在TF 2.0上设置随机种子

import tensorflow as tf
import numpy as np
import random

seed = 42
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)

# 在创建模型时设置随机种子
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=seed)),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 在数据集划分时设置随机种子
train_dataset, test_dataset = tf.keras.datasets.mnist.load_data()
train_dataset = tf.data.Dataset.from_tensor_slices(train_dataset).shuffle(60000).batch(64, seed=seed)

# 在数据增强时设置随机种子
data_augmentation = tf.keras.Sequential([
    tf.keras.layers.experimental.preprocessing.RandomFlip(seed=seed),
    tf.keras.layers.experimental.preprocessing.RandomRotation(0.2, seed=seed)
])