In TensorFlow, I can initialize variables in two the ways:
Call global_variable_intializer() before declaration of variable:
import tensorflow as tf
# Initialize the global variable and session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
W = tf.Variable([.3], tf.float32)
x = tf.Variable([-.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
linear_model = W * x + b
Call global_variable_intializer() after declaration of variable:
import tensorflow as tf
W = tf.Variable([.3], tf.float32)
x = tf.Variable([-.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
linear_model = W * x + b
# Initialize the global variable and session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
What is the difference between both? Which is the best way to initialize variables?
Edit
This is the actual program I am running :
import tensorflow as tf
# Initialize the global variable and session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
linear_model = W * x + b
square_delta = tf.square(linear_model - y)
loss = tf.reduce_sum(square_delta)
fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
In the case 1, the variables are not initialized, and if you try
sess.run(linear_model)
it should give you some kind of error (FailedPreconditionError on my compiler).
Case 2 is the working one.
The command
tf.global_variables_initializer()
should be called after all variables are created, otherwise the same error will be raised.
As far as I understand, each time you call tf.Variable, the nodes related to a variable are added to the graph. These are the following:
Variable/initial_value
Variable
Variable/Assign
Variable/read
(you obtain the nodes constructed so far with the command
for n in tf.get_default_graph().as_graph_def().node:
print n.name
)
The variable itself, has no value until you run within a Session the Variable/Assign node.
The command
init = tf.global_variables_initializer()
creates a single node containing all the assign nodes of all the variables constructed so far, and associate it to the python variable 'init' so that when it is executed the line
sess.run(init)
all variables acquire the initial value.
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