为什么 get_tensor_by_name 无法正确获取 tf.keras.layers 定义的层的权重 [英] Why get_tensor_by_name can't get the weights of layers defined by tf.keras.layers properly

问题描述

我尝试通过在 tensorflow 中使用 get_tensor_by_name 来获取由 tf.keras.layers 定义的层的权重.代码呈现如下

I try to get the weights of layers defined by tf.keras.layers by using get_tensor_by_name in tensorflow. The code is presented as follows

# encoding: utf-8
import tensorflow as tf

x = tf.placeholder(tf.float32, (None,3))
h = tf.keras.layers.dense(3)(x)
y = tf.keras.layers.dense(1)(h)

for tn in tf.trainable_variables():
    print(tn.name)

sess = tf.Session()
sess.run(tf.global_variables_initializer())
w = tf.get_default_graph().get_tensor_by_name("dense/kernel:0")
print(sess.run(w))

权重的名称是dense/kernel:0.然而，sess.run(w) 的输出很奇怪

The name of the weight is dense/kernel:0. However, the output of sess.run(w) is weird

[( 10,) ( 44,) ( 47,) (106,) (111,) ( 98,) ( 58,) (108,) (111,) ( 99,)
 ( 97,) (108,) (104,) (111,) (115,) (116,) ( 47,) (114,) (101,) 
 ... ]

这不是浮点数组.事实上，如果我使用 tf.layers.dense 来定义网络，一切都会好起来的.所以我的问题是如何通过正确使用张量名称来获得 tf.keras.layers 定义的层的权重.

which is not an array of floats. In fact, if I use tf.layers.dense to define the network, everything goes fine. So my question is that how I can get the weights of layers defined by tf.keras.layers by using tensor name properly.

推荐答案

您可以在图层上使用 get_weights() 来获取特定图层的权重值.以下是您案例的示例代码:

You can use get_weights() on layers to get the weight values of particular layers. Here is an example code for your case:

import tensorflow as tf

input_x = tf.placeholder(tf.float32, [None, 3], name='x')    
dense1 = tf.keras.Dense(3, activation='relu')
l1 = dense1(input_x)
dense2 = tf.keras.Dense(1)
y = dense2(l1)

weights = dense1.get_weights()

可以使用 Keras API 以更简单的方式完成，如下所示:

It can be done in a even simpler way with Keras API as follows:

def mymodel():
    i = Input(shape=(3, ))
    x = Dense(3, activation='relu')(i)
    o = Dense(1)(x)

    model = Model(input=i, output=o)
    return model


model = mymodel()

names = [weight.name for layer in model.layers for weight in layer.weights]
weights = model.get_weights()

for name, weight in zip(names, weights):
    print(name, weight.shape)

此示例获取模型每一层的权重矩阵.

This example gets weight matrices for each layer of your model.

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