TensorFlow Attempting to use uninitialized value in variable initialization

Question

I am trying to implement multivariate linear regression in Python using TensorFlow  but have run into some logical and implementation issues  My code throws the following error   Attempting to use uninitialized value Variable Caused by op u Variable read    Ideally the weights output should be  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

User · Answer

There is another the error happening which related to the order when calling initializing global variables. I've had the sample of code has similar error FailedPreconditionError (see above for traceback): Attempting to use uninitialized value 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())

You should change to following

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))

User · Answer

It s not 100  clear from the code example  but if the list initial parameters of hypothesis function is a list of tf Variable objects  then the line session run init  will fail because TensorFlow isn t  yet  smart enough to figure out the dependencies in variable initialization  To work around this  you should change the loop that creates parameters to use initial parameters of hypothesis function i  initialized value    which adds the necessary dependency   parameters      for i in range 0  number of attributes  1       parameters append tf Variable          initial parameters of hypothesis function i  initialized value

User · Answer

I want to give my resolution  it work when i replace the line  session   tf Session    with  sess   tf InteractiveSession     Hope this will be useful to others

User · Answer

run both    sess run tf global variables initializer     sess run tf local variables initializer

User · Answer

Normally there are two ways of initializing variables  1  using the sess run tf global variables initializer    as the previous answers noted  2  the load the graph from checkpoint    You can do like this    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    And the third method is to use the tf train Supervisor  The session will be      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

User · Answer

Run this   init   tf global variables initializer   sess run init    Or  depending on the version of TF that you have    init   tf initialize all variables   sess run init

[python] TensorFlow: "Attempting to use uninitialized value" in variable initialization

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