As we learned earlier, Keras modules contains pre-defined classes, functions and variables which are useful for deep learning algorithm. Let us learn the modules provided by Keras in this blog.

Available modules

Let us first see the list of modules available in the Keras.

Initializers − Provides a list of initializers function. We can learn it in details in Keras layer chapter. during model creation phase of machine learning.
Regularizers − Provides a list of regularizers function. We can learn it in details in Keras Layers chapter.
Constraints − Provides a list of constraints function. We can learn it in details in Keras Layers chapter.
Activations − Provides a list of activator function. We can learn it in details in Keras Layers chapter.
Losses − Provides a list of loss function. We can learn it in details in Model Training chapter.
Metrics − Provides a list of metrics function. We can learn it in details in Model Training chapter.
Optimizers − Provides a list of optimizer function. We can learn it in details in Model Training chapter.
Callback − Provides a list of callback function. We can use it during the training process to print the intermediate data as well as to stop the training itself (EarlyStopping method) based on some condition.
Text processing − Provides functions to convert text into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.
Image processing − Provides functions to convert images into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.
Sequence processing − Provides functions to generate time based data from the given input data. We can use it in data preparation phase of machine learning.
Backend − Provides function of the backend library like TensorFlow and Theano.
Utilities − Provides lot of utility function useful in deep learning.

Let us see backend module and utils model in this blog.

backend module

backend module is used for keras backend operations. By default, keras runs on top of TensorFlow backend. If you want, you can switch to other backends like Theano or CNTK. Defualt backend configuration is defined inside your root directory under .keras/keras.json file.

Keras backend module can be imported using below code

>>> from keras import backend as k
If we are using default backend TensorFlow, then the below function returns TensorFlow based information as specified below −
>>> k.backend()
'tensorflow'
>>> k.epsilon()
1e-07
>>> k.image_data_format()
'channels_last'
>>> k.floatx()
'float32'
Let us understand some of the significant backend functions used for data analysis in brief −
get_uid()
It is the identifier for the default graph. It is defined below −
>>> k.get_uid(prefix='')
1
>>> k.get_uid(prefix='') 2
reset_uids
It is used resets the uid value.
>>> k.reset_uids()
Now, again execute the get_uid(). This will be reset and change again to 1.
>>> k.get_uid(prefix='')
1
placeholder
It is used instantiates a placeholder tensor. Simple placeholder to hold 3-D shape is shown below −
>>> data = k.placeholder(shape = (1,3,3))
>>> data
<tf.Tensor 'Placeholder_9:0' shape = (1, 3, 3) dtype = float32>
If you use int_shape(), it will show the shape.
>>> k.int_shape(data) (1, 3, 3)
dot
It is used to multiply two tensors. Consider a and b are two tensors and c will be the outcome of multiply of ab. Assume a shape is (4,2) and b shape is (2,3). It is defined below,
>>> a = k.placeholder(shape = (4,2))
>>> b = k.placeholder(shape = (2,3))
>>> c = k.dot(a,b)
>>> c
<tf.Tensor 'MatMul_3:0' shape = (4, 3) dtype = float32>
>>>
ones
It is used to initialize all as one value.
>>> res = k.ones(shape = (2,2))
#print the value
>>> k.eval(res)
array([[1., 1.], [1., 1.]], dtype = float32)
batch_dot
It is used to perform the product of two data in batches. Input dimension must be 2 or higher. It is shown below −
>>> a_batch = k.ones(shape = (2,3))
>>> b_batch = k.ones(shape = (3,2))
>>> c_batch = k.batch_dot(a_batch,b_batch)
>>> c_batch
<tf.Tensor 'ExpandDims:0' shape = (2, 1) dtype = float32>
variable
It is used to initializes a variable. Let us perform simple transpose operation in this variable.
>>> data = k.variable([[10,20,30,40],[50,60,70,80]])
#variable initialized here
>>> result = k.transpose(data)
>>> print(result)
Tensor("transpose_6:0", shape = (4, 2), dtype = float32)
>>> print(k.eval(result))
[[10. 50.]
[20. 60.]
[30. 70.]
[40. 80.]]
If you want to access from numpy −
>>> data = np.array([[10,20,30,40],[50,60,70,80]])
>>> print(np.transpose(data))
[[10 50]
[20 60]
[30 70]
[40 80]]
>>> res = k.variable(value = data)
>>> print(res)
<tf.Variable 'Variable_7:0' shape = (2, 4) dtype = float32_ref>
is_sparse(tensor)
It is used to check whether the tensor is sparse or not.
>>> a = k.placeholder((2, 2), sparse=True)
>>> print(a) SparseTensor(indices =
Tensor("Placeholder_8:0",
shape = (?, 2), dtype = int64),
values = Tensor("Placeholder_7:0", shape = (?,),
dtype = float32), dense_shape = Tensor("Const:0", shape = (2,), dtype = int64))
>>> print(k.is_sparse(a)) True
to_dense()
It is used to converts sparse into dense.
>>> b = k.to_dense(a)
>>> print(b) Tensor("SparseToDense:0", shape = (2, 2), dtype = float32)
>>> print(k.is_sparse(b)) False
random_uniform_variable
It is used to initialize using uniform distribution concept.
k.random_uniform_variable(shape, mean, scale)
Here,
shape − denotes the rows and columns in the format of tuples.
mean − mean of uniform distribution.
scale − standard deviation of uniform distribution.
Let us have a look at the below example usage −
>>> a = k.random_uniform_variable(shape = (2, 3), low=0, high = 1)
>>> b = k. random_uniform_variable(shape = (3,2), low = 0, high = 1)
>>> c = k.dot(a, b)
>>> k.int_shape(c)
(2, 2)
utils module
utils provides useful utilities function for deep learning. Some of the methods provided by the utils module is as follows −
HDF5Matrix
It is used to represent the input data in HDF5 format.
from keras.utils import HDF5Matrix data = HDF5Matrix('data.hdf5', 'data')
to_categorical
It is used to convert class vector into binary class matrix.
>>> from keras.utils import to_categorical
>>> labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> to_categorical(labels)
array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype = float32)
>>> from keras.utils import normalize
>>> normalize([1, 2, 3, 4, 5])
array([[0.13483997, 0.26967994, 0.40451992, 0.53935989, 0.67419986]])
print_summary
It is used to print the summary of the model.
from keras.utils import print_summary print_summary(model)
plot_model
It is used to create the model representation in dot format and save it to file.
from keras.utils import plot_model
plot_model(model,to_file = 'image.png')
This plot_model will generate an image to understand the performance of model.

So, this brings us to the end of blog. This Tecklearn ‘Deep Dive into Modules provided by Keras Library’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

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