import tensorflow as tf
new_model = tf.keras.models.load_model('saved_model/my_model_KNOCK_2_RMS')
new_model.get_weights()
在检索权重时如何检索偏差矩阵？还是有不同的方法来获得偏差矩阵？
问问题
106 次
2 回答
1
model.get_weights()返回网络的所有变量，包括偏差。
您可以遍历variables属性并过滤名称tf.Variable以仅获取偏差。
biases = [var for var in new_model.variables if "bias" in var.name]
于 2021-05-21T14:32:13.690 回答
0
new_model.variables
[<tf.Variable 'dense/kernel:0' shape=(4, 4) dtype=float32, numpy=
array([[ 1.8315854 , 1.6919162 , 2.107687 , 2.1731293 ],
[-0.4066143 , 0.24807486, -0.34563315, 0.70929044],
[ 0.5660119 , -0.39092124, 0.57988596, 0.7534707 ],
[ 0.38233787, 0.09385393, 0.25826836, 0.28291 ]],
dtype=float32)>,
<tf.Variable 'dense/bias:0' shape=(4,) dtype=float32, numpy=array([-0.84321135, -0.93141776, -0.95930505, -0.36669353], dtype=float32)>,
<tf.Variable 'dense_1/kernel:0' shape=(4, 10) dtype=float32, numpy=
array([[ 1.7296976 , 0.68885595, 0.47779882, 1.2458457 , 1.3748846 ,
1.0451635 , -0.05860029, 0.2059054 , 1.1549207 , -0.14830673],
[ 2.5991514 , 1.7558911 , -0.65219647, 1.948722 , 1.2213669 ,
2.0473976 , -0.57937807, 0.16753212, 1.2601147 , -0.4593185 ],
[ 1.8368433 , 1.253897 , -0.5726242 , 0.83214754, 0.85430264,
1.3974545 , 0.8320734 , -0.624181 , 1.2566972 , -0.26695323],
[ 0.8506076 , 0.52599937, -0.31859252, 0.45206892, 0.727149 ,
0.8375796 , -0.6014804 , -0.963803 , 0.4475311 , -0.52666175]],
dtype=float32)>,
<tf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=
array([-0.69184536, -0.64424694, 0. , -0.7509772 , -0.7106489 ,
-0.8235373 , 0.5724167 , 0.8609912 , -0.5833405 , 0. ],
dtype=float32)>,
<tf.Variable 'dense_2/kernel:0' shape=(10, 10) dtype=float32, numpy=
array([[-0.43679273, -0.18084297, -0.19233215, 2.261455 , 0.0789384 ,
-0.57109463, -0.18623981, -0.13344003, -0.16805501, -0.08802319],
[-0.44742152, 0.5294885 , 0.44007984, -0.8000098 , 0.43828738,
-0.14650299, 0.3873879 , 0.4865123 , 0.7062375 , 0.3959973 ],
[ 0.42817426, -0.25457212, -0.06503531, 0.25967544, -0.36173528,
-0.28454632, 0.5185325 , 0.52907014, -0.50651705, -0.01279312],
[-0.17378198, 0.61804473, 0.40438575, -0.84975696, 0.46335214,
0.03958785, 0.59150505, 0.3461628 , 0.5345084 , 0.64246666],
[-0.05633026, 0.19354568, 0.14509334, -0.65395653, 0.82471824,
-0.33441678, 0.45360735, 0.3273876 , 0.48969913, 0.5674778 ],
[-0.08906014, 0.35384002, 0.3765939 , -0.55490863, 0.49258858,
-0.20532486, 0.31845653, 0.23653099, 0.29804358, 0.32997373],
[-0.44412076, -2.754143 , -3.0303166 , -1.8454257 , -0.30921584,
-0.338421 , -2.954837 , -2.8979506 , -2.6388538 , -2.6987133 ],
[-0.35004058, -1.8016477 , -1.896363 , -0.9093097 , 0.01669006,
-0.57078224, -2.097838 , -2.196825 , -2.2054377 , -2.0546691 ],
[ 0.0622101 , 0.56875587, 0.64062774, -0.8085089 , 0.2828998 ,
-0.01109717, 0.42925444, 0.46951735, 0.45354208, 0.44528142],
[-0.17158392, 0.00582236, 0.05534732, 0.52236724, 0.24274498,
0.06249171, -0.39602056, -0.49559176, 0.22903812, -0.2540841 ]],
dtype=float32)>,
<tf.Variable 'dense_2/bias:0' shape=(10,) dtype=float32, numpy=
array([-0.4239517 , 5.001588 , 4.77233 , -0.1700763 , 3.3235748 ,
-0.36088318, 5.0669746 , 4.534893 , 5.156966 , 5.2919803 ],
dtype=float32)>,
<tf.Variable 'dense_3/kernel:0' shape=(10, 1) dtype=float32, numpy=
array([[-0.11622595],
[ 1.4069948 ],
[ 1.5280082 ],
[ 4.962922 ],
[ 1.3706445 ],
[-0.09094736],
[ 1.4304978 ],
[ 1.7208712 ],
[ 1.4002761 ],
[ 1.2989398 ]], dtype=float32)>,
<tf.Variable 'dense_3/bias:0' shape=(1,) dtype=float32, numpy=array([25.658356], dtype=float32)>]
于 2021-05-21T15:52:07.757 回答