如何对Keras文本分类进行预测？

Question

我用这个引用训练了一个模型：https://www.tensorflow.org/tutorials/keras/text_classification_with_hub

这是我的密码：

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds

train_data, validation_data, test_data = tfds.load(
    name="imdb_reviews", 
    split=('train[:60%]', 'train[60%:]', 'test'),
    as_supervised=True)

train_examples_batch, train_labels_batch = next(iter(train_data.batch(10)))

embedding = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1"
hub_layer = hub.KerasLayer(embedding, input_shape=[], 
                        dtype=tf.string, trainable=True)
hub_layer(train_examples_batch[:3])

model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1))

model.summary()

model.compile(optimizer='adam',
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=['accuracy'])

history = model.fit(train_data.shuffle(10000).batch(512),
    epochs=20,
    validation_data=validation_data.batch(512),
    verbose=1)

results = model.evaluate(test_data.batch(512), verbose=2)

model.save("imdb_model.h5")

我已将模型保存为imdb\u model.h5。我想对自定义文本进行预测。例如，我看过的最好的电影。我怎么做

Answer 1

你可以用

model.predict(["This is the best movie I have ever seen"])