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تحميل دفتر

انظر نموذج TF Hub

TF-المحور هو منبر لتبادل الخبرات والتعلم الآلي وتعبئتها في موارد قابلة لإعادة الاستخدام، وحدات خصوصا قبل المدربين. في هذا البرنامج التعليمي ، سوف نستخدم وحدة تضمين نص TF-Hub لتدريب مصنف بسيط للمشاعر بدقة أساسية معقولة. ثم سنرسل التنبؤات إلى Kaggle.

لتعليمي أكثر تفصيلا على تصنيف النص مع TF-المحور واتخاذ مزيد من الخطوات لتحسين دقة، نلقي نظرة على تصنيف النص مع TF-المحور .

يثبت

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

وبما أن هذا البرنامج التعليمي سوف تستخدم مجموعة بيانات من Kaggle، فإنه يتطلب تهيئة API رمز لحساب Kaggle الخاص بك، وتحميلها على البيئة Colab.

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()

ابدء

البيانات

سنحاول حل تحليل المشاعر على الفيلم مراجعات مهمة من Kaggle. تتكون مجموعة البيانات من عبارات فرعية نحوية لمراجعات فيلم Rotten Tomatoes. وتتمثل المهمة في تسمية العبارات بأنها سلبية أو إيجابية على مقياس من 1 إلى 5.

يجب عليك قبول قواعد المنافسة قبل أن تتمكن من استخدام API لتحميل البيانات.

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)

تدريب نموذج

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

تنبؤ

قم بتشغيل التنبؤات لمجموعة التحقق ومجموعة التدريب.

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

الارتباك مصفوفة

آخر إحصائية مثيرة جدا للاهتمام، وخاصة بالنسبة للمشاكل multiclass، هو مصفوفة الارتباك . تسمح مصفوفة الارتباك بتصور نسبة الأمثلة المصنفة بشكل صحيح وغير صحيح. يمكننا أن نرى بسهولة إلى أي مدى يكون المصنف لدينا متحيزًا وما إذا كان توزيع الملصقات أمرًا منطقيًا. من الناحية المثالية ، يجب توزيع الجزء الأكبر من التنبؤات على طول القطر.

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')

يمكننا بسهولة إرسال التنبؤات مرة أخرى إلى Kaggle عن طريق لصق الكود التالي في خلية كود وتنفيذها:

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")

بعد تقديم، تحقق المتصدرين لنرى كيف فعلت.