Doğal grafikler kullanarak belge sınıflandırması için grafik düzenleme

TensorFlow.org'da görüntüleyin Google Colab'de çalıştırın Kaynağı GitHub'da görüntüleyin

Genel Bakış

Grafik düzenlenmesi, daha geniş Nöral Grafik Öğrenme paradigması altında özel bir tekniktir ( Bui ve diğerleri, 2018 ). Temel fikir, hem etiketli hem de etiketlenmemiş verileri kullanan, grafikle düzenlenmiş bir hedefle sinir ağı modellerini eğitmektir.

Bu eğitimde, doğal (organik) bir grafik oluşturan belgeleri sınıflandırmak için grafik düzenleyicinin kullanımını inceleyeceğiz.

Sinirsel Yapılandırılmış Öğrenme (NSL) çerçevesini kullanarak grafiğe göre düzenlenmiş bir model oluşturmanın genel tarifi aşağıdaki gibidir:

  1. Giriş grafiğinden ve örnek özelliklerden eğitim verileri oluşturun. Grafikteki düğümler örneklere karşılık gelir ve grafikteki kenarlar örnek çiftleri arasındaki benzerliğe karşılık gelir. Elde edilen eğitim verileri, orijinal düğüm özelliklerine ek olarak komşu özellikleri de içerecektir.
  2. Keras sıralı, işlevsel veya alt sınıf API'sini kullanarak temel model olarak bir sinir ağı oluşturun.
  3. Yeni bir grafik Keras modeli oluşturmak için temel modeli NSL çerçevesi tarafından sağlanan GraphRegularization sarmalayıcı sınıfıyla sarın. Bu yeni model, eğitim hedefinde düzenlilik terimi olarak bir grafik düzenleme kaybı içerecektir.
  4. Grafik Keras modelini eğitin ve değerlendirin.

Kurulum

Sinirsel Yapılandırılmış Öğrenme paketini yükleyin.

pip install --quiet neural-structured-learning

Bağımlılıklar ve ithalatlar

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

Cora veri kümesi

Cora veri kümesi , düğümlerin makine öğrenimi kağıtlarını ve kenarların kağıt çiftleri arasındaki alıntıları temsil ettiği bir alıntı grafiğidir. İlgili görev, amacın her bir kağıdı 7 kategoriden birine ayırmak olduğu belge sınıflandırmasıdır. Yani bu 7 sınıflı çok sınıflı bir sınıflandırma problemidir.

Grafik

Orijinal grafik yönlendirilir. Bununla birlikte, bu örneğin amacı doğrultusunda, bu grafiğin yönsüz versiyonunu ele alıyoruz. Öyleyse, eğer A makalesi B kağıdına atıfta bulunuyorsa, B kağıdının da A'dan alıntı yaptığını düşünüyoruz. Bu mutlaka doğru olmasa da, bu örnekte alıntıları benzerlik için bir vekil olarak görüyoruz ki bu genellikle değişmeli bir özelliktir.

Özellikleri

Girişteki her kağıt etkili bir şekilde 2 özellik içerir:

  1. Kelimeler : Kağıttaki metnin yoğun, çok sıcak bir kelime torbası temsili. Cora veri setinin kelime dağarcığı 1433 benzersiz kelime içerir. Dolayısıyla, bu özelliğin uzunluğu 1433'tür ve 'i' konumundaki değer 0 / 1'dir ve bu, söz konusu makalede kelime dağarcığındaki 'i' kelimesinin var olup olmadığını gösterir.

  2. Etiket : Kağıdın sınıf kimliğini (kategori) temsil eden tek bir tam sayı.

Cora veri kümesini indirin

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

Cora verilerini NSL formatına dönüştürün

Cora veri kümesini önceden işlemek ve onu Sinirsel Yapılandırılmış Öğrenmenin gerektirdiği biçime dönüştürmek için, NSL github havuzunda bulunan 'preprocess_cora_dataset.py' komut dosyasını çalıştıracağız. Bu komut dosyası şunları yapar:

  1. Orijinal düğüm özelliklerini ve grafiği kullanarak komşu unsurları oluşturun.
  2. tf.train.Example örnekleri içeren tren ve test verileri bölümleri oluşturun.
  3. Ortaya çıkan tren ve test verilerini TFRecord biçiminde devam 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.

Global değişkenler

Tren ve test verilerine giden dosya yolları, yukarıdaki 'preprocess_cora_dataset.py' komut dosyasını çağırmak için kullanılan komut satırı bayrak değerlerine dayanır.

### 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'

Hiperparametreler

Eğitim ve değerlendirme için kullanılan çeşitli hiperparametreleri ve sabitleri dahil etmek için bir HParams örneğini kullanacağız. Her birini aşağıda kısaca açıklıyoruz:

  • sınıf_sayısı : Toplam 7 farklı sınıf vardır

  • max_seq_length : Bu, kelime dağarcığının boyutudur ve girişteki tüm örnekler yoğun, birden çok kelime içeren, kelime torbası temsiline sahiptir. Başka bir deyişle, bir kelime için 1 değeri, kelimenin girişte mevcut olduğunu ve 0 değeri olmadığını gösterir.

  • mesafe_türü : Bu, numuneyi komşularıyla düzenlemek için kullanılan mesafe ölçüsüdür.

  • graph_regularization_multiplier : Bu, genel kayıp fonksiyonundaki grafik düzenleme teriminin göreceli ağırlığını kontrol eder.

  • num_neighbors : Grafik düzenlenmesi için kullanılan komşuların sayısı. Bu değer, preprocess_cora_dataset.py çalıştırılırken yukarıda kullanılan max_nbrs komut satırı bağımsız değişkeninden küçük veya ona eşit olmalıdır.

  • num_fc_units : Sinir ağımızdaki tam olarak bağlı katmanların sayısı.

  • train_epochs : Eğitim dönemi sayısı.

  • batch_size : Eğitim ve değerlendirme için kullanılan parti boyutu.

  • dropout_rate : Tamamen bağlı her katmanın ardından bırakma oranını kontrol eder

  • eval_steps : Değerlendirmenin tamamlandığı varsayılmadan önce işlenecek parti sayısı. None ayarlanırsa, test setindeki tüm örnekler değerlendirilir.

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()

Yükleme treni ve test verileri

Bu defterde daha önce açıklandığı gibi, giriş eğitimi ve test verileri 'preprocess_cora_dataset.py' tarafından oluşturulmuştur. Bunları ikitf.data.Dataset nesnesinetf.data.Dataset - biri tren, diğeri test için.

Modelimizin girdi katmanında, her örnekten yalnızca 'sözcükler' ve 'etiket' özelliklerini değil, aynı zamanda hparams.num_neighbors değerine dayalı olarak karşılık gelen komşu özellikleri de hparams.num_neighbors . hparams.num_neighbors daha az komşusu olan hparams.num_neighbors , var olmayan bu komşu özellikleri için sahte değerler atanacaktır.

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)

İçeriğine bakmak için tren veri kümesine göz atalım.

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)

İçeriğine bakmak için test veri kümesine göz atalım.

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)

Model tanımı

Grafik düzenleyiciliğin kullanımını göstermek için, önce bu problem için bir temel model oluşturuyoruz. 2 gizli katman ve aralarında bırakma bulunan basit bir ileri beslemeli sinir ağı kullanacağız. Sıralı, işlevsel ve alt sınıf tf.Keras çerçevesi tarafından desteklenen tüm model türlerini kullanarak temel modelin oluşturulmasını tf.Keras .

Sıralı temel model

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

Fonksiyonel temel model

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

Alt sınıf temel modeli

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()

Temel modeller oluştur

# 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
_________________________________________________________________

Tren temel MLP modeli

# 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>

Temel MLP modelini değerlendirin

# 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

MLP modelini grafik düzenleme ile eğitin

Mevcut bir tf.Keras.Model kayıp terimine grafik düzenlileştirmeyi dahil etmek sadece birkaç satır kod gerektirir. Temel model, kaybı grafik düzenlileştirmeyi içeren yeni bir tf.Keras altsınıf modeli oluşturmak için sarılır.

Grafik düzenlemenin artan faydasını değerlendirmek için, yeni bir temel model örneği oluşturacağız. Bunun nedeni, base_model halihazırda birkaç yineleme için eğitilmiş olmasıdır ve bu eğitimli modeli grafiğe göre düzenlenmiş bir model oluşturmak için yeniden kullanmak, base_model için adil bir karşılaştırma 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>

MLP modelini grafik düzenleme ile değerlendirin

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

Grafiğe göre düzenlenmiş modelin doğruluğu, temel base_model (temel model) yaklaşık% 2-3 daha yüksektir.

Sonuç

Nöral Yapılandırılmış Öğrenme (NSL) çerçevesini kullanarak doğal bir atıf grafiğindeki (Cora) belge sınıflandırması için grafik düzenlemesinin kullanımını gösterdik. Gelişmiş eğitimimiz, grafik düzenleme ile bir sinir ağını eğitmeden önce örnek gömmelere dayalı grafikleri sentezlemeyi içerir. Bu yaklaşım, girdi açık bir grafik içermiyorsa kullanışlıdır.

Kullanıcıları, denetim miktarını değiştirmenin yanı sıra grafik düzenleme için farklı sinir mimarileri deneyerek daha fazla deneme yapmaya teşvik ediyoruz.