TensorFlow：“尝试使用未初始化的值”在变量初始化中

Question

我试图在Python中使用TensorFlow实现多元线性回归，但遇到了一些逻辑和实现问题。我的代码引发以下错误：

Attempting to use uninitialized value Variable 
Caused by op u'Variable/read' 

理想的weights输出应该是[2, 3]

def hypothesis_function(input_2d_matrix_trainingexamples, 
         output_matrix_of_trainingexamples, 
         initial_parameters_of_hypothesis_function, 
         learning_rate, num_steps): 
    # calculate num attributes and num examples 
    number_of_attributes = len(input_2d_matrix_trainingexamples[0]) 
    number_of_trainingexamples = len(input_2d_matrix_trainingexamples) 

    #Graph inputs 
    x = [] 
    for i in range(0, number_of_attributes, 1): 
     x.append(tf.placeholder("float")) 
    y_input = tf.placeholder("float") 

    # Create Model and Set Model weights 
    parameters = [] 
    for i in range(0, number_of_attributes, 1): 
     parameters.append(
      tf.Variable(initial_parameters_of_hypothesis_function[i])) 

    #Contruct linear model 
    y = tf.Variable(parameters[0], "float") 
    for i in range(1, number_of_attributes, 1): 
     y = tf.add(y, tf.multiply(x[i], parameters[i])) 

    # Minimize the mean squared errors 
    loss = tf.reduce_mean(tf.square(y - y_input)) 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate) 
    train = optimizer.minimize(loss) 

    #Initialize the variables 
    init = tf.initialize_all_variables() 

    # launch the graph 
    session = tf.Session() 
    session.run(init) 
    for step in range(1, num_steps + 1, 1): 
     for i in range(0, number_of_trainingexamples, 1): 
      feed = {} 
      for j in range(0, number_of_attributes, 1): 
       array = [input_2d_matrix_trainingexamples[i][j]] 
       feed[j] = array 
      array1 = [output_matrix_of_trainingexamples[i]] 
      feed[number_of_attributes] = array1 
      session.run(train, feed_dict=feed) 

    for i in range(0, number_of_attributes - 1, 1): 
     print (session.run(parameters[i])) 

array = [[0.0, 1.0, 2.0], [0.0, 2.0, 3.0], [0.0, 4.0, 5.0]] 
hypothesis_function(array, [8.0, 13.0, 23.0], [1.0, 1.0, 1.0], 0.01, 200) 

Answer 1

这是从代码示例不是100％清楚，但如果该列表initial_parameters_of_hypothesis_function是tf.Variable对象的列表，那么行session.run(init)将会失败，因为TensorFlow还不够聪明，无法找出变量初始化中的依赖关系。要解决这个问题，你应该改变创建parameters循环使用initial_parameters_of_hypothesis_function[i].initialized_value()，增加必要的依赖关系：

parameters = [] 
for i in range(0, number_of_attributes, 1): 
    parameters.append(tf.Variable(
     initial_parameters_of_hypothesis_function[i].initialized_value())) 

Answer 2

运行以下命令：

init = tf.global_variables_initializer() 
sess.run(init) 

或（视TF的版本，您有）：

init = tf.initialize_all_variables() 
sess.run(init) 

Answer 3

我想给我的分辨率，当我与[sess = tf.InteractiveSession()]更换线[session = tf.Session()]工作。希望这对其他人有用。

Answer 4

在调用初始化全局变量时，还有另一个与顺序有关的错误发生。我有代码的样本具有类似的错误FailedPreconditionError（见上文回溯）：试图使用未初始化值W

def linear(X, n_input, n_output, activation = None): 
    W = tf.Variable(tf.random_normal([n_input, n_output], stddev=0.1), name='W') 
    b = tf.Variable(tf.constant(0, dtype=tf.float32, shape=[n_output]), name='b') 
    if activation != None: 
     h = tf.nn.tanh(tf.add(tf.matmul(X, W),b), name='h') 
    else: 
     h = tf.add(tf.matmul(X, W),b, name='h') 
    return h 

from tensorflow.python.framework import ops 
ops.reset_default_graph() 
g = tf.get_default_graph() 
print([op.name for op in g.get_operations()]) 
with tf.Session() as sess: 
    # RUN INIT 
    sess.run(tf.global_variables_initializer()) 
    # But W hasn't in the graph yet so not know to initialize 
    # EVAL then error 
    print(linear(np.array([[1.0,2.0,3.0]]).astype(np.float32), 3, 3).eval()) 

您应更改为以下

from tensorflow.python.framework import ops 
ops.reset_default_graph() 
g = tf.get_default_graph() 
print([op.name for op in g.get_operations()]) 
with tf.Session() as 
    # NOT RUNNING BUT ASSIGN 
    l = linear(np.array([[1.0,2.0,3.0]]).astype(np.float32), 3, 3) 
    # RUN INIT 
    sess.run(tf.global_variables_initializer()) 
    print([op.name for op in g.get_operations()]) 
    # ONLY EVAL AFTER INIT 
    print(l.eval(session=sess)) 

Answer 5

通常有两种初始化变量的方法，1）使用sess.run(tf.global_variables_initializer())作为以前的答案; 2）从检查点加载图形。

你可以这样做：

sess = tf.Session(config=config) 
saver = tf.train.Saver(max_to_keep=3) 
try: 
    saver.restore(sess, tf.train.latest_checkpoint(FLAGS.model_dir)) 
    # start from the latest checkpoint, the sess will be initialized 
    # by the variables in the latest checkpoint 
except ValueError: 
    # train from scratch 
    init = tf.global_variables_initializer() 
    sess.run(init) 

而第三种方法是使用tf.train.Supervisor。该会议将是

Create a session on 'master', recovering or initializing the model as needed, or wait for a session to be ready.

sv = tf.train.Supervisor([parameters]) 
sess = sv.prepare_or_wait_for_session()