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To know the value, we need to create the session graph and explicitly use the run
command with the desired tensor values as done with the following code:
Chapter 2
print(sess.run(t_1)) # Will print the value of t_1 defined in step 1
An example of TensorFlow 1.x in
TensorFlow 2.x
We can see that the TensorFlow 1.x APIs offer flexible ways to create and manipulate
computational graphs representing (deep) neural networks and many other types
of machine learning programs. TensorFlow 2.x on the other hand offers higher-level
APIs that abstract more lower-level details. To conclude, let's go back to an example
of the TensorFlow 1.x program that we encountered in the previous chapter. Here,
we also add a line to show the computational graph:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
in_a = tf.placeholder(dtype=tf.float32, shape=(2))
def model(x):
with tf.variable_scope("matmul"):
W = tf.get_variable("W", initializer=tf.ones(shape=(2,2)))
b = tf.get_variable("b", initializer=tf.zeros(shape=(2)))
return x * W + b
out_a = model(in_a)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
outs = sess.run([out_a],
feed_dict={in_a: [1, 0]})
writer = tf.summary.FileWriter("./logs/example", sess.graph)
Please note that the syntax x * W + b is just the linear perceptron defined in the
previous chapter. Now let's start a visualization application called "TensorBoard"
to show the computational graph:
tensorboard --logdir=./logs/example/
And let's open a browser pointing to http://localhost:6006/#graphs&run=.
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