Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data. Keras model provides a function, evaluate which does the evaluation of the model. It has three main arguments,

Test data
Test data label
verbose – true or false

Let us evaluate the model

score = model.evaluate(x_test, y_test, verbose = 0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Executing the above code will output the below information.
0

The test accuracy is 98.28%. We have created a best model to identify the handwriting digits. On the positive side, we can still scope to improve our model.

Model Prediction

Prediction is the final step and our expected outcome of the model generation. Keras provides a method, predict to get the prediction of the trained model. The signature of the predict method is as follows,

predict(
x,
batch_size = None,
verbose = 0,
steps = None,
callbacks = None,
max_queue_size = 10,
workers = 1,
use_multiprocessing = False
)
Here, all arguments are optional except the first argument, which refers the unknown input data. The shape should be maintained to get the proper prediction.
Let us do prediction for our MPL model created in previous chapter using below code −
pred = model.predict(x_test)
pred = np.argmax(pred, axis = 1)[:5]
label = np.argmax(y_test,axis = 1)[:5]
print(pred)
print(label)
Here,
Line 1 call the predict function using test data.
Line 2 gets the first five prediction
Line 3 gets the first five labels of the test data.
Line 5 - 6 prints the prediction and actual label.
The output of the above application is as follows −
[7 2 1 0 4]
[7 2 1 0 4]

The output of both array is identical and it indicate that our model predicts correctly the first five images.

So, this brings us to the end of blog. This Tecklearn ‘Model Evaluation and Model Prediction in Keras’ 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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Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

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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

Got a question for us? Please mention it in the comments section and we will get back to you.

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