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Visión general
La regularización de gráficos es una técnica específica bajo el paradigma más amplio de aprendizaje de gráficos neuronales ( Bui et al., 2018 ). La idea central es entrenar modelos de redes neuronales con un objetivo de regularización de gráficos, aprovechando datos etiquetados y no etiquetados.
En este tutorial, exploraremos el uso de la regularización de gráficos para clasificar documentos que forman un gráfico natural (orgánico).
La receta general para crear un modelo con regularización de gráficos utilizando el marco de aprendizaje estructurado neuronal (NSL) es la siguiente:
- Genere datos de entrenamiento a partir del gráfico de entrada y las funciones de muestra. Los nodos en el gráfico corresponden a muestras y los bordes en el gráfico corresponden a similitudes entre pares de muestras. Los datos de entrenamiento resultantes contendrán características vecinas además de las características originales del nodo.
- Cree una red neuronal como modelo base utilizando la API secuencial, funcional o de subclase de
Keras. - Envuelva el modelo base con la clase contenedora
GraphRegularization, que es proporcionada por el marco NSL, para crear un nuevo modelo gráfico deKeras. Este nuevo modelo incluirá un gráfico de pérdida de regularización como término de regularización en su objetivo de entrenamiento. - Entrenar y evaluar el modelo gráfico de
Keras.
Preparar
Instale el paquete Neural Structured Learning.
pip install --quiet neural-structured-learning
Dependencias e importaciones
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: 2.2.0 Eager mode: True GPU is NOT AVAILABLE
Conjunto de datos de Cora
El conjunto de datos de Cora es un gráfico de citas donde los nodos representan artículos de aprendizaje automático y los bordes representan citas entre pares de artículos. La tarea involucrada es la clasificación de documentos donde el objetivo es categorizar cada artículo en una de 7 categorías. En otras palabras, este es un problema de clasificación de clases múltiples con 7 clases.
Grafico
La gráfica original está dirigida. Sin embargo, a los efectos de este ejemplo, consideramos la versión no dirigida de este gráfico. Entonces, si el artículo A cita el artículo B, también consideramos que el artículo B ha citado A. Aunque esto no es necesariamente cierto, en este ejemplo, consideramos las citas como un sustituto de la similitud, que generalmente es una propiedad conmutativa.
Caracteristicas
Cada papel en la entrada contiene efectivamente 2 características:
Palabras : Una representación densa, con varias palabras en caliente del texto en el documento. El vocabulario del conjunto de datos Cora contiene 1433 palabras únicas. Entonces, la longitud de esta característica es 1433, y el valor en la posición 'i' es 0/1, lo que indica si la palabra 'i' en el vocabulario existe en el documento dado o no.
Etiqueta : un número entero que representa el ID de clase (categoría) del artículo.
Descarga el conjunto de datos de 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
Convierta los datos de Cora al formato NSL
Para preprocesar el conjunto de datos Cora y convertirlo al formato requerido por Neural Structured Learning, ejecutaremos el script 'preprocess_cora_dataset.py' , que se incluye en el repositorio github de NSL. Este script hace lo siguiente:
- Genere entidades vecinas utilizando las entidades de nodo originales y el gráfico.
- Genere divisiones de datos de prueba y tren que
tf.train.Exampleinstancias detf.train.Example. - Conservar el tren resultante y los datos de prueba en formato
TFRecord.
!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
--2020-07-01 11:15:33-- 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)... 151.101.192.133, 151.101.128.133, 151.101.64.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.192.133|: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 2020-07-01 11:15:33 (84.9 MB/s) - ‘preprocess_cora_dataset.py’ saved [11640/11640] 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 (0.06 seconds). Total graph nodes: 2708 Joining seed and neighbor tf.train.Examples with graph edges... Done creating and writing 2155 merged tf.train.Examples (1.38 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.
Variables globales
Las rutas de archivo al tren y a los datos de prueba se basan en los valores de la marca de línea de comando que se utilizan para invocar el script 'preprocess_cora_dataset.py' anterior.
### 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'
Hiperparámetros
Usaremos una instancia de HParams para incluir varios hiperparámetros y constantes usados para entrenamiento y evaluación. A continuación, describimos brevemente cada uno de ellos:
num_classes : Hay un total de 7 clases diferentes
max_seq_length : Este es el tamaño del vocabulario y todas las instancias en la entrada tienen una representación densa de múltiples palabras calientes. En otras palabras, un valor de 1 para una palabra indica que la palabra está presente en la entrada y un valor de 0 indica que no lo está.
distance_type : esta es la métrica de distancia utilizada para regularizar la muestra con sus vecinos.
graph_regularization_multiplier : Controla el peso relativo del término de regularización del gráfico en la función de pérdida general.
num_neighbors : el número de vecinos utilizados para la regularización del gráfico. Este valor tiene que ser menor o igual que el argumento de línea de comando
max_nbrsusado anteriormente cuando se ejecutapreprocess_cora_dataset.py.num_fc_units : el número de capas completamente conectadas en nuestra red neuronal.
train_epochs : el número de épocas de entrenamiento.
batch_size : tamaño de lote usado para entrenamiento y evaluación.
dropout_rate : controla la tasa de abandono después de cada capa completamente conectada
eval_steps : el número de lotes a procesar antes de considerar que la evaluación está completa. Si se establece en
None, se evalúan todas las instancias del conjunto de prueba.
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()
Tren de carga y datos de prueba
Como se describió anteriormente en este cuaderno, los datos de prueba y entrenamiento de entrada han sido creados por 'preprocess_cora_dataset.py' . Los tf.data.Dataset en dos objetos tf.data.Dataset : uno para entrenar y otro para prueba.
En la capa de entrada de nuestro modelo, extraeremos no solo las 'palabras' y las características de la 'etiqueta' de cada muestra, sino también las características vecinas correspondientes basadas en el valor hparams.num_neighbors . A las instancias con menos vecinos que hparams.num_neighbors se les asignarán valores ficticios para esas características vecinas no existentes.
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)
Echemos un vistazo al conjunto de datos del tren para ver su contenido.
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 ... 0 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( [4 3 1 2 1 6 2 5 6 2 2 6 5 0 2 2 1 6 2 2 2 2 5 4 2 0 2 1 1 2 0 5 2 2 2 0 2 2 0 6 1 1 0 2 1 2 3 2 0 0 0 4 1 3 3 1 2 5 3 3 1 1 6 0 0 4 6 5 6 0 3 4 2 2 2 3 3 2 4 0 2 3 2 2 3 1 2 2 1 0 6 1 2 1 6 2 1 0 4 3 2 5 2 3 1 0 3 4 3 4 1 0 5 6 4 2 1 1 2 5 3 4 3 1 3 2 6 3], shape=(128,), dtype=int64)
Echemos un vistazo al conjunto de datos de prueba para ver su contenido.
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)
Definición de modelo
Para demostrar el uso de la regularización de gráficos, primero construimos un modelo base para este problema. Usaremos una red neuronal de avance simple con 2 capas ocultas y abandono en el medio. Ilustramos la creación del modelo base utilizando todos los tipos de modelos compatibles con el marco tf.Keras : secuencial, funcional y subclase.
Modelo base secuencial
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, activation='softmax'))
return model
Modelo básico 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, activation='softmax')(
cur_layer)
model = tf.keras.Model(inputs, outputs=outputs)
return model
Modelo base de subclase
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, activation='softmax')
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()
Crear modelo (s) 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 base de tren
# Compile and train the base MLP model
base_model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
base_model.fit(train_dataset, epochs=HPARAMS.train_epochs, verbose=1)
Epoch 1/100 17/17 [==============================] - 0s 11ms/step - loss: 1.9256 - accuracy: 0.1870 Epoch 2/100 17/17 [==============================] - 0s 10ms/step - loss: 1.8410 - accuracy: 0.2835 Epoch 3/100 17/17 [==============================] - 0s 9ms/step - loss: 1.7479 - accuracy: 0.3374 Epoch 4/100 17/17 [==============================] - 0s 10ms/step - loss: 1.6384 - accuracy: 0.3884 Epoch 5/100 17/17 [==============================] - 0s 9ms/step - loss: 1.5086 - accuracy: 0.4390 Epoch 6/100 17/17 [==============================] - 0s 10ms/step - loss: 1.3606 - accuracy: 0.5016 Epoch 7/100 17/17 [==============================] - 0s 9ms/step - loss: 1.2165 - accuracy: 0.5791 Epoch 8/100 17/17 [==============================] - 0s 10ms/step - loss: 1.0783 - accuracy: 0.6311 Epoch 9/100 17/17 [==============================] - 0s 9ms/step - loss: 0.9552 - accuracy: 0.6947 Epoch 10/100 17/17 [==============================] - 0s 9ms/step - loss: 0.8680 - accuracy: 0.7090 Epoch 11/100 17/17 [==============================] - 0s 9ms/step - loss: 0.7915 - accuracy: 0.7425 Epoch 12/100 17/17 [==============================] - 0s 9ms/step - loss: 0.7124 - accuracy: 0.7773 Epoch 13/100 17/17 [==============================] - 0s 9ms/step - loss: 0.6582 - accuracy: 0.7907 Epoch 14/100 17/17 [==============================] - 0s 10ms/step - loss: 0.6021 - accuracy: 0.8065 Epoch 15/100 17/17 [==============================] - 0s 10ms/step - loss: 0.5416 - accuracy: 0.8325 Epoch 16/100 17/17 [==============================] - 0s 10ms/step - loss: 0.5042 - accuracy: 0.8473 Epoch 17/100 17/17 [==============================] - 0s 10ms/step - loss: 0.4433 - accuracy: 0.8761 Epoch 18/100 17/17 [==============================] - 0s 10ms/step - loss: 0.4310 - accuracy: 0.8640 Epoch 19/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3894 - accuracy: 0.8840 Epoch 20/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3676 - accuracy: 0.8891 Epoch 21/100 17/17 [==============================] - 0s 10ms/step - loss: 0.3576 - accuracy: 0.8812 Epoch 22/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3132 - accuracy: 0.9067 Epoch 23/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3058 - accuracy: 0.9142 Epoch 24/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2924 - accuracy: 0.9155 Epoch 25/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2769 - accuracy: 0.9197 Epoch 26/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2636 - accuracy: 0.9244 Epoch 27/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2429 - accuracy: 0.9313 Epoch 28/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2324 - accuracy: 0.9323 Epoch 29/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2285 - accuracy: 0.9346 Epoch 30/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2039 - accuracy: 0.9374 Epoch 31/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1943 - accuracy: 0.9471 Epoch 32/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1898 - accuracy: 0.9439 Epoch 33/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1879 - accuracy: 0.9425 Epoch 34/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1828 - accuracy: 0.9443 Epoch 35/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1635 - accuracy: 0.9541 Epoch 36/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1648 - accuracy: 0.9476 Epoch 37/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1603 - accuracy: 0.9499 Epoch 38/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1428 - accuracy: 0.9624 Epoch 39/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1483 - accuracy: 0.9601 Epoch 40/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1352 - accuracy: 0.9582 Epoch 41/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1379 - accuracy: 0.9555 Epoch 42/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1410 - accuracy: 0.9582 Epoch 43/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1198 - accuracy: 0.9684 Epoch 44/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1148 - accuracy: 0.9731 Epoch 45/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1228 - accuracy: 0.9657 Epoch 46/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1135 - accuracy: 0.9703 Epoch 47/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1134 - accuracy: 0.9661 Epoch 48/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1175 - accuracy: 0.9619 Epoch 49/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1002 - accuracy: 0.9703 Epoch 50/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1143 - accuracy: 0.9671 Epoch 51/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0923 - accuracy: 0.9777 Epoch 52/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1068 - accuracy: 0.9731 Epoch 53/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0972 - accuracy: 0.9712 Epoch 54/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0828 - accuracy: 0.9796 Epoch 55/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1036 - accuracy: 0.9703 Epoch 56/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0954 - accuracy: 0.9745 Epoch 57/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0883 - accuracy: 0.9768 Epoch 58/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0859 - accuracy: 0.9777 Epoch 59/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0856 - accuracy: 0.9759 Epoch 60/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0858 - accuracy: 0.9754 Epoch 61/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0848 - accuracy: 0.9726 Epoch 62/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0840 - accuracy: 0.9763 Epoch 63/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0770 - accuracy: 0.9805 Epoch 64/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0823 - accuracy: 0.9745 Epoch 65/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0665 - accuracy: 0.9828 Epoch 66/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0788 - accuracy: 0.9777 Epoch 67/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0690 - accuracy: 0.9800 Epoch 68/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0683 - accuracy: 0.9805 Epoch 69/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9838 Epoch 70/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9833 Epoch 71/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9810 Epoch 72/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0704 - accuracy: 0.9800 Epoch 73/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0645 - accuracy: 0.9814 Epoch 74/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9791 Epoch 75/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0638 - accuracy: 0.9791 Epoch 76/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0648 - accuracy: 0.9814 Epoch 77/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0591 - accuracy: 0.9838 Epoch 78/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0606 - accuracy: 0.9861 Epoch 79/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0699 - accuracy: 0.9814 Epoch 80/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0603 - accuracy: 0.9828 Epoch 81/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0629 - accuracy: 0.9828 Epoch 82/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0596 - accuracy: 0.9828 Epoch 83/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0542 - accuracy: 0.9828 Epoch 84/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0452 - accuracy: 0.9893 Epoch 85/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0551 - accuracy: 0.9838 Epoch 86/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0555 - accuracy: 0.9842 Epoch 87/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0514 - accuracy: 0.9824 Epoch 88/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0553 - accuracy: 0.9847 Epoch 89/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0475 - accuracy: 0.9884 Epoch 90/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0476 - accuracy: 0.9893 Epoch 91/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0427 - accuracy: 0.9903 Epoch 92/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0475 - accuracy: 0.9847 Epoch 93/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0423 - accuracy: 0.9893 Epoch 94/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0473 - accuracy: 0.9865 Epoch 95/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0560 - accuracy: 0.9819 Epoch 96/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0547 - accuracy: 0.9810 Epoch 97/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0576 - accuracy: 0.9814 Epoch 98/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0429 - accuracy: 0.9893 Epoch 99/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0440 - accuracy: 0.9875 Epoch 100/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0513 - accuracy: 0.9838 <tensorflow.python.keras.callbacks.History at 0x7fc47a3c78d0>
Evaluar el modelo MLP base
# 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: 1.3380 - accuracy: 0.7740 Eval accuracy for Base MLP model : 0.7739602327346802 Eval loss for Base MLP model : 1.3379606008529663
Entrene el modelo MLP con regularización de gráficos
La incorporación de la regularización de gráficos en el término de pérdida de un tf.Keras.Model existente requiere solo unas pocas líneas de código. El modelo base se tf.Keras para crear un nuevo modelo de subclase tf.Keras , cuya pérdida incluye la regularización de gráficos.
Para evaluar el beneficio incremental de la regularización de gráficos, crearemos una nueva instancia de modelo base. Esto se debe a que base_model ya ha sido entrenado para algunas iteraciones, y reutilizar este modelo entrenado para crear un modelo regularizado por gráficos no será una comparación 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='sparse_categorical_crossentropy',
metrics=['accuracy'])
graph_reg_model.fit(train_dataset, epochs=HPARAMS.train_epochs, verbose=1)
Epoch 1/100 /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " 17/17 [==============================] - 0s 10ms/step - loss: 1.9454 - accuracy: 0.1652 - graph_loss: 0.0076 Epoch 2/100 17/17 [==============================] - 0s 10ms/step - loss: 1.8517 - accuracy: 0.2956 - graph_loss: 0.0117 Epoch 3/100 17/17 [==============================] - 0s 10ms/step - loss: 1.7589 - accuracy: 0.3151 - graph_loss: 0.0261 Epoch 4/100 17/17 [==============================] - 0s 10ms/step - loss: 1.6714 - accuracy: 0.3392 - graph_loss: 0.0476 Epoch 5/100 17/17 [==============================] - 0s 9ms/step - loss: 1.5607 - accuracy: 0.4037 - graph_loss: 0.0622 Epoch 6/100 17/17 [==============================] - 0s 10ms/step - loss: 1.4486 - accuracy: 0.4807 - graph_loss: 0.0921 Epoch 7/100 17/17 [==============================] - 0s 10ms/step - loss: 1.3135 - accuracy: 0.5383 - graph_loss: 0.1236 Epoch 8/100 17/17 [==============================] - 0s 10ms/step - loss: 1.1902 - accuracy: 0.5912 - graph_loss: 0.1616 Epoch 9/100 17/17 [==============================] - 0s 10ms/step - loss: 1.0647 - accuracy: 0.6575 - graph_loss: 0.1920 Epoch 10/100 17/17 [==============================] - 0s 9ms/step - loss: 0.9416 - accuracy: 0.7067 - graph_loss: 0.2181 Epoch 11/100 17/17 [==============================] - 0s 10ms/step - loss: 0.8601 - accuracy: 0.7378 - graph_loss: 0.2470 Epoch 12/100 17/17 [==============================] - 0s 9ms/step - loss: 0.7968 - accuracy: 0.7462 - graph_loss: 0.2565 Epoch 13/100 17/17 [==============================] - 0s 10ms/step - loss: 0.6881 - accuracy: 0.7912 - graph_loss: 0.2681 Epoch 14/100 17/17 [==============================] - 0s 10ms/step - loss: 0.6548 - accuracy: 0.8139 - graph_loss: 0.2941 Epoch 15/100 17/17 [==============================] - 0s 10ms/step - loss: 0.5874 - accuracy: 0.8376 - graph_loss: 0.3010 Epoch 16/100 17/17 [==============================] - 0s 9ms/step - loss: 0.5537 - accuracy: 0.8348 - graph_loss: 0.3014 Epoch 17/100 17/17 [==============================] - 0s 10ms/step - loss: 0.5123 - accuracy: 0.8529 - graph_loss: 0.3097 Epoch 18/100 17/17 [==============================] - 0s 10ms/step - loss: 0.4771 - accuracy: 0.8640 - graph_loss: 0.3192 Epoch 19/100 17/17 [==============================] - 0s 10ms/step - loss: 0.4294 - accuracy: 0.8826 - graph_loss: 0.3182 Epoch 20/100 17/17 [==============================] - 0s 10ms/step - loss: 0.4109 - accuracy: 0.8854 - graph_loss: 0.3169 Epoch 21/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3901 - accuracy: 0.8965 - graph_loss: 0.3250 Epoch 22/100 17/17 [==============================] - 0s 9ms/step - loss: 0.3700 - accuracy: 0.8956 - graph_loss: 0.3349 Epoch 23/100 17/17 [==============================] - 0s 10ms/step - loss: 0.3716 - accuracy: 0.8974 - graph_loss: 0.3408 Epoch 24/100 17/17 [==============================] - 0s 10ms/step - loss: 0.3258 - accuracy: 0.9202 - graph_loss: 0.3361 Epoch 25/100 17/17 [==============================] - 0s 10ms/step - loss: 0.3043 - accuracy: 0.9253 - graph_loss: 0.3351 Epoch 26/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2919 - accuracy: 0.9253 - graph_loss: 0.3361 Epoch 27/100 17/17 [==============================] - 0s 10ms/step - loss: 0.3005 - accuracy: 0.9202 - graph_loss: 0.3249 Epoch 28/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2629 - accuracy: 0.9336 - graph_loss: 0.3442 Epoch 29/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2617 - accuracy: 0.9401 - graph_loss: 0.3302 Epoch 30/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2510 - accuracy: 0.9383 - graph_loss: 0.3436 Epoch 31/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2452 - accuracy: 0.9411 - graph_loss: 0.3364 Epoch 32/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2397 - accuracy: 0.9466 - graph_loss: 0.3333 Epoch 33/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2239 - accuracy: 0.9466 - graph_loss: 0.3373 Epoch 34/100 17/17 [==============================] - 0s 9ms/step - loss: 0.2084 - accuracy: 0.9513 - graph_loss: 0.3330 Epoch 35/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2075 - accuracy: 0.9499 - graph_loss: 0.3383 Epoch 36/100 17/17 [==============================] - 0s 10ms/step - loss: 0.2064 - accuracy: 0.9513 - graph_loss: 0.3394 Epoch 37/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1857 - accuracy: 0.9568 - graph_loss: 0.3371 Epoch 38/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1799 - accuracy: 0.9601 - graph_loss: 0.3477 Epoch 39/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1844 - accuracy: 0.9573 - graph_loss: 0.3385 Epoch 40/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1823 - accuracy: 0.9592 - graph_loss: 0.3445 Epoch 41/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1713 - accuracy: 0.9615 - graph_loss: 0.3451 Epoch 42/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1669 - accuracy: 0.9624 - graph_loss: 0.3398 Epoch 43/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1692 - accuracy: 0.9671 - graph_loss: 0.3483 Epoch 44/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1605 - accuracy: 0.9647 - graph_loss: 0.3437 Epoch 45/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1485 - accuracy: 0.9703 - graph_loss: 0.3338 Epoch 46/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1467 - accuracy: 0.9717 - graph_loss: 0.3405 Epoch 47/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1492 - accuracy: 0.9694 - graph_loss: 0.3466 Epoch 48/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1577 - accuracy: 0.9666 - graph_loss: 0.3338 Epoch 49/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1363 - accuracy: 0.9773 - graph_loss: 0.3424 Epoch 50/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1511 - accuracy: 0.9694 - graph_loss: 0.3402 Epoch 51/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1366 - accuracy: 0.9759 - graph_loss: 0.3385 Epoch 52/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1254 - accuracy: 0.9777 - graph_loss: 0.3474 Epoch 53/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1289 - accuracy: 0.9740 - graph_loss: 0.3469 Epoch 54/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1410 - accuracy: 0.9689 - graph_loss: 0.3475 Epoch 55/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1356 - accuracy: 0.9703 - graph_loss: 0.3483 Epoch 56/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1283 - accuracy: 0.9773 - graph_loss: 0.3412 Epoch 57/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1264 - accuracy: 0.9745 - graph_loss: 0.3473 Epoch 58/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1242 - accuracy: 0.9740 - graph_loss: 0.3443 Epoch 59/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1144 - accuracy: 0.9782 - graph_loss: 0.3440 Epoch 60/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1250 - accuracy: 0.9735 - graph_loss: 0.3357 Epoch 61/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1190 - accuracy: 0.9787 - graph_loss: 0.3400 Epoch 62/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1141 - accuracy: 0.9814 - graph_loss: 0.3419 Epoch 63/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1085 - accuracy: 0.9787 - graph_loss: 0.3395 Epoch 64/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1148 - accuracy: 0.9768 - graph_loss: 0.3504 Epoch 65/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1137 - accuracy: 0.9791 - graph_loss: 0.3360 Epoch 66/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1121 - accuracy: 0.9745 - graph_loss: 0.3469 Epoch 67/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1046 - accuracy: 0.9810 - graph_loss: 0.3476 Epoch 68/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1112 - accuracy: 0.9791 - graph_loss: 0.3431 Epoch 69/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1075 - accuracy: 0.9787 - graph_loss: 0.3455 Epoch 70/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0986 - accuracy: 0.9875 - graph_loss: 0.3403 Epoch 71/100 17/17 [==============================] - 0s 9ms/step - loss: 0.1141 - accuracy: 0.9782 - graph_loss: 0.3508 Epoch 72/100 17/17 [==============================] - 0s 10ms/step - loss: 0.1012 - accuracy: 0.9814 - graph_loss: 0.3453 Epoch 73/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0958 - accuracy: 0.9833 - graph_loss: 0.3430 Epoch 74/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0958 - accuracy: 0.9842 - graph_loss: 0.3447 Epoch 75/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0988 - accuracy: 0.9842 - graph_loss: 0.3430 Epoch 76/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0915 - accuracy: 0.9856 - graph_loss: 0.3475 Epoch 77/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0960 - accuracy: 0.9833 - graph_loss: 0.3353 Epoch 78/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0916 - accuracy: 0.9838 - graph_loss: 0.3441 Epoch 79/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0979 - accuracy: 0.9800 - graph_loss: 0.3476 Epoch 80/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0994 - accuracy: 0.9782 - graph_loss: 0.3400 Epoch 81/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0978 - accuracy: 0.9838 - graph_loss: 0.3386 Epoch 82/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0994 - accuracy: 0.9805 - graph_loss: 0.3416 Epoch 83/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0957 - accuracy: 0.9838 - graph_loss: 0.3398 Epoch 84/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0896 - accuracy: 0.9879 - graph_loss: 0.3379 Epoch 85/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0891 - accuracy: 0.9838 - graph_loss: 0.3441 Epoch 86/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0906 - accuracy: 0.9847 - graph_loss: 0.3445 Epoch 87/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0891 - accuracy: 0.9852 - graph_loss: 0.3506 Epoch 88/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0821 - accuracy: 0.9898 - graph_loss: 0.3448 Epoch 89/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0803 - accuracy: 0.9865 - graph_loss: 0.3370 Epoch 90/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0896 - accuracy: 0.9828 - graph_loss: 0.3428 Epoch 91/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0887 - accuracy: 0.9852 - graph_loss: 0.3505 Epoch 92/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0882 - accuracy: 0.9847 - graph_loss: 0.3396 Epoch 93/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0807 - accuracy: 0.9879 - graph_loss: 0.3473 Epoch 94/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0820 - accuracy: 0.9861 - graph_loss: 0.3367 Epoch 95/100 17/17 [==============================] - 0s 9ms/step - loss: 0.0864 - accuracy: 0.9838 - graph_loss: 0.3353 Epoch 96/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0786 - accuracy: 0.9889 - graph_loss: 0.3392 Epoch 97/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0735 - accuracy: 0.9912 - graph_loss: 0.3443 Epoch 98/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0861 - accuracy: 0.9842 - graph_loss: 0.3381 Epoch 99/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0850 - accuracy: 0.9833 - graph_loss: 0.3376 Epoch 100/100 17/17 [==============================] - 0s 10ms/step - loss: 0.0841 - accuracy: 0.9879 - graph_loss: 0.3510 <tensorflow.python.keras.callbacks.History at 0x7fc3d853ce10>
Evaluar el modelo MLP con regularización de gráficos
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 6ms/step - loss: 1.2475 - accuracy: 0.8192 Eval accuracy for MLP + graph regularization : 0.8191681504249573 Eval loss for MLP + graph regularization : 1.2474583387374878
La precisión del modelo de gráfico regularizado es aproximadamente un 2-3% mayor que la del modelo base ( base_model ).
Conclusión
Hemos demostrado el uso de la regularización de gráficos para la clasificación de documentos en un gráfico de citas naturales (Cora) utilizando el marco de aprendizaje estructurado neuronal (NSL). Nuestro tutorial avanzado implica sintetizar gráficos basados en incrustaciones de muestra antes de entrenar una red neuronal con regularización de gráficos. Este enfoque es útil si la entrada no contiene un gráfico explícito.
Alentamos a los usuarios a experimentar más variando la cantidad de supervisión y probando diferentes arquitecturas neuronales para la regularización de gráficos.