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Note: Please read through this tutorial carefully. The TensorFlow programming model is probably different from others that you have encountered, and thus may not be as intuitive as you'd expect.
TensorFlow gets its name from tensors, which are arrays of arbitrary dimensionality. Using TensorFlow, you can manipulate tensors with a very high number of dimensions. That said, most of the time you will work with one or more of the following low-dimensional tensors:
[2, 3, 5, 7, 11]or
[[3.1, 8.2, 5.9][4.3, -2.7, 6.5]]
TensorFlow operations create, destroy, and manipulate tensors. Most of the lines of code in a typical TensorFlow program are operations.
A TensorFlow graph (also known as a computational graph or a dataflow graph) is, yes, a graph data structure. A graph's nodes are operations (in TensorFlow, every operation is associated with a graph). Many TensorFlow programs consist of a single graph, but TensorFlow programs may optionally create multiple graphs. A graph's nodes are operations; a graph's edges are tensors. Tensors flow through the graph, manipulated at each node by an operation. The output tensor of one operation often becomes the input tensor to a subsequent operation. TensorFlow implements a lazy execution model, meaning that nodes are only computed when needed, based on the needs of associated nodes.
Tensors can be stored in the graph as constants or variables. As you might guess, constants hold tensors whose values can't change, while variables hold tensors whose values can change. However, what you may not have guessed is that constants and variables are just more operations in the graph. A constant is an operation that always returns the same tensor value. A variable is an operation that will return whichever tensor has been assigned to it.
To define a constant, use the
tf.constant operator and pass in its value. For example:
x = tf.constant(5.2)
Similarly, you can create a variable like this:
y = tf.Variable()
Or you can create the variable first and then subsequently assign a value like this (note that you always have to specify a default value):
y = tf.Variable() y = y.assign()
Once you've defined some constants or variables, you can combine them with other operations like
tf.add. When you evaluate the
tf.add operation, it will call your
tf.Variable operations to get their values and then return a new tensor with the sum of those values.
Graphs must run within a TensorFlow session, which holds the state for the graph(s) it runs:
with tf.Session() as sess: initialization = tf.global_variables_initializer() print(y.eval())
When working with
tf.Variables, you must explicitly initialize them by calling
tf.global_variables_initializer at the start of your session, as shown above.
Note: A session can distribute graph execution across multiple machines (assuming the program is run on some distributed computation framework). For more information, see Distributed TensorFlow.
TensorFlow programming is essentially a two-step process:
As with nearly all Python programs, you'll begin by specifying some
The set of
import statements required to run a TensorFlow program depends, of course, on the features your program will access. At a minimum, you must provide the
import tensorflow statement in all TensorFlow programs:
In :import tensorflow as tf
Don't forget to execute the preceding code block (the
Other common import statements include the following:
import matplotlib.pyplot as plt # Dataset visualization. import numpy as np # Low-level numerical Python library. import pandas as pd # Higher-level numerical Python library.
TensorFlow provides a default graph. However, we recommend explicitly creating your own
Graph instead to facilitate tracking state (e.g., you may wish to work with a different
Graph in each cell).
In :from __future__ import print_function import tensorflow as tf # Create a graph. g = tf.Graph() # Establish the graph as the "default" graph. with g.as_default(): # Assemble a graph consisting of the following three operations: # * Two tf.constant operations to create the operands. # * One tf.add operation to add the two operands. x = tf.constant(8, name="x_const") y = tf.constant(5, name="y_const") my_sum = tf.add(x, y, name="x_y_sum") # Now create a session. # The session will run the default graph. with tf.Session() as sess: print(my_sum.eval())
Revise the above code listing to add three integers, instead of two:
z, and assign it a value of
my_sum to yield a new sum.
Hint: See the API docs for tf.add() for more details on its function signature.
Re-run the modified code block. Did the program generate the correct grand total?
In :# Create a graph. g = tf.Graph() # Establish our graph as the "default" graph. with g.as_default(): # Assemble a graph consisting of three operations. # (Creating a tensor is an operation.) x = tf.constant(8, name="x_const") y = tf.constant(5, name="y_const") my_sum = tf.add(x, y, name="x_y_sum") # Task 1: Define a third scalar integer constant z. z = tf.constant(4, name="z_const") # Task 2: Add z to `my_sum` to yield a new sum. new_sum = tf.add(my_sum, z, name="x_y_z_sum") # Now create a session. # The session will run the default graph. with tf.Session() as sess: # Task 3: Ensure the program yields the correct grand total. print(new_sum.eval())