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TF-Hub 上的 CORD-19 Swivel 文本嵌入向量模块 (https://tfhub.dev/tensorflow/cord-19/swivel-128d/1) 旨在支持研究员分析与 COVID-19 相关的自然语言文本。这些嵌入向量针对 CORD-19 数据集中文章的标题、作者、摘要、正文文本和参考文献标题进行了训练。
在此 Colab 中，我们将进行以下操作：
- 分析嵌入向量空间中语义相似的单词
- 使用 CORD-19 嵌入向量在 SciCite 数据集上训练分类器
设置
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
分析嵌入向量
首先，我们通过计算和绘制不同术语之间的相关矩阵来分析嵌入向量。如果嵌入向量学会了成功捕获不同单词的含义，则语义相似的单词的嵌入向量应相互靠近。我们来看一些与 COVID-19 相关的术语。
# 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)
2022-06-03 15:57:19.859817: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions.
可以看到，嵌入向量成功捕获了不同术语的含义。每个单词都与其所在簇的其他单词相似（即“coronavirus”与“SARS”和“MERS”高度相关），但与其他簇的术语不同（即“SARS”与“Spain”之间的相似度接近于 0）。
现在，我们来看看如何使用这些嵌入向量解决特定任务。
SciCite：引用意图分类
本部分介绍了将嵌入向量用于下游任务（如文本分类）的方法。我们将使用 TensorFlow 数据集中的 SciCite 数据集对学术论文中的引文意图进行分类。给定一个带有学术论文引文的句子，对引用的主要意图进行分类：是背景信息、使用方法，还是比较结果。
Set up the dataset from 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)
Let's take a look at a few labeled examples from the training set
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]]
}))
训练引用意图分类器
我们将使用 Estimator 在 SciCite 数据集上对分类器进行训练。让我们设置 input_fns，将数据集读取到模型中。
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
我们构建一个模型，该模型使用 CORD-19 嵌入向量，并在顶部具有一个分类层。
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,
})
Hyperparmeters
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
}
训练并评估模型
让我们训练并评估模型以查看在 SciCite 任务上的性能。
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)
2022-06-03 15:57:24.599718: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. /tmpfs/tmp/ipykernel_7888/393120678.py:7: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. logits = tf.layers.dense( 2022-06-03 15:57:27.292257: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 0: loss 0.757, accuracy 0.693 2022-06-03 15:57:28.691465: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:30.098430: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 200: loss 0.692, accuracy 0.728 2022-06-03 15:57:31.097252: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:32.448996: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 400: loss 0.648, accuracy 0.755 2022-06-03 15:57:33.518678: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:34.888730: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 600: loss 0.630, accuracy 0.760 2022-06-03 15:57:35.894770: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:37.323809: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 800: loss 0.615, accuracy 0.761 2022-06-03 15:57:38.395212: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:39.742995: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 1000: loss 0.602, accuracy 0.772 2022-06-03 15:57:40.768911: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:42.217078: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 1200: loss 0.584, accuracy 0.775 2022-06-03 15:57:43.260860: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:44.769598: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 1400: loss 0.579, accuracy 0.776 2022-06-03 15:57:45.765620: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:47.089638: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 1600: loss 0.572, accuracy 0.777 2022-06-03 15:57:48.119079: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:49.510924: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 1800: loss 0.572, accuracy 0.777 2022-06-03 15:57:50.535905: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:51.872578: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 2000: loss 0.562, accuracy 0.786 2022-06-03 15:57:52.856432: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:54.179289: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 2200: loss 0.561, accuracy 0.783 2022-06-03 15:57:55.209365: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:56.570057: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 2400: loss 0.562, accuracy 0.781 2022-06-03 15:57:57.581781: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:57:58.957478: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 2600: loss 0.561, accuracy 0.777 2022-06-03 15:57:59.938241: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:01.418487: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 2800: loss 0.555, accuracy 0.785 2022-06-03 15:58:02.504248: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:03.837227: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 3000: loss 0.554, accuracy 0.788 2022-06-03 15:58:04.857276: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:06.205629: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 3200: loss 0.552, accuracy 0.791 2022-06-03 15:58:07.262203: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:08.671257: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 3400: loss 0.555, accuracy 0.778 2022-06-03 15:58:09.899864: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:11.247664: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 3600: loss 0.547, accuracy 0.786 2022-06-03 15:58:12.317154: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:13.695433: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 3800: loss 0.549, accuracy 0.780 2022-06-03 15:58:14.698366: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:16.026056: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 4000: loss 0.540, accuracy 0.792 2022-06-03 15:58:17.097506: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:18.473474: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 4200: loss 0.543, accuracy 0.788 2022-06-03 15:58:19.655576: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:20.971677: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 4400: loss 0.551, accuracy 0.784 2022-06-03 15:58:22.111667: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:23.560839: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 4600: loss 0.543, accuracy 0.794 2022-06-03 15:58:24.606913: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:25.973537: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 4800: loss 0.556, accuracy 0.771 2022-06-03 15:58:26.985245: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:28.332203: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 5000: loss 0.553, accuracy 0.779 2022-06-03 15:58:29.318939: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:30.656186: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 5200: loss 0.548, accuracy 0.780 2022-06-03 15:58:31.692607: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:33.053518: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 5400: loss 0.553, accuracy 0.774 2022-06-03 15:58:34.034002: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:35.397200: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 5600: loss 0.541, accuracy 0.788 2022-06-03 15:58:36.713981: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:38.097827: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 5800: loss 0.539, accuracy 0.790 2022-06-03 15:58:39.089203: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:40.442520: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 6000: loss 0.532, accuracy 0.799 2022-06-03 15:58:41.493169: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:42.827953: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 6200: loss 0.535, accuracy 0.791 2022-06-03 15:58:43.818725: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:45.162272: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 6400: loss 0.535, accuracy 0.790 2022-06-03 15:58:46.154605: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:47.483783: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 6600: loss 0.539, accuracy 0.791 2022-06-03 15:58:48.468785: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:49.815873: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 6800: loss 0.530, accuracy 0.798 2022-06-03 15:58:50.794918: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:52.183922: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 7000: loss 0.532, accuracy 0.797 2022-06-03 15:58:53.248291: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:54.600848: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 7200: loss 0.536, accuracy 0.782 2022-06-03 15:58:55.596162: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:56.923534: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 7400: loss 0.531, accuracy 0.798 2022-06-03 15:58:57.943252: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:58:59.361269: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 7600: loss 0.530, accuracy 0.795 2022-06-03 15:59:00.351200: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. 2022-06-03 15:59:01.726499: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. Global step 7800: loss 0.533, accuracy 0.793
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")
可以看到，损失迅速减小，而准确率迅速提高。我们绘制一些样本来检查预测与真实标签的关系：
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]
}))
2022-06-03 15:59:03.340237: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1087. Shape inference will have run different parts of the graph with different producer versions. /tmpfs/tmp/ipykernel_7888/393120678.py:7: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead. logits = tf.layers.dense(
可以看到，对于此随机样本，模型大多数时候都会预测正确的标签，这表明它可以很好地嵌入科学句子。
后续计划
现在，您已经对 TF-Hub 中的 CORD-19 Swivel 嵌入向量有了更多了解，我们鼓励您参加 CORD-19 Kaggle 竞赛，为从 COVID-19 相关学术文本中获得更深入的科学洞见做出贡献。
- 参加 CORD-19 Kaggle Challenge
- 详细了解 COVID-19 Open Research Dataset (CORD-19)
- 访问 https://tfhub.dev/tensorflow/cord-19/swivel-128d/1，参阅文档并详细了解 TF-Hub 嵌入向量
- 使用 TensorFlow Embedding Projector 探索 CORD-19 嵌入向量空间