如何在tf.keras中设置Conv2D的默认参数？

Question

支持我有一个具有 5 个卷积的网络。我是用 Keras 写的。x = Input(shape=(None, None, 3))y = Conv2D(10, 3, strides=1)(x)y = Conv2D(16, 3, strides=1)(y)y = Conv2D(32, 3, strides=1)(y)y = Conv2D(48, 3, strides=1)(y)y = Conv2D(64, 3, strides=1)(y)我想将所有卷积设置kernel_initializer为 xavier。方法之一是：x = Input(shape=(None, None, 3))y = Conv2D(10, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer())(x)y = Conv2D(16, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer())(y)y = Conv2D(32, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer())(y)y = Conv2D(48, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer())(y)y = Conv2D(64, 3, strides=1, kernel_initializer=tf.glorot_uniform_initializer())(y)但是这种写法很伤感，代码也很冗余。有没有更好的写作方式？

Answer 1

Keras 无法更改默认值，因此您只需创建一个包装函数即可：

def myConv2D(filters, kernel):

    return Conv2D(filters, kernel, strides=1, kernel_initializer=tf.glorot_uniform_initializer())

然后将其用作：

x = Input(shape=(None, None, 3))

y = myConv2D(10, 3)(x)

y = myConv2D(16, 3)(y)

y = myConv2D(32, 3)(y)

y = myConv2D(48, 3)(y)

y = myConv2D(64, 3)(y)

Answer 2

更好地 make alambda将创建一个Conv2D层并根据需要修复初始化程序并在模型定义部分调用它。

我认为 lambda 比函数更适合这种情况。

你可以这样做，

customConv = lambda filters, kernel : Conv2D(filters, kernel, strides=1, kernel_initializer=tf.glorot_uniform_initializer())

x = Input(shape=(None, None, 3))

y = customConv(10, 3)(x)

y = customConv(16, 3)(y)

y = customConv(32, 3)(y)

y = customConv(48, 3)(y)

y = customConv(64, 3)(y)