tf.layer.conv2d这里面默认的kernel_initializer为None,经查阅源码 self.kernel= vs.get_variable('kernel',shape=kernel_shape,initializer=self.kernel_initializer,regularizer=self.kernel_regularizer,trainable=True,dtype=self.dtype) 这里面有一段说明 Ifinitializeris`None` (thedefault), thedefaultinitializer pas...
你只需要在创建层时设置 `kernel_initializer` 参数即可 在Keras 中使用 Xavier 初始化(也称为 Glorot 初始化)非常简单。你只需要在创建层时设置 `kernel_initializer` 参数即可。对于 Xavier 初始化,你可以使用 `'glorot_uniform'` 或 `'glorot_normal'`,前者是从均匀分布中抽取权重,后者是从正态分布中抽取权重...
原地址: https://zhuanlan.zhihu.com/p/134578878dense的参数 kernel_initializer 和 bias_initializer ,可选列表如下。 zero = zeros = Zeros one = ones = Ones constant = Constant uniform = random_uniform…
import tensorflow as tf # 定义GRU层 gru_layer = tf.keras.layers.GRU(num_units=256, activation='tanh', kernel_initializer='glorot_uniform', bias_initializer='zeros') # 输入数据形状为(batch_size, time_steps, input_dim) inputs = tf.keras.Input(shape=(time_steps, input_dim)) # 将输入数...
kernel_initializer=tf.TruncatedNormal(stddev=0.01) bias_initializer=tf.Constant(0), ) 或者: conv1 = tf.layers.conv2d(batch_images, filters=64, kernel_size=7, strides=2, activation=tf.nn.relu, kernel_initializer=tf.truncated_normal_initializer(stddev=0.01) ...
tf.zeros_initializer:可以简写为tf.Zeros。 tf.ones_initializer:可以简写为tf.Ones。 在卷积层中,将偏置项b初始化为0,有多种写法: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 conv1 = tf.layers.conv2d( # 方法1 batch_images, filters=64, kernel_size=7, strides=2, activation=tf.nn.relu...
kernel_initializer — 核权重的初始值设定项 bias_initializer —偏置向量的初始值设定项。 model = Sequential(layers=None, name=None) model.add(Dense(10, input_shape = (29,), activation = 'tanh')) model.add(Dense(5, activation = 'tanh')) model.add(Dense(1, activation = 'sigmoid')) sgd...
kernel_initializer="glorot_uniform", bias_initializer="zeros", kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs ) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
importtensorflowastfX=tf.constant([[1.0,2.0,3.0],[4.0,5.0,6.0]])y=tf.constant([[10.0],[20.0]])classLinear(tf.keras.Model):def__init__(self):super().__init__()self.dense=tf.keras.layers.Dense(units=1,activation=None,kernel_initializer=tf.zeros_initializer(),bias_initializer=tf.zeros...
kernel_initializer='glorot_uniform', bias_initializer='zeros', activation='tanh') ) # add hidden layer model.add( keras.layers.Dense( units=50, input_dim=50, kernel_initializer='glorot_uniform', bias_initializer='zeros', activation='tanh') ...