mirror of
https://github.com/tengge1/ShadowEditor.git
synced 2026-01-25 15:08:11 +00:00
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
|
|
|
|
import numpy as np
|
|
|
|
import tensorflow as tf
|
|
|
|
import tensorflow_hub as hub
|
|
import tensorflow_datasets as tfds
|
|
|
|
print("Version: ", tf.__version__)
|
|
print("Eager mode: ", tf.executing_eagerly())
|
|
print("Hub version: ", hub.__version__)
|
|
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")
|
|
|
|
# 将训练集按照 6:4 的比例进行切割，从而最终我们将得到 15,000
|
|
# 个训练样本, 10,000 个验证样本以及 25,000 个测试样本
|
|
train_validation_split = tfds.Split.TRAIN.subsplit([6, 4])
|
|
|
|
(train_data, validation_data), test_data = tfds.load(
|
|
name="imdb_reviews",
|
|
split=(train_validation_split, tfds.Split.TEST),
|
|
as_supervised=True)
|
|
|
|
train_examples_batch, train_labels_batch = next(iter(train_data.batch(10)))
|
|
train_examples_batch
|
|
|
|
embedding = "https://hub.tensorflow.google.cn/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, activation='sigmoid'))
|
|
|
|
model.summary()
|
|
|
|
model.compile(optimizer='adam',
|
|
loss='binary_crossentropy',
|
|
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=0)
|
|
for name, value in zip(model.metrics_names, results):
|
|
print("%s: %.3f" % (name, value))
|