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python - 如何用另一个网络的权重初始化一个网络的权重？

问题描述

我想将 2 个网络合并为一个网络，同时保持原始网络的权重。

我使用以下方法以 numpy 形式保存了权重：

for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
    weights[i.name] = i.eval()

我找不到将权重加载到新网络变量中的方法。有没有办法将权重加载到所有变量？

我尝试了以下但得到错误：

for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
    i.initializer = weights[i.name]

错误：

AttributeError: can't set attribute

标签： pythonnumpytensorflow

解决方案

您可以编写这两个函数

def save_to_dict(sess, collection=tf.GraphKeys.TRAINABLE_VARIABLES):
    return {v.name: sess.run(v) for v in tf.global_variables()}


def load_from_dict(sess, data):
    for v in tf.global_variables():
        if v.name in data.keys():
            sess.run(v.assign(data[v.name]))

诀窍是简单地遍历所有变量并检查它们是否存在于字典中，比如

import tensorflow as tf
import numpy as np


def save_to_dict(sess, collection=tf.GraphKeys.TRAINABLE_VARIABLES):
    return {v.name: sess.run(v) for v in tf.global_variables()}


def load_from_dict(sess, data):
    for v in tf.global_variables():
        if v.name in data.keys():
            sess.run(v.assign(data[v.name]))


def network(x):
    x = tf.layers.dense(x, 512, activation=tf.nn.relu, name='fc0')
    x = tf.layers.dense(x, 512, activation=tf.nn.relu, name='fc1')
    x = tf.layers.dense(x, 512, activation=tf.nn.relu, name='fc2')
    x = tf.layers.dense(x, 512, activation=tf.nn.relu, name='fc3')
    x = tf.layers.dense(x, 512, activation=tf.nn.relu, name='fc4')
    return x


element = np.random.randn(8, 10)
weights = None

# first session
with tf.Session() as sess:

    x = tf.placeholder(dtype=tf.float32, shape=[None, 10])
    y = network(x)
    sess.run(tf.global_variables_initializer())

    # first evaluation
    expected = sess.run(y, {x: element})

    # dump as dict
    weights = save_to_dict(sess)

# destroy session and graph
tf.reset_default_graph()

# second session
with tf.Session() as sess:

    x = tf.placeholder(dtype=tf.float32, shape=[None, 10])
    y = network(x)
    sess.run(tf.global_variables_initializer())

    # use randomly initialized parameters
    actual = sess.run(y, {x: element})
    assert np.sum(np.abs(actual - expected)) > 0  # should NOT match

    # load previous parameters
    load_from_dict(sess, weights)

    actual = sess.run(y, {x: element})
    assert np.sum(np.abs(actual - expected)) == 0  # should match

这样，您可以简单地从字典中删除一些参数，在加载之前更改权重，甚至更改参数名称。

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