Question

在我的培训档案（train.py）中，我写道：

def deep_part(self):
    with tf.variable_scope("deep-part"):                                              
        y_deep = tf.reshape(self.embeddings, shape=[-1, self.field_size * self.factor_size]) # None * (F*K)
        # self.deep_layers = 2
        for i in range(0,len(self.deep_layers)):
            y_deep = tf.contrib.layers.fully_connected(y_deep, self.deep_layers[i], \ 
            activation_fn=self.deep_layers_activation, scope = 'fc%d' % i)
        return y_deep

现在在预测文件（predict.py）中，我恢复了检查点，但我不知道如何重新加载“深部”网络的权重和偏差。因为我认为“full_conncted”函数可能会隐藏权重和偏差

Answer 1

我写了一篇冗长的explanation here。简短摘要：

按saver.save(sess, '/tmp/my_model') Tensorflow生成多个文件：

checkpoint
my_model.data-00000-of-00001
my_model.index
my_model.meta

检查点文件checkpoint只是指向最新版本的模型权重的指针，它只是一个包含

的纯文本文件

$ !cat /tmp/model/checkpoint
model_checkpoint_path: "/tmp/my_model"
all_model_checkpoint_paths: "/tmp/my_model"

其他是包含图形（ .meta）和权重（ .data *）的二进制文件。

您可以通过

来帮助自己

import tensorflow as tf
import numpy as np

data = np.arange(9 * 1).reshape(1, 9).astype(np.float32)

plhdr = tf.placeholder(tf.float32, shape=[1, 9], name='input')
print plhdr.name

activation = tf.layers.dense(plhdr, 10, name='fc')
print activation.name

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    expected = sess.run(activation, {plhdr: data})
    print expected

    saver = tf.train.Saver(tf.global_variables())
    saver.save(sess, '/tmp/my_model')


tf.reset_default_graph()
with tf.Session() as sess:
    # load the computation graph (the fully connected + placeholder)
    loader = tf.train.import_meta_graph('/tmp/my_model.meta')
    sess.run(tf.global_variables_initializer())

    plhdr = tf.get_default_graph().get_tensor_by_name('input:0')
    activation = tf.get_default_graph().get_tensor_by_name('fc/BiasAdd:0')
    actual = sess.run(activation, {plhdr: data})
    assert np.allclose(actual, expected) is False

    # now load the weights
    loader = loader.restore(sess, '/tmp/my_model')
    actual = sess.run(activation, {plhdr: data})
    assert np.allclose(actual, expected) is True

如何在fully_connected函数中恢复变量

1 个答案: