Keras layer.weights和layer.get_weights()给出不同的值 [英] Keras layer.weights and layer.get_weights() give different values

问题描述

我的Keras模型具有Dense层，我需要访问这些层的权重和偏差值.我可以使用get_weights()方法访问它们.它会返回给我预期的大小和权重矩阵(权重为57X50).

My Keras model have Dense layers which I need to access the weights and bias values. I can access them using get_weights() method. It returns me expected sized matrices (57X50 for the weights) for weights and biases.

model.layers[0].get_weights()[0]

但是，以下代码段为我提供了大小相同但大小不同的矩阵.

However the following code snippet gives me same sized matrices with different values.

import tensorflow as tf
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print(sess.run(model.layers[0].weights[0]))

在第二种方法中，由于所有模型的全零和权重与get_weights()方法的输出不同，因此会返回偏差值.

In the second method bias values are returned as all zeros for all models and weights are different than the output of get_weights() method.

您是否知道哪种方法正确，第二种方法究竟能做什么?

Do you have any idea which way is correct and what exactly the second method does?

推荐答案

使用init_op初始化所有可训练变量，这意味着偏差为零，模型的其他权重为随机值.试试:

With init_op, you initialize all trainable variables, which means zeros for biases and random values for the other weights of your model. Try:

import keras.backend as K
with K.get_session() as sess:
    print(sess.run(model.layers[0].weights[0]))

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