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Các CORD-19 xoay văn bản nhúng mô-đun từ TF-Hub ( https://tfhub.dev/tensorflow/cord-19/swivel-128d/1 ) được xây dựng để các nhà nghiên cứu hỗ trợ việc phân tích ngôn ngữ văn bản tự nhiên liên quan đến COVID-19. Những embeddings được tập huấn về các tiêu đề, tác giả, tóm tắt, văn bản cơ thể, và các chức danh tham khảo các bài viết trong tập dữ liệu CORD-19 .
Trong chuyên mục này, chúng tôi sẽ:
- Phân tích các từ tương tự về mặt ngữ nghĩa trong không gian nhúng
- Đào tạo người phân loại trên tập dữ liệu SciCite bằng cách sử dụng nhúng CORD-19
Thành lập
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
Phân tích các nhúng
Hãy bắt đầu bằng cách phân tích nhúng bằng cách tính toán và vẽ một ma trận tương quan giữa các thuật ngữ khác nhau. Nếu phương pháp nhúng đã học để nắm bắt thành công ý nghĩa của các từ khác nhau, thì các vectơ nhúng của các từ tương tự về mặt ngữ nghĩa phải gần nhau. Chúng ta hãy xem xét một số thuật ngữ liên quan đến 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.
Chúng ta có thể thấy rằng việc nhúng đã nắm bắt thành công ý nghĩa của các thuật ngữ khác nhau. Mỗi từ tương tự với các từ khác trong cụm của nó (tức là "coronavirus" có tương quan cao với "SARS" và "MERS"), trong khi chúng khác với các từ của các cụm khác (nghĩa là sự giống nhau giữa "SARS" và "Tây Ban Nha" là gần bằng 0).
Bây giờ chúng ta hãy xem cách chúng ta có thể sử dụng các nhúng này để giải quyết một nhiệm vụ cụ thể.
SciCite: Phân loại ý định trích dẫn
Phần này cho thấy cách người ta có thể sử dụng tính năng nhúng cho các tác vụ phía dưới, chẳng hạn như phân loại văn bản. Chúng tôi sẽ sử dụng các dữ liệu SciCite từ TensorFlow Datasets để intents trích dẫn classify trong giấy tờ học tập. Đưa ra một câu có trích dẫn từ một bài báo học thuật, hãy phân loại xem mục đích chính của trích dẫn là làm thông tin cơ bản, sử dụng các phương pháp hay so sánh kết quả.
Thiết lập tập dữ liệu từ 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)
Hãy xem một vài ví dụ được gắn nhãn từ tập huấn luyện
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]]
}))
Đào tạo một trình phân loại ý định citaton
Chúng tôi sẽ đào tạo một phân loại trên bộ dữ liệu SciCite sử dụng Công cụ Ước tính. Hãy thiết lập input_fns để đọc tập dữ liệu vào mô hình
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
Hãy xây dựng một mô hình sử dụng nhúng CORD-19 với một lớp phân loại ở trên cùng.
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
}
Đào tạo và đánh giá mô hình
Hãy cùng đào tạo và đánh giá mô hình để xem hiệu suất trên tác vụ 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")
Chúng ta có thể thấy rằng sự mất mát nhanh chóng giảm đi trong khi đặc biệt là độ chính xác tăng lên nhanh chóng. Hãy vẽ một số ví dụ để kiểm tra mức độ liên quan của dự đoán với các nhãn thực:
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.
Chúng ta có thể thấy rằng đối với mẫu ngẫu nhiên này, mô hình dự đoán nhãn chính xác hầu hết các lần, cho thấy rằng nó có thể nhúng các câu khoa học khá tốt.
Cái gì tiếp theo?
Bây giờ bạn đã biết thêm một chút về nhúng CORD-19 Swivel từ TF-Hub, chúng tôi khuyến khích bạn tham gia cuộc thi CORD-19 Kaggle để góp phần thu được những hiểu biết khoa học từ các văn bản học thuật liên quan đến COVID-19.
- Tham gia vào CORD-19 Kaggle Challenge
- Tìm hiểu thêm về COVID-19 mở Research Dataset (CORD-19)
- Xem tài liệu và nhiều hơn nữa về embeddings TF-Hub tại https://tfhub.dev/tensorflow/cord-19/swivel-128d/1
- Khám phá CORD-19 không gian nhúng với TensorFlow Nhúng chiếu