Aiuto proteggere la Grande Barriera Corallina con tensorflow sul Kaggle Join Sfida

Esplorazione degli inserti girevoli CORD-19 TF-Hub

Visualizza su TensorFlow.org Esegui in Google Colab Visualizza su GitHub Scarica taccuino Vedi il modello del mozzo TF

Il modulo CORD-19 girevole testo incorporamento da TF-mozzo (https: //tfhub.dev/ tensorflow / cavo-19 / girevole-128d / 1) era costruito per supportare i ricercatori che analizzano testi in lingua naturale relativi a COVID-19. Questi incastri sono stati formati sui titoli, autori, abstract, testi per il corpo e titoli di riferimento di articoli della CORD-19 set di dati .

In questa collaborazione:

  • Analizza parole semanticamente simili nello spazio di incorporamento
  • Addestrare un classificatore sul set di dati SciCite utilizzando gli incorporamenti CORD-19

Impostare

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

Analizza gli incorporamenti

Iniziamo analizzando l'incorporamento calcolando e tracciando una matrice di correlazione tra termini diversi. Se l'incorporamento ha imparato a catturare con successo il significato di parole diverse, i vettori di inclusione di parole semanticamente simili dovrebbero essere vicini tra loro. Diamo un'occhiata ad alcuni termini relativi al 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

Possiamo vedere che l'incorporamento ha catturato con successo il significato dei diversi termini. Ogni parola è simile alle altre parole del suo cluster (cioè "coronavirus" è altamente correlato con "SARS" e "MERS"), mentre sono diversi dai termini di altri cluster (cioè la somiglianza tra "SARS" e "Spain" è vicino a 0).

Ora vediamo come possiamo usare questi incorporamenti per risolvere un compito specifico.

SciCite: Citation Intent Classification

Questa sezione mostra come è possibile utilizzare l'incorporamento per attività a valle come la classificazione del testo. Useremo il set di dati SciCite da tensorflow Dataset al intenti citazione classificano in pubblicazioni accademiche. Data una frase con una citazione da un documento accademico, classificare se l'intento principale della citazione è come informazioni di base, uso di metodi o confronto dei risultati.

Imposta il set di dati da TFDS

Diamo un'occhiata ad alcuni esempi etichettati dal training set

Addestrare un classificatore di intenti citato

Ci alleniamo un classificatore sul set di dati SciCite utilizzando uno stimatore. Impostiamo input_fns per leggere il set di dati nel modello

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

Costruiamo un modello che utilizzi gli incorporamenti CORD-19 con uno strato di classificazione in cima.

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,
        })

Iperparametri

Addestra e valuta il modello

Addestriamo e valutiamo il modello per vedere le prestazioni sull'attività 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

Possiamo vedere che la perdita diminuisce rapidamente mentre soprattutto la precisione aumenta rapidamente. Tracciamo alcuni esempi per verificare come la previsione si riferisce alle vere etichette:

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.

Possiamo vedere che per questo campione casuale, il modello prevede l'etichetta corretta la maggior parte delle volte, indicando che può incorporare frasi scientifiche abbastanza bene.

Qual è il prossimo?

Ora che hai avuto modo di conoscere un po' di più sugli inserti CORD-19 Swivel di TF-Hub, ti invitiamo a partecipare al concorso CORD-19 Kaggle per contribuire ad acquisire approfondimenti scientifici dai testi accademici relativi al COVID-19.