Intro to tensorflow Flashcards
1
Q
Tensor and tf.constant
A
- In TensorFlow, data isn’t stored as integers, floats, or strings. These values are encapsulated in an object called a tensor. In the case of hello_constant = tf.constant(‘Hello World!’), hello_constant is a 0-dimensional string tensor.
- The tensor returned by tf.constant() is called a constant tensor, because the value of the tensor never changes.
2
Q
Tensorflow session
A
- A “TensorFlow Session”, as shown above, is an environment for running a graph. The session is in charge of allocating the operations to GPU(s) and/or CPU(s), including remote machines
- with tf.Session() as sess:
output = sess.run(hello_constant)
3
Q
python with
A
with statement is actually very simple, once you understand the problem it’s trying to solve. Consider this piece of code:
set things up try: do something finally: tear things down Here, “set things up” could be opening a file, or acquiring some sort of external resource, and “tear things down” would then be closing the file, or releasing or removing the resource. The try-finally construct guarantees that the “tear things down” part is always executed, even if the code that does the work doesn’t finish.
4
Q
tf.placeholder()
A
- What if you want to use a non-constant? This is where tf.placeholder() and feed_dict come into place.
- Sadly you can’t just set x to your dataset and put it in TensorFlow, because over time you’ll want your TensorFlow model to take in different datasets with different parameters. You need tf.placeholder()!
- tf.placeholder() returns a tensor that gets its value from data passed to the tf.session.run() function, allowing you to set the input right before the session runs.
5
Q
tensor math operations
A
x = tf.add(5, 2) # 7 x = tf.subtract(10, 4) # 6 y = tf.multiply(2, 5) # 10
6
Q
Creating a variable in tensorflow
A
- tf.Variable()
- To initialize call tf.global_variables_initializer()
7
Q
Interesting functions
A
- tf.truncated_normal()
- tf.zeros()
- tf.matmul()
- tf.nn.softmax()
- tf.reduce_sum()
- tf.log()