我目前正在使用如下定义的conv2D layer训练convolutional neural network：
conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='SAME', activation='relu')(inputs)
我的理解是，默认的kernel_initializer是glorot_uniform，它的默认种子为"none"：
tf.keras.layers.Conv2D(
filters, kernel_size, strides=(1, 1), padding='valid', data_format=None,
dilation_rate=(1, 1), activation=None, use_bias=True,
kernel_initializer='glorot_uniform', bias_initializer='zeros',
kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, bias_constraint=None, **kwargs
)
tf.compat.v1.keras.initializers.glorot_uniform(seed=None, dtype=tf.dtypes.float32)
我正在尝试生成可复制的代码，并且已经根据StackOverflow帖子设置了随机种子：
seed_num = 1
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(seed_num)
rn.seed(seed_num)
session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
tf.random.set_seed(seed_num)
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)
K.set_session(sess)
glorot_uniform使用的tf.random.set_seed种子号是否在conv2D layer中？如果没有，在定义conv2D layer时，该种子将如何定义？
对于每一层，都可以使用内核和偏置初始化器的种子。
你可以单独为你的初始值设定器种子，
kernel_initializer=initializers.glorot_uniform(seed=0))
来自文件：
glorot_normal
keras.initializers.glorot_normal(seed=None)
Glorot normal initializer, also called Xavier normal initializer.
It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.
Arguments
seed: A Python integer. Used to seed the random generator.
感谢Zabir Al Nazil，答案是"是的"。设置tf.random.set_seed()还设置Conv2D层的glorot_uniform种子。