 Посмотреть на TensorFlow.org |  Запускаем в Google Colab |  Посмотреть на GitHub |  Скачать блокнот |  См. Модель TF Hub |
CORD-19 Поворотный текст встраивание модуль из TF-концентратор ( https://tfhub.dev/tensorflow/cord-19/swivel-128d/1 ) был построен для поддержки исследователей , анализирующих естественных языков текст , связанный с COVID-19. Эти вложения были подготовлены на названия, авторов, рефераты, тексты для тела, и справочные названия статей в CORD-19 набора данных .
В этом колабе мы:
- Анализировать семантически похожие слова в пространстве вложения
- Обучите классификатор на наборе данных SciCite, используя вложения CORD-19
Настраивать
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
Проанализируйте вложения
Давайте начнем с анализа вложения путем вычисления и построения корреляционной матрицы между различными терминами. Если встраивание научилось успешно улавливать значение разных слов, векторы встраивания семантически похожих слов должны быть близко друг к другу. Давайте посмотрим на некоторые термины, связанные с 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.
Мы видим, что встраивание успешно уловило значение различных терминов. Каждое слово похоже на другие слова своего кластера (то есть «коронавирус» сильно коррелирует с «SARS» и «MERS»), в то время как они отличаются от терминов других кластеров (то есть сходство между «SARS» и «Испания» составляет близко к 0).
Теперь посмотрим, как мы можем использовать эти вложения для решения конкретной задачи.
SciCite: классификация намерений цитирования
В этом разделе показано, как можно использовать встраивание для последующих задач, таких как классификация текста. Мы будем использовать набор данных SciCite из TensorFlow Datasets классифицировать цитирования намерений в научных работах. Учитывая предложение с цитатой из академической статьи, классифицируйте, является ли основная цель цитирования справочной информацией, использованием методов или сравнением результатов.
Настройте набор данных из TFDS
class Dataset:
"""Build a dataset from a TFDS dataset."""
def __init__(self, tfds_name, feature_name, label_name):
self.dataset_builder = tfds.builder(tfds_name)
self.dataset_builder.download_and_prepare()
self.feature_name = feature_name
self.label_name = label_name
def get_data(self, for_eval):
splits = THE_DATASET.dataset_builder.info.splits
if tfds.Split.TEST in splits:
split = tfds.Split.TEST if for_eval else tfds.Split.TRAIN
else:
SPLIT_PERCENT = 80
split = "train[{}%:]".format(SPLIT_PERCENT) if for_eval else "train[:{}%]".format(SPLIT_PERCENT)
return self.dataset_builder.as_dataset(split=split)
def num_classes(self):
return self.dataset_builder.info.features[self.label_name].num_classes
def class_names(self):
return self.dataset_builder.info.features[self.label_name].names
def preprocess_fn(self, data):
return data[self.feature_name], data[self.label_name]
def example_fn(self, data):
feature, label = self.preprocess_fn(data)
return {'feature': feature, 'label': label}, label
def get_example_data(dataset, num_examples, **data_kw):
"""Show example data"""
with tf.Session() as sess:
batched_ds = dataset.get_data(**data_kw).take(num_examples).map(dataset.preprocess_fn).batch(num_examples)
it = tf.data.make_one_shot_iterator(batched_ds).get_next()
data = sess.run(it)
return data
TFDS_NAME = 'scicite'
TEXT_FEATURE_NAME = 'string'
LABEL_NAME = 'label'
THE_DATASET = Dataset(TFDS_NAME, TEXT_FEATURE_NAME, LABEL_NAME)
Давайте посмотрим на несколько помеченных примеров из обучающей выборки.
NUM_EXAMPLES = 20
data = get_example_data(THE_DATASET, NUM_EXAMPLES, for_eval=False)
display_df(
pd.DataFrame({
TEXT_FEATURE_NAME: [ex.decode('utf8') for ex in data[0]],
LABEL_NAME: [THE_DATASET.class_names()[x] for x in data[1]]
}))
Обучение классификатора намерений citaton
Мы обучим классификатор на SciCite набора данных с использованием оценщика. Давайте настроим input_fns для чтения набора данных в модель
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
Давайте построим модель, которая использует вложения CORD-19 с классификационным слоем наверху.
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,
})
Гиперпараметры
EMBEDDING = 'https://tfhub.dev/tensorflow/cord-19/swivel-128d/1'
TRAINABLE_MODULE = False
STEPS = 8000
EVAL_EVERY = 200
BATCH_SIZE = 10
LEARNING_RATE = 0.01
params = {
'batch_size': BATCH_SIZE,
'learning_rate': LEARNING_RATE,
'module_name': EMBEDDING,
'trainable_module': TRAINABLE_MODULE
}
Обучите и оцените модель
Давайте обучим и оценим модель, чтобы увидеть производительность задачи 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")
Мы видим, что потери быстро уменьшаются, в то время как точность особенно быстро увеличивается. Давайте изобразим несколько примеров, чтобы проверить, как прогноз соотносится с истинными метками:
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.
Мы видим, что для этой случайной выборки модель в большинстве случаев предсказывает правильную метку, что указывает на то, что она может довольно хорошо встраивать научные предложения.
Что дальше?
Теперь, когда вы узнали немного больше о встраиваниях CORD-19 Swivel от TF-Hub, мы призываем вас принять участие в конкурсе CORD-19 Kaggle, чтобы внести свой вклад в получение научных идей из академических текстов, связанных с COVID-19.
- Участие в CORD-19 Kaggle Вызова
- Узнайте больше о COVID-19 Открытый исследовательский Dataset (CORD-19)
- Обратитесь к документации и больше о вложениях TF-концентратор на https://tfhub.dev/tensorflow/cord-19/swivel-128d/1
- Исследовать вложение пространства CORD-19 с TensorFlow Встраивание Projector