_loss: 0.2966
Epoch 37/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0235 - accuracy: 0.8961 - scaled_graph_loss: 0.2890
Epoch 38/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0219 - accuracy: 0.8984 - scaled_graph_loss: 0.2965
Epoch 39/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0168 - accuracy: 0.9044 - scaled_graph_loss: 0.3023
Epoch 40/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0148 - accuracy: 0.9035 - scaled_graph_loss: 0.2984
Epoch 41/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9956 - accuracy: 0.9118 - scaled_graph_loss: 0.2888
Epoch 42/100
17/17 [==============================] - 0s 3ms/step - loss: 1.0019 - accuracy: 0.9021 - scaled_graph_loss: 0.2877
Epoch 43/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9956 - accuracy: 0.9049 - scaled_graph_loss: 0.2912
Epoch 44/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9986 - accuracy: 0.9026 - scaled_graph_loss: 0.3040
Epoch 45/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9939 - accuracy: 0.9067 - scaled_graph_loss: 0.3016
Epoch 46/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9828 - accuracy: 0.9058 - scaled_graph_loss: 0.2877
Epoch 47/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9629 - accuracy: 0.9137 - scaled_graph_loss: 0.2844
Epoch 48/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9645 - accuracy: 0.9146 - scaled_graph_loss: 0.2933
Epoch 49/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9752 - accuracy: 0.9165 - scaled_graph_loss: 0.3013
Epoch 50/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9552 - accuracy: 0.9179 - scaled_graph_loss: 0.2865
Epoch 51/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9539 - accuracy: 0.9193 - scaled_graph_loss: 0.3044
Epoch 52/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9443 - accuracy: 0.9183 - scaled_graph_loss: 0.3010
Epoch 53/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9559 - accuracy: 0.9244 - scaled_graph_loss: 0.2987
Epoch 54/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9497 - accuracy: 0.9225 - scaled_graph_loss: 0.2979
Epoch 55/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9674 - accuracy: 0.9183 - scaled_graph_loss: 0.3034
Epoch 56/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9537 - accuracy: 0.9174 - scaled_graph_loss: 0.2834
Epoch 57/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9341 - accuracy: 0.9188 - scaled_graph_loss: 0.2939
Epoch 58/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9392 - accuracy: 0.9225 - scaled_graph_loss: 0.2998
Epoch 59/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9240 - accuracy: 0.9313 - scaled_graph_loss: 0.3022
Epoch 60/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9368 - accuracy: 0.9267 - scaled_graph_loss: 0.2979
Epoch 61/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9306 - accuracy: 0.9234 - scaled_graph_loss: 0.2952
Epoch 62/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9197 - accuracy: 0.9230 - scaled_graph_loss: 0.2916
Epoch 63/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9360 - accuracy: 0.9206 - scaled_graph_loss: 0.2947
Epoch 64/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9181 - accuracy: 0.9299 - scaled_graph_loss: 0.2996
Epoch 65/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9105 - accuracy: 0.9341 - scaled_graph_loss: 0.2981
Epoch 66/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9014 - accuracy: 0.9323 - scaled_graph_loss: 0.2897
Epoch 67/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9059 - accuracy: 0.9364 - scaled_graph_loss: 0.3083
Epoch 68/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9053 - accuracy: 0.9309 - scaled_graph_loss: 0.2976
Epoch 69/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9099 - accuracy: 0.9258 - scaled_graph_loss: 0.3069
Epoch 70/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9025 - accuracy: 0.9355 - scaled_graph_loss: 0.2890
Epoch 71/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8849 - accuracy: 0.9281 - scaled_graph_loss: 0.2933
Epoch 72/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8959 - accuracy: 0.9323 - scaled_graph_loss: 0.2918
Epoch 73/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9074 - accuracy: 0.9248 - scaled_graph_loss: 0.3065
Epoch 74/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8845 - accuracy: 0.9369 - scaled_graph_loss: 0.2874
Epoch 75/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8873 - accuracy: 0.9401 - scaled_graph_loss: 0.2996
Epoch 76/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8942 - accuracy: 0.9327 - scaled_graph_loss: 0.3086
Epoch 77/100
17/17 [==============================] - 0s 3ms/step - loss: 0.9052 - accuracy: 0.9253 - scaled_graph_loss: 0.2986
Epoch 78/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8811 - accuracy: 0.9336 - scaled_graph_loss: 0.2948
Epoch 79/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8896 - accuracy: 0.9276 - scaled_graph_loss: 0.2919
Epoch 80/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8853 - accuracy: 0.9313 - scaled_graph_loss: 0.2944
Epoch 81/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8875 - accuracy: 0.9323 - scaled_graph_loss: 0.2925
Epoch 82/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8639 - accuracy: 0.9323 - scaled_graph_loss: 0.2967
Epoch 83/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8820 - accuracy: 0.9332 - scaled_graph_loss: 0.3047
Epoch 84/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8752 - accuracy: 0.9346 - scaled_graph_loss: 0.2942
Epoch 85/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8651 - accuracy: 0.9374 - scaled_graph_loss: 0.3066
Epoch 86/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8765 - accuracy: 0.9332 - scaled_graph_loss: 0.2881
Epoch 87/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8691 - accuracy: 0.9420 - scaled_graph_loss: 0.3030
Epoch 88/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8631 - accuracy: 0.9374 - scaled_graph_loss: 0.2916
Epoch 89/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8651 - accuracy: 0.9392 - scaled_graph_loss: 0.3032
Epoch 90/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8632 - accuracy: 0.9420 - scaled_graph_loss: 0.3019
Epoch 91/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8600 - accuracy: 0.9425 - scaled_graph_loss: 0.2965
Epoch 92/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8569 - accuracy: 0.9346 - scaled_graph_loss: 0.2977
Epoch 93/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8704 - accuracy: 0.9374 - scaled_graph_loss: 0.3083
Epoch 94/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8562 - accuracy: 0.9406 - scaled_graph_loss: 0.2883
Epoch 95/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8545 - accuracy: 0.9415 - scaled_graph_loss: 0.3030
Epoch 96/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8592 - accuracy: 0.9332 - scaled_graph_loss: 0.2927
Epoch 97/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8503 - accuracy: 0.9397 - scaled_graph_loss: 0.2927
Epoch 98/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8434 - accuracy: 0.9462 - scaled_graph_loss: 0.2937
Epoch 99/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8578 - accuracy: 0.9374 - scaled_graph_loss: 0.3064
Epoch 100/100
17/17 [==============================] - 0s 3ms/step - loss: 0.8504 - accuracy: 0.9411 - scaled_graph_loss: 0.3043
<keras.callbacks.History at 0x7f70041be650>
Avalie o modelo MLP com regularização de gráfico
eval_results = dict(
zip(graph_reg_model.metrics_names,
graph_reg_model.evaluate(test_dataset, steps=HPARAMS.eval_steps)))
print_metrics('MLP + graph regularization', eval_results)
5/5 [==============================] - 0s 5ms/step - loss: 0.8884 - accuracy: 0.7957 Eval accuracy for MLP + graph regularization : 0.7956600189208984 Eval loss for MLP + graph regularization : 0.8883611559867859
A precisão do modelo regularizado-gráfico é de cerca de 2-3% mais elevada do que a do modelo de base ( base_model ).
Conclusão
Demonstramos o uso de regularização de gráfico para classificação de documentos em um gráfico de citação natural (Cora) usando o framework Neural Structured Learning (NSL). Nosso tutorial avançado envolve sintetizar gráficos baseados em embeddings amostra antes de treinar uma rede neural com regularização gráfico. Essa abordagem é útil se a entrada não contém um gráfico explícito.
Nós encorajamos os usuários a experimentar mais, variando a quantidade de supervisão, bem como tentando diferentes arquiteturas neurais para regularização de gráfico.