TensorFlow.org'da görüntüleyin

Google Colab'da çalıştırın

GitHub'da görüntüle

Not defterini indir

TF Hub modeline bakın

TF-Hub yeniden kullanılabilir kaynaklarında paketlenmiş pay makine öğrenme uzmanlığı, özellikle önceden eğitilmiş modülleri bir platformdur. Bu öğreticide, makul bir temel doğrulukla basit bir duygu sınıflandırıcıyı eğitmek için bir TF-Hub metin gömme modülü kullanacağız. Daha sonra tahminleri Kaggle'a göndereceğiz.

TF-Hub ve hassasiyeti geliştirmek için daha ileri adımlar metin sınıflandırma ile ilgili daha ayrıntılı bir eğitici için bir göz atın TF-Hub ile Metin sınıflandırma .

Kurmak

pip install -q kaggle

import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import zipfile

from sklearn import model_selection

Bu öğretici Kaggle bir veri seti kullanarak olacağından, bunun gerektirir bir API Jetonu oluşturarak sizin Kaggle hesabının ve CoLab ortamına yükleyerek.

import os
import pathlib

# Upload the API token.
def get_kaggle():
  try:
    import kaggle
    return kaggle
  except OSError:
    pass

  token_file = pathlib.Path("~/.kaggle/kaggle.json").expanduser()
  token_file.parent.mkdir(exist_ok=True, parents=True)

  try:
    from google.colab import files
  except ImportError:
    raise ValueError("Could not find kaggle token.")

  uploaded = files.upload()
  token_content = uploaded.get('kaggle.json', None)
  if token_content:
    token_file.write_bytes(token_content)
    token_file.chmod(0o600)
  else:
    raise ValueError('Need a file named "kaggle.json"')

  import kaggle
  return kaggle


kaggle = get_kaggle()

Başlarken

Veri

Biz çözmeye çalışacağı Film Eleştirileri üzerine Duygu Analizi Kaggle gelen görev. Veri seti, Rotten Tomatoes film incelemelerinin sözdizimsel alt sözcüklerinden oluşur. Görev 1'den 5'e kadar ölçekte negatif veya pozitif olarak ifadeler nitelemektedir.

Sen gerekir rekabet kurallarını kabul verileri indirmek için API kullanmadan önce.

SENTIMENT_LABELS = [
    "negative", "somewhat negative", "neutral", "somewhat positive", "positive"
]

# Add a column with readable values representing the sentiment.
def add_readable_labels_column(df, sentiment_value_column):
  df["SentimentLabel"] = df[sentiment_value_column].replace(
      range(5), SENTIMENT_LABELS)

# Download data from Kaggle and create a DataFrame.
def load_data_from_zip(path):
  with zipfile.ZipFile(path, "r") as zip_ref:
    name = zip_ref.namelist()[0]
    with zip_ref.open(name) as zf:
      return pd.read_csv(zf, sep="\t", index_col=0)


# The data does not come with a validation set so we'll create one from the
# training set.
def get_data(competition, train_file, test_file, validation_set_ratio=0.1):
  data_path = pathlib.Path("data")
  kaggle.api.competition_download_files(competition, data_path)
  competition_path = (data_path/competition)
  competition_path.mkdir(exist_ok=True, parents=True)
  competition_zip_path = competition_path.with_suffix(".zip")

  with zipfile.ZipFile(competition_zip_path, "r") as zip_ref:
    zip_ref.extractall(competition_path)

  train_df = load_data_from_zip(competition_path/train_file)
  test_df = load_data_from_zip(competition_path/test_file)

  # Add a human readable label.
  add_readable_labels_column(train_df, "Sentiment")

  # We split by sentence ids, because we don't want to have phrases belonging
  # to the same sentence in both training and validation set.
  train_indices, validation_indices = model_selection.train_test_split(
      np.unique(train_df["SentenceId"]),
      test_size=validation_set_ratio,
      random_state=0)

  validation_df = train_df[train_df["SentenceId"].isin(validation_indices)]
  train_df = train_df[train_df["SentenceId"].isin(train_indices)]
  print("Split the training data into %d training and %d validation examples." %
        (len(train_df), len(validation_df)))

  return train_df, validation_df, test_df


train_df, validation_df, test_df = get_data(
    "sentiment-analysis-on-movie-reviews",
    "train.tsv.zip", "test.tsv.zip")

Split the training data into 140315 training and 15745 validation examples.

train_df.head(20)

Model Eğitimi

class MyModel(tf.keras.Model):
  def __init__(self, hub_url):
    super().__init__()
    self.hub_url = hub_url
    self.embed = hub.load(self.hub_url).signatures['default']
    self.sequential = tf.keras.Sequential([
      tf.keras.layers.Dense(500),
      tf.keras.layers.Dense(100),
      tf.keras.layers.Dense(5),
    ])

  def call(self, inputs):
    phrases = inputs['Phrase'][:,0]
    embedding = 5*self.embed(phrases)['default']
    return self.sequential(embedding)

  def get_config(self):
    return {"hub_url":self.hub_url}

model = MyModel("https://tfhub.dev/google/nnlm-en-dim128/1")
model.compile(
    loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=tf.optimizers.Adam(), 
    metrics = [tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")])

history = model.fit(x=dict(train_df), y=train_df['Sentiment'],
          validation_data=(dict(validation_df), validation_df['Sentiment']),
          epochs = 25)

Epoch 1/25
4385/4385 [==============================] - 16s 3ms/step - loss: 1.0237 - accuracy: 0.5869 - val_loss: 1.0023 - val_accuracy: 0.5870
Epoch 2/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9995 - accuracy: 0.5941 - val_loss: 0.9903 - val_accuracy: 0.5952
Epoch 3/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9946 - accuracy: 0.5967 - val_loss: 0.9811 - val_accuracy: 0.6011
Epoch 4/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9924 - accuracy: 0.5971 - val_loss: 0.9851 - val_accuracy: 0.5935
Epoch 5/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9912 - accuracy: 0.5988 - val_loss: 0.9896 - val_accuracy: 0.5934
Epoch 6/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9896 - accuracy: 0.5984 - val_loss: 0.9810 - val_accuracy: 0.5936
Epoch 7/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9892 - accuracy: 0.5978 - val_loss: 0.9845 - val_accuracy: 0.5994
Epoch 8/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9889 - accuracy: 0.5996 - val_loss: 0.9772 - val_accuracy: 0.6015
Epoch 9/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9880 - accuracy: 0.5992 - val_loss: 0.9798 - val_accuracy: 0.5991
Epoch 10/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9879 - accuracy: 0.6002 - val_loss: 0.9869 - val_accuracy: 0.5935
Epoch 11/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9878 - accuracy: 0.5998 - val_loss: 0.9790 - val_accuracy: 0.5985
Epoch 12/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9871 - accuracy: 0.5999 - val_loss: 0.9845 - val_accuracy: 0.5964
Epoch 13/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9871 - accuracy: 0.6001 - val_loss: 0.9800 - val_accuracy: 0.5947
Epoch 14/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9873 - accuracy: 0.6001 - val_loss: 0.9810 - val_accuracy: 0.5934
Epoch 15/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9865 - accuracy: 0.5988 - val_loss: 0.9824 - val_accuracy: 0.5898
Epoch 16/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9865 - accuracy: 0.5993 - val_loss: 0.9779 - val_accuracy: 0.5974
Epoch 17/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9866 - accuracy: 0.5991 - val_loss: 0.9785 - val_accuracy: 0.5972
Epoch 18/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9863 - accuracy: 0.6001 - val_loss: 0.9803 - val_accuracy: 0.5991
Epoch 19/25
4385/4385 [==============================] - 16s 4ms/step - loss: 0.9863 - accuracy: 0.5996 - val_loss: 0.9773 - val_accuracy: 0.5957
Epoch 20/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9862 - accuracy: 0.5995 - val_loss: 0.9744 - val_accuracy: 0.6009
Epoch 21/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9861 - accuracy: 0.5997 - val_loss: 0.9787 - val_accuracy: 0.5968
Epoch 22/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9855 - accuracy: 0.5998 - val_loss: 0.9794 - val_accuracy: 0.5976
Epoch 23/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9861 - accuracy: 0.5998 - val_loss: 0.9778 - val_accuracy: 0.5966
Epoch 24/25
4385/4385 [==============================] - 15s 3ms/step - loss: 0.9860 - accuracy: 0.5999 - val_loss: 0.9831 - val_accuracy: 0.5912
Epoch 25/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9858 - accuracy: 0.5999 - val_loss: 0.9780 - val_accuracy: 0.5977

Tahmin

Doğrulama seti ve eğitim seti için tahminler çalıştırın.

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])

[<matplotlib.lines.Line2D at 0x7f62684da090>]

train_eval_result = model.evaluate(dict(train_df), train_df['Sentiment'])
validation_eval_result = model.evaluate(dict(validation_df), validation_df['Sentiment'])

print(f"Training set accuracy: {train_eval_result[1]}")
print(f"Validation set accuracy: {validation_eval_result[1]}")

4385/4385 [==============================] - 14s 3ms/step - loss: 0.9834 - accuracy: 0.6007
493/493 [==============================] - 1s 2ms/step - loss: 0.9780 - accuracy: 0.5977
Training set accuracy: 0.6006770730018616
Validation set accuracy: 0.5976500511169434

karışıklık matrisi

Bir başka ilginç istatistik, özellikle çok sınıflı sorunlar için vardır karışıklık matrisi . Karışıklık matrisi, doğru ve yanlış etiketlenmiş örneklerin oranının görselleştirilmesini sağlar. Sınıflandırıcımızın ne kadar önyargılı olduğunu ve etiketlerin dağılımının anlamlı olup olmadığını kolayca görebiliriz. İdeal olarak, tahminlerin en büyük kısmı köşegen boyunca dağıtılmalıdır.

predictions = model.predict(dict(validation_df))
predictions = tf.argmax(predictions, axis=-1)
predictions

<tf.Tensor: shape=(15745,), dtype=int64, numpy=array([1, 1, 2, ..., 2, 2, 2])>

cm = tf.math.confusion_matrix(validation_df['Sentiment'], predictions)
cm = cm/cm.numpy().sum(axis=1)[:, tf.newaxis]

sns.heatmap(
    cm, annot=True,
    xticklabels=SENTIMENT_LABELS,
    yticklabels=SENTIMENT_LABELS)
plt.xlabel("Predicted")
plt.ylabel("True")

Text(32.99999999999999, 0.5, 'True')

Aşağıdaki kodu bir kod hücresine yapıştırıp çalıştırarak tahminleri kolayca Kaggle'a gönderebiliriz:

test_predictions = model.predict(dict(test_df))
test_predictions = np.argmax(test_predictions, axis=-1)

result_df = test_df.copy()

result_df["Predictions"] = test_predictions

result_df.to_csv(
    "predictions.csv",
    columns=["Predictions"],
    header=["Sentiment"])
kaggle.api.competition_submit("predictions.csv", "Submitted from Colab",
                              "sentiment-analysis-on-movie-reviews")

Gönderdikten sonra, afiş kontrol nasıl yaptığını görmek için.