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python - 将 Tensorflow tf.keras.initializers.GlorotNormal(seed=1) 应用于 tf.Variable

问题描述

如何将初始化程序应用于 tf.Variable 函数？我在正确的轨道上吗？

def initialize_parameters():
                                
    initializer = tf.keras.initializers.GlorotNormal(seed=1)   
   
    W1 = tf.Variable(initializer(shape=([25, 12288]))
    b1 = tf.Variable(initializer(shape=([25, 1]))
    W2 = tf.Variable(initializer(shape=([12, 25]))
    b2 = tf.Variable(initializer(shape=([12, 1]))
    W3 = tf.Variable(initializer(shape=([6, 12]))
    b3 = tf.Variable(initializer(shape=([6, 1]))
  

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}
    
    return parameters

我希望形状如下 -


W1 shape: (25, 12288)
b1 shape: (25, 1)
W2 shape: (12, 25)
b2 shape: (12, 1)
W3 shape: (6, 12)
b3 shape: (6, 1)

标签： pythontensorflowdeep-learning

解决方案

应该是W1 = tf.Variable(initializer(shape=(25, 12288)))。注意圆括号

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