相关疑难解决方法(0)

如何列出节点所依赖的所有Tensorflow变量/常量/占位符？

示例1(添加常量):

import tensorflow as tf

a = tf.constant(1, name = 'a')
b = tf.constant(3, name = 'b')
c = tf.constant(9, name = 'c')
d = tf.add(a, b, name='d')
e = tf.add(d, c, name='e')

sess = tf.Session()
print(sess.run([d, e]))

我想有一个功能list_dependencies(),如:

• list_dependencies(d) 回报 ['a', 'b']
• list_dependencies(e) 回报 ['a', 'b', 'c']

示例2(占位符和权重矩阵之间的矩阵乘法,然后添加偏差向量):

tf.set_random_seed(1)
input_size  = 5
output_size = 3
input       = tf.placeholder(tf.float32, shape=[1, input_size], name='input')
W           = tf.get_variable(
                "W",
                shape=[input_size, output_size],
                initializer=tf.contrib.layers.xavier_initializer())
b           = tf.get_variable(
                "b",
                shape=[output_size],
                initializer=tf.constant_initializer(2))
output      = …

我将探讨如何在图中表示变量.我创建一个变量,初始化它并在每个动作后创建图形快照:

import tensorflow as tf

def dump_graph(g, filename):
    with open(filename, 'w') as f:
        print(g.as_graph_def(), file=f)

g = tf.get_default_graph()
var = tf.Variable(2)
dump_graph(g, 'data/after_var_creation.graph')

init = tf.global_variables_initializer()
dump_graph(g, 'data/after_initializer_creation.graph')

with tf.Session() as sess:
    sess.run(init)
    dump_graph(g, 'data/after_initializer_run.graph')

变量创建后的图形看起来像

node {
  name: "Variable/initial_value"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 2
      }
    }
  }
}
node {
  name: "Variable"
  op: "VariableV2"
  attr {
    key: …