Question

我正在尝试在训练后从模型中提取权重。这是一个玩具示例

import tensorflow as tf
import numpy as np

X_ = tf.placeholder(tf.float64, [None, 5], name="Input")
Y_ = tf.placeholder(tf.float64, [None, 1], name="Output")

X = ...
Y = ...
with tf.name_scope("LogReg"):
    pred = fully_connected(X_, 1, activation_fn=tf.nn.sigmoid)
    loss = tf.losses.mean_squared_error(labels=Y_, predictions=pred)
    training_ops = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(200):
        sess.run(training_ops, feed_dict={
            X_: X,
            Y_: Y
        })
        if (i + 1) % 100 == 0:
            print("Accuracy: ", sess.run(accuracy, feed_dict={
                X_: X,
                Y_: Y
            }))

# Get weights of *pred* here

我查看了Get weights from tensorflow model 和docs，但无法找到检索权重值的方法。

所以在玩具示例中，假设X_具有形状（1000,5），我怎样才能获得1层权重中的5个值

Answer 1

您的代码中存在一些需要修复的问题：

1-您需要在以下行使用variable_scope代替name_scope（请参阅TensorFlow文档了解它们之间的差异）：

with tf.name_scope("LogReg"):

2-为了能够在代码中稍后检索变量，您需要知道它的名称。因此，您需要为感兴趣的变量指定一个名称（如果您不支持该变量，则会分配一个默认值，但是您需要确定它是什么！）：

pred = tf.contrib.layers.fully_connected(X_, 1, activation_fn=tf.nn.sigmoid, scope = 'fc1')

现在让我们看看上述修复如何帮助我们获取变量的值。每一层都有两种类型的变量：权重和偏差。在以下代码段（您的修改版本）中，我将仅展示如何检索完全连接层的权重：

X_ = tf.placeholder(tf.float64, [None, 5], name="Input")
Y_ = tf.placeholder(tf.float64, [None, 1], name="Output")

X = np.random.randint(1,10,[10,5])
Y = np.random.randint(0,2,[10,1])

with tf.variable_scope("LogReg"):
    pred = tf.fully_connected(X_, 1, activation_fn=tf.nn.sigmoid, scope = 'fc1')
    loss = tf.losses.mean_squared_error(labels=Y_, predictions=pred)
    training_ops = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

with tf.Session() as sess:

    all_vars= tf.global_variables()
    def get_var(name):
        for i in range(len(all_vars)):
            if all_vars[i].name.startswith(name):
                return all_vars[i]
        return None
    fc1_var = get_var('LogReg/fc1/weights')

    sess.run(tf.global_variables_initializer())    
    for i in range(200):
        _,fc1_var_np = sess.run([training_ops,fc1_var], feed_dict={
        X_: X,
        Y_: Y 
        })
        print fc1_var_np

Answer 2

试试这个：

with tf.Session() as sess:
    last_check = tf.train.latest_checkpoint(tf_data)
    saver = tf.train.import_meta_graph(last_check+'.meta')
    saver.restore(sess,last_check)
    ######
    Model_variables = tf.GraphKeys.MODEL_VARIABLES
    Global_Variables = tf.GraphKeys.GLOBAL_VARIABLES
    ######
    all_vars = tf.get_collection(Model_variables)
    # print (all_vars)
    for i in all_vars:
        print (str(i) + '  -->  '+ str(i.eval()))

我明白了：

<tf.Variable 'linear/linear_model/DOLocationID/weights/part_0:0' shape=(1, 1) dtype=float32_ref>  -->  [[-0.00912262]]
<tf.Variable 'linear/linear_model/PULocationID/weights/part_0:0' shape=(1, 1) dtype=float32_ref>  -->  [[ 0.00573495]]
<tf.Variable 'linear/linear_model/passenger_count/weights/part_0:0' shape=(1, 1) dtype=float32_ref>  -->  [[-0.07072949]]
<tf.Variable 'linear/linear_model/trip_distance/weights/part_0:0' shape=(1, 1) dtype=float32_ref>  -->  [[ 2.59973669]]
<tf.Variable 'linear/linear_model/bias_weights/part_0:0' shape=(1,) dtype=float32_ref>  -->  [ 4.27982235]

如何从tensorflow完全连接获得权重

2 个答案: