【发布时间】：2021-10-13 08:38:07
【问题描述】：
我正在尝试获取给定网络的初始权重。
这个帖子建议需要指定输入维度：How to view initialized weights (i.e. before training)?
这个帖子建议编译后权重应该可用：Reset weights in Keras layer
在编译模型之后但在训练之前保存初始权重。
虽然我在第一篇文章中复制了结果，但我无法将其应用于我的案例：
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
# Reproducing https://stackoverflow.com/questions/46798708/how-to-view-initialized-weights-i-e-before-training
# First model without input_dim prints an empty list
model = Sequential()
model.add(Dense(5, weights=[np.ones((3, 5)), np.zeros(5)],
activation='relu'))
print(model.get_weights())
# Second model with input_dim prints the assigned weights
model1 = Sequential()
model1.add(Dense(5, weights=[np.ones((3, 5)), np.zeros(5)],
input_dim=3,
activation='relu'))
model1.add(Dense(1, activation='sigmoid'))
print(model1.get_weights())
class Test(tf.keras.Model):
def __init__(self, n_inputs: int, neurons=10):
super(Test, self).__init__(name="Test")
self.neurons = neurons
# Initilializers
mean, std = 0., 0.0005
bias_normalization = None
kernel_initializer = tf.keras.initializers.RandomNormal(mean=mean,
stddev=std)
self.h1 = Dense(n_inputs, activation="linear", name="h1",
kernel_initializer=kernel_initializer,
bias_initializer=bias_normalization,
input_dim=n_inputs)
def call(self, inputs):
x = self.h1(inputs)
return x
# Model Test
test = Test(n_inputs=1, neurons=100)
test.get_weights() # empty, expected
test.compile()
test.get_weights() # empty, unexpected
【问题讨论】：
-
第二个建议有什么问题？立即保存权重？
标签： python tensorflow machine-learning keras deep-learning