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TensorFlow 1.x and 2.x
Here, we used the optional argument name to give a name to a variable as defined
in the computational graph. In all the preceding examples, the source of variable
initialization is a constant. We can also specify a variable to be initialized from
another variable. The following statement will initialize weight2 from the weights
defined above:
weight2=tf.Variable(weights.initialized_value(), name='w2')
Initializing variables: The definition of variables specifies how they are to
be initialized, since we must explicitly initialize all declared variables. In the
definition of the computational graph, we do so by declaring an Operation Object:
intial_op = tf.global_variables_initializer()
Each variable can also be initialized separately using tf.Variable.initializer
while running the graph:
bias = tf.Variable(tf.zeros([100,100]))
with tf.Session() as sess:
sess.run(bias.initializer)
Saving variables: We can save all the variables using the Saver class. To do so, we
define a saver operation object:
saver = tf.train.Saver()
Placeholders: We define a placeholder using:
tf.placeholder(dtype, shape=None, name=None)
dtype specifies the data type of placeholder and must be specified while declaring
the placeholder. Next, we define the placeholder for x and calculate y=2x using
feed_dict for a random 4×5 matrix (remember that feed_dict is used to feed
values to TensorFlow placeholders):
x = tf.placeholder("float")
y = 2 * x
data = tf.random_uniform([4,5],10)
with tf.Session() as sess:
x_data = sess.run(data)
print(sess.run(y, feed_dict = {x:x_data}))
All variables and placeholders are defined in the Computational Graph section of
the code. If we use a print statement in the definition section, we will only get
information about the type of tensor and not about its value.
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