Se usó la API de Cloud Translation para traducir esta página.

Regularización de gráficos para la clasificación de documentos utilizando gráficos naturales

Ver en TensorFlow.org

Ejecutar en Google Colab

Ver fuente en GitHub

Descargar cuaderno

Descripción general

Graph regularización es una técnica específica bajo el paradigma más amplio de Neural Graph Learning ( 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 de gráfico regularizado 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.
Crear una red neuronal como un modelo base con el Keras secuencial, o API subclase funcional.
Envolver el modelo de base con el GraphRegularization clase de contenedor, que es proporcionado por el marco NSL, para crear un nuevo gráfico Keras modelo. Este nuevo modelo incluirá un gráfico de pérdida de regularización como término de regularización en su objetivo de entrenamiento.
Capacitar y evaluar la gráfica Keras modelo.

Configuración

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:  2.8.0-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 datos de Cora

El conjunto de datos Cora es un gráfico citación donde los nodos representan papeles de aprendizaje automático y los bordes representan las citas entre pares de papeles. 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, para el propósito 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.

Características

Cada papel en la entrada contiene efectivamente 2 características:

Palabras: Una densa, múltiples caliente bolsa de representación-de-palabras del texto en el papel. El vocabulario del conjunto de datos de 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.
Label: Un solo entero que representa el identificador de clase (categoría) del papel.

Descarga el conjunto de datos de Cora

wget --quiet -P /tmp https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz
tar -C /tmp -xvzf /tmp/cora.tgz

cora/
cora/README
cora/cora.cites
cora/cora.content

Convierta los datos de Cora al formato NSL

Con el fin de procesar previamente el conjunto de datos Cora y convertirlo al formato requerido por el aprendizaje de los nervios estructurado, vamos a ejecutar el script 'preprocess_cora_dataset.py', que se incluye en el repositorio GitHub NSL. Este script hace lo siguiente:

Genere entidades vecinas utilizando las entidades de nodo originales y el gráfico.
Generar divisiones de tren y de datos de prueba que contienen tf.train.Example casos.
Persistir el tren resultante y los datos de prueba en el TFRecord formato.

!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.

Variables globales

Las rutas de los archivos a los datos del tren y la prueba se basan en los valores de los indicadores de línea de comandos que se utilizan para invocar el script 'preprocess_cora_dataset.py' arriba.

### 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

Vamos a utilizar una instancia de HParams para incluir varios hiperparámetros y constantes utilizadas para la formación 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 tiene un multi-caliente, la representación densa bolsa de palabras. 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 distancia métrica utilizada para regularizar la muestra con sus vecinos.
graph_regularization_multiplier: Esto controla el peso relativo de la expresión gráfica de regularización en la función general de pérdida.
num_neighbors: El número de vecinos usados para la regularización gráfico. Este valor tiene que ser menor o igual a los max_nbrs argumento que se utiliza cuando se ejecuta por encima de la línea de comandos preprocess_cora_dataset.py .
num_fc_units: El número de capas totalmente conectados en nuestra red neuronal.
train_epochs: El número de épocas de formación.
El tamaño del lote utilizado para la formación y evaluación: batch_size.
dropout_rate: controla la velocidad de deserción después de cada capa totalmente conectado
eval_steps: El número de lotes a proceso antes considerando la evaluación es completa. Si se establece en None , se evalúan todas las instancias en el 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 ha descrito anteriormente en este cuaderno, el banco de datos de entrada y las pruebas han sido creados por el 'preprocess_cora_dataset.py'. Vamos a cargarlos en dos tf.data.Dataset objetos - uno para el tren y uno para la prueba.

En la capa de entrada de nuestro modelo, vamos a extraer no sólo las 'palabras' y la 'etiqueta' características de cada muestra, pero también vecino correspondientes características en base a la hparams.num_neighbors valor. Instancias con un menor número de vecinos que hparams.num_neighbors serán asignados ficticia valores relativos a las características vecino que no existen.

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 ... 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)

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. Nos ilustran la creación del modelo de base con todos los tipos de modelos soportados por el tf.Keras marco - 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))
  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)(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)

    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=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>

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: 1.4192 - accuracy: 0.7939


Eval accuracy for  Base MLP model :  0.7938517332077026
Eval loss for  Base MLP model :  1.4192423820495605

Entrene el modelo MLP con regularización de gráficos

La incorporación de regularización gráfico en el término pérdida de una ya existente tf.Keras.Model requiere sólo unas pocas líneas de código. El modelo base se envuelve para crear una nueva tf.Keras subclases de modelo, cuya pérdida incluye regularización gráfico.

Para evaluar el beneficio incremental de la regularización de gráficos, crearemos una nueva instancia de modelo base. Esto se debe a base_model ya se ha entrenado durante unas pocas iteraciones, y la reutilización de este modelo entrenado para crear un modelo gráfico-regularizado 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=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>

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 5ms/step - loss: 0.8884 - accuracy: 0.7957


Eval accuracy for  MLP + graph regularization :  0.7956600189208984
Eval loss for  MLP + graph regularization :  0.8883611559867859

La exactitud del modelo gráfico-regularizado es de aproximadamente 2-3% más alto que el del modelo de 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 la síntesis de gráficos basados en la formación de incrustaciones de muestra antes de una red neuronal con regularización gráfico. 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.