Deep learning is one of the broader methods of machine learning which learns the high level features from the data itself. In deep learning, multiple artificial neurons stacked up as layers perform individual functions and serve as an input to the next layer.
TensorFlow
TensorFlow is software library for dataflow programming released by Google in 2015 which is used implementing deep learning models. TensorFlow first defines an abstract model which defines the computations, called the Computational Graph. The computational graph then runs within a session to make the model a reality. A Computational Graph is a series of TensorFlow operations arranged into a graph of nodes. When Computational Graph is defined all the operations are created without holding any values or running any calculations. Below is an example of an computational graph.
import tensorflow as tf node1 = tf.constant(3.0, tf.float32) node2 = tf.constant(4.0) node3 = node1 * node2 print(node1, node2, node3)
The TensorFlow session's allows to execute computational graph or a part of the graph, and producing actual results as shown below. The session encapsulates the control and state of the TensorFlow runtime.
session = tf.Session() print(session.run([node1, node2, node3])) session.close()
Data is represented in form of tensors in TensoreFlow. A tensor is a multi dimensional arrays or a lists, for example, an array is a 1-dimensional tensor, a matrix is 2-dimensional tensor, a three dimensional matrix is a 3-dimensional tensor. Tensors are described by a unit of dimensions called the rank.
| Rank | Math entity | Python example |
|---|---|---|
| 0 | Scalar (magnitude only) | s = 483 |
| 1 | Vector (magnitude and direction) | v = [1.1, 2.2, 3.3] |
| 2 | Matrix (table of numbers) | m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] |
| 3 | 3-Tensor (cube of numbers) | t = [[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]]] |
| n | n-Tensor | .... |
TensorBoard is a suite of web applications for visualizing and understanding TensoreFlow graphs. To create a TensorFlow graph FileWriter is used to output the graph to a directory.
session = tf.Session()
File = tf.summary.FileWriter('log_simple_graph', session.graph)
session.close()
TensorBoard runs as a local web app, on default port 6006 on executing the command tensorboard --logdir="path_to_the_graph".DataTypes in Tensorflow
Constant nodes takes no inputs and outputs the value it stores internally.
Placeholder is a parameter of the graph that can accept external inputs. It is a promise to provide a value later. Below is an example.
import tensorflow as tf
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b
session = tf.Session()
print(session.run(adder_node,{a: [1,3], b: [2,4]}))
session.close()
Variable allows to add a trainable parameters to a graph. They are used to hold and update parameters while training a model. Variable must be initialized before using them unlike constants and placeholders as below.
import tensorflow as tf
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b
init = tf.global_variables_initialzer()
session = tf.Session()
session.run(init)
print(session.run(linear_model, {x:[1,2,3,4]}))
session.close()
