עזרה להגן על שונית המחסום הגדולה עם TensorFlow על Kaggle הצטרפו אתגר

חקר הטמעות סיבוב TF-Hub CORD-19

הצג באתר TensorFlow.org הפעל בגוגל קולאב הצג ב-GitHub הורד מחברת ראה דגם TF Hub

טקסט סיבוב טבורי 19 הטבעת מודול מ TF-Hub ( https://tfhub.dev/tensorflow/cord-19/swivel-128d/1 ) נבנה חוקרת תמיכה בניתוח טקסט בשפות טבעית הקשורות COVID-19. שיבוצים אלה אומנו על כותרים, מחברים, תקצירים, טקסטים הגוף, וכותרות ההתייחסות של מאמרים במערך טבורי 19 .

בקולאב זה נעשה:

  • נתח מילים דומות מבחינה סמנטית במרחב ההטמעה
  • אימון מסווג במערך הנתונים של SciCite באמצעות הטמעות CORD-19

להכין

import functools
import itertools
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd

import tensorflow.compat.v1 as tf
tf.disable_eager_execution()
tf.logging.set_verbosity('ERROR')

import tensorflow_datasets as tfds
import tensorflow_hub as hub

try:
  from google.colab import data_table
  def display_df(df):
    return data_table.DataTable(df, include_index=False)
except ModuleNotFoundError:
  # If google-colab is not available, just display the raw DataFrame
  def display_df(df):
    return df

נתח את ההטבעות

נתחיל בניתוח ההטבעה על ידי חישוב ושרטוט מטריצת מתאם בין מונחים שונים. אם ההטמעה למדה ללכוד בהצלחה את המשמעות של מילים שונות, וקטורי הטבעה של מילים דומות מבחינה סמנטית צריכים להיות קרובים זה לזה. בואו נסתכל על כמה מונחים הקשורים ל-COVID-19.

# Use the inner product between two embedding vectors as the similarity measure
def plot_correlation(labels, features):
  corr = np.inner(features, features)
  corr /= np.max(corr)
  sns.heatmap(corr, xticklabels=labels, yticklabels=labels)


with tf.Graph().as_default():
  # Load the module
  query_input = tf.placeholder(tf.string)
  module = hub.Module('https://tfhub.dev/tensorflow/cord-19/swivel-128d/1')
  embeddings = module(query_input)

  with tf.train.MonitoredTrainingSession() as sess:

    # Generate embeddings for some terms
    queries = [
        # Related viruses
        "coronavirus", "SARS", "MERS",
        # Regions
        "Italy", "Spain", "Europe",
        # Symptoms
        "cough", "fever", "throat"
    ]

    features = sess.run(embeddings, feed_dict={query_input: queries})
    plot_correlation(queries, features)
2021-11-05 11:36:25.521420: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.

png

אנו יכולים לראות שההטבעה תפסה בהצלחה את המשמעות של המונחים השונים. כל מילה דומה למילים האחרות של האשכול שלה (כלומר "קורונה וירוס" מתאם מאוד עם "SARS" ו-"MERS"), בעוד שהן שונות ממונחים של אשכולות אחרים (כלומר הדמיון בין "SARS" ו"ספרד" הוא קרוב ל-0).

כעת נראה כיצד אנו יכולים להשתמש בהטמעות אלה כדי לפתור משימה ספציפית.

SciCite: סיווג כוונת ציטוט

סעיף זה מראה כיצד ניתן להשתמש בהטמעה עבור משימות במורד הזרם כגון סיווג טקסט. אנו נשתמש במערך SciCite מן TensorFlow מערכי נתונים כדי דבר הציטוט לסווג מאמרים אקדמיים. בהינתן משפט עם ציטוט מעבודה אקדמית, סווגו אם הכוונה העיקרית של הציטוט היא כמידע רקע, שימוש בשיטות או השוואת תוצאות.

הגדר את מערך הנתונים מ-TFDS

בואו נסתכל על כמה דוגמאות מתויגות מתוך ערכת ההדרכה

הכשרת מסווג כוונת ציטון

נצטרך להכשיר מסווג על בסיס נתון SciCite באמצעות הערכה. בואו נגדיר את input_fns כדי לקרוא את מערך הנתונים לתוך המודל

def preprocessed_input_fn(for_eval):
  data = THE_DATASET.get_data(for_eval=for_eval)
  data = data.map(THE_DATASET.example_fn, num_parallel_calls=1)
  return data


def input_fn_train(params):
  data = preprocessed_input_fn(for_eval=False)
  data = data.repeat(None)
  data = data.shuffle(1024)
  data = data.batch(batch_size=params['batch_size'])
  return data


def input_fn_eval(params):
  data = preprocessed_input_fn(for_eval=True)
  data = data.repeat(1)
  data = data.batch(batch_size=params['batch_size'])
  return data


def input_fn_predict(params):
  data = preprocessed_input_fn(for_eval=True)
  data = data.batch(batch_size=params['batch_size'])
  return data

בואו נבנה מודל שמשתמש בהטמעות CORD-19 עם שכבת סיווג למעלה.

def model_fn(features, labels, mode, params):
  # Embed the text
  embed = hub.Module(params['module_name'], trainable=params['trainable_module'])
  embeddings = embed(features['feature'])

  # Add a linear layer on top
  logits = tf.layers.dense(
      embeddings, units=THE_DATASET.num_classes(), activation=None)
  predictions = tf.argmax(input=logits, axis=1)

  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(
        mode=mode,
        predictions={
            'logits': logits,
            'predictions': predictions,
            'features': features['feature'],
            'labels': features['label']
        })

  # Set up a multi-class classification head
  loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits)
  loss = tf.reduce_mean(loss)

  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=params['learning_rate'])
    train_op = optimizer.minimize(loss, global_step=tf.train.get_or_create_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

  elif mode == tf.estimator.ModeKeys.EVAL:
    accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
    precision = tf.metrics.precision(labels=labels, predictions=predictions)
    recall = tf.metrics.recall(labels=labels, predictions=predictions)

    return tf.estimator.EstimatorSpec(
        mode=mode,
        loss=loss,
        eval_metric_ops={
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
        })

היפרפרמטרים

אימון והערכת המודל

בואו נאמן ונעריך את המודל כדי לראות את הביצועים במשימת SciCite

estimator = tf.estimator.Estimator(functools.partial(model_fn, params=params))
metrics = []

for step in range(0, STEPS, EVAL_EVERY):
  estimator.train(input_fn=functools.partial(input_fn_train, params=params), steps=EVAL_EVERY)
  step_metrics = estimator.evaluate(input_fn=functools.partial(input_fn_eval, params=params))
  print('Global step {}: loss {:.3f}, accuracy {:.3f}'.format(step, step_metrics['loss'], step_metrics['accuracy']))
  metrics.append(step_metrics)
2021-11-05 11:36:35.089196: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/ipykernel_launcher.py:8: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.
  
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/legacy_tf_layers/core.py:255: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
  return layer.apply(inputs)
2021-11-05 11:36:37.257679: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 0: loss 0.795, accuracy 0.683
2021-11-05 11:36:39.963864: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:42.567978: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 200: loss 0.720, accuracy 0.725
2021-11-05 11:36:44.412196: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:46.167367: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 400: loss 0.685, accuracy 0.735
2021-11-05 11:36:47.454541: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:49.859524: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 600: loss 0.657, accuracy 0.743
2021-11-05 11:36:51.159394: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:52.973479: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 800: loss 0.628, accuracy 0.766
2021-11-05 11:36:54.272092: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:56.197500: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 1000: loss 0.612, accuracy 0.771
2021-11-05 11:36:57.712701: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:36:59.448515: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 1200: loss 0.597, accuracy 0.776
2021-11-05 11:37:00.731476: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:02.656841: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 1400: loss 0.590, accuracy 0.779
2021-11-05 11:37:03.997415: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:05.749426: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 1600: loss 0.590, accuracy 0.779
2021-11-05 11:37:07.015652: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:08.900851: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 1800: loss 0.578, accuracy 0.779
2021-11-05 11:37:10.373800: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:12.102286: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 2000: loss 0.587, accuracy 0.773
2021-11-05 11:37:13.767595: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:15.731627: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 2200: loss 0.573, accuracy 0.785
2021-11-05 11:37:17.022574: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:18.746940: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 2400: loss 0.566, accuracy 0.785
2021-11-05 11:37:20.026853: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:21.980533: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 2600: loss 0.575, accuracy 0.775
2021-11-05 11:37:23.273076: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:25.039058: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 2800: loss 0.563, accuracy 0.782
2021-11-05 11:37:26.531677: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:28.482071: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 3000: loss 0.566, accuracy 0.783
2021-11-05 11:37:29.764582: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:31.474578: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 3200: loss 0.560, accuracy 0.784
2021-11-05 11:37:32.745235: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:34.614998: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 3400: loss 0.561, accuracy 0.781
2021-11-05 11:37:35.899823: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:37.566025: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 3600: loss 0.551, accuracy 0.789
2021-11-05 11:37:39.015831: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:40.902011: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 3800: loss 0.552, accuracy 0.783
2021-11-05 11:37:42.175585: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:43.887723: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 4000: loss 0.560, accuracy 0.779
2021-11-05 11:37:45.190449: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:47.072682: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 4200: loss 0.547, accuracy 0.790
2021-11-05 11:37:48.363401: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:50.068385: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 4400: loss 0.558, accuracy 0.781
2021-11-05 11:37:51.357653: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:53.266687: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 4600: loss 0.548, accuracy 0.787
2021-11-05 11:37:54.746584: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:56.482845: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 4800: loss 0.541, accuracy 0.792
2021-11-05 11:37:57.753726: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:37:59.675499: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 5000: loss 0.546, accuracy 0.784
2021-11-05 11:38:00.956026: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:02.706523: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 5200: loss 0.539, accuracy 0.790
2021-11-05 11:38:03.991646: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:05.864592: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 5400: loss 0.540, accuracy 0.788
2021-11-05 11:38:07.325910: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:09.053490: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 5600: loss 0.544, accuracy 0.785
2021-11-05 11:38:10.336937: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:12.242602: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 5800: loss 0.539, accuracy 0.790
2021-11-05 11:38:13.523562: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:15.234561: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 6000: loss 0.544, accuracy 0.788
2021-11-05 11:38:16.496935: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:18.398152: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 6200: loss 0.536, accuracy 0.789
2021-11-05 11:38:19.665205: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:21.576480: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 6400: loss 0.537, accuracy 0.788
2021-11-05 11:38:22.862922: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:24.759211: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 6600: loss 0.544, accuracy 0.790
2021-11-05 11:38:26.042820: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:27.790787: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 6800: loss 0.539, accuracy 0.784
2021-11-05 11:38:29.061025: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:30.972826: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 7000: loss 0.539, accuracy 0.788
2021-11-05 11:38:32.280235: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:34.021577: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 7200: loss 0.536, accuracy 0.784
2021-11-05 11:38:35.536367: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:37.468553: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 7400: loss 0.534, accuracy 0.785
2021-11-05 11:38:38.732636: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:40.459254: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 7600: loss 0.535, accuracy 0.784
2021-11-05 11:38:41.727159: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
2021-11-05 11:38:43.631400: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
Global step 7800: loss 0.539, accuracy 0.788
global_steps = [x['global_step'] for x in metrics]
fig, axes = plt.subplots(ncols=2, figsize=(20,8))

for axes_index, metric_names in enumerate([['accuracy', 'precision', 'recall'],
                                            ['loss']]):
  for metric_name in metric_names:
    axes[axes_index].plot(global_steps, [x[metric_name] for x in metrics], label=metric_name)
  axes[axes_index].legend()
  axes[axes_index].set_xlabel("Global Step")

png

אנו יכולים לראות שהאובדן פוחת במהירות בעוד במיוחד הדיוק עולה במהירות. בואו נתווה כמה דוגמאות כדי לבדוק איך התחזית קשורה לתוויות האמיתיות:

predictions = estimator.predict(functools.partial(input_fn_predict, params))
first_10_predictions = list(itertools.islice(predictions, 10))

display_df(
  pd.DataFrame({
      TEXT_FEATURE_NAME: [pred['features'].decode('utf8') for pred in first_10_predictions],
      LABEL_NAME: [THE_DATASET.class_names()[pred['labels']] for pred in first_10_predictions],
      'prediction': [THE_DATASET.class_names()[pred['predictions']] for pred in first_10_predictions]
  }))
2021-11-05 11:38:45.219327: W tensorflow/core/common_runtime/graph_constructor.cc:1511] Importing a graph with a lower producer version 27 into an existing graph with producer version 898. Shape inference will have run different parts of the graph with different producer versions.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/ipykernel_launcher.py:8: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.

אנו יכולים לראות שלמדגם אקראי זה, המודל מנבא את התווית הנכונה רוב הפעמים, מה שמצביע על כך שהוא יכול להטביע משפטים מדעיים די טוב.

מה הלאה?

כעת, לאחר שלמדת לדעת קצת יותר על הטבעות CORD-19 Swivel מ-TF-Hub, אנו ממליצים לך להשתתף בתחרות CORD-19 Kaggle כדי לתרום להשגת תובנות מדעיות מטקסטים אקדמיים הקשורים ל-COVID-19.