How to make predictions using keras model?

This recipe helps you make predictions using keras model

Recipe Objective

In machine learning, our main motive is to create a model that can predict the output from new data. We can do this by training the model.

So this recipe is a short example of how to make predictions using keras model?

Learn How to Build a Multi Class Text Classification Model using BERT

Step 1 - Import the library

import pandas as pd import numpy as np from keras.datasets import mnist from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout

We have imported pandas, numpy, mnist(which is the dataset), train_test_split, Sequential, Dense and Dropout. We will use these later in the recipe.

Step 2 - Loading the Dataset

Here we have used the inbuilt mnist dataset and stored the train data in X_train and y_train. We have used X_test and y_test to store the test data. (X_train, y_train), (X_test, y_test) = mnist.load_data()

Step 3 - Creating model and adding layers

We have created an object model for sequential model. We can use two args i.e layers and name. model = Sequential() Now, We are adding the layers by using 'add'. We can specify the type of layer, activation function to be used and many other things while adding the layer.
Here we have added four layers which will be connected one after other. model.add(Dense(512)) model.add(Dropout(0.3)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.2))

Step 4 - Compiling the model

We can compile a model by using compile attribute. Let us first look at its parameters before using it.

  • optimizer : In this, we can pass the optimizer we want to use. There is various optimizer like SGD, Adam etc.
  • loss : In this, we can pass a loss function which we want for the model
  • metrics : In this, we can pass the metric on which we want the model to be scored

model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])

Step 5 - Fitting the model

We can fit a model on the data we have and can use the model after that. Here we are using the data which we have splitted i.e the training data for fitting the model.
While fitting we can pass various parameters like batch_size, epochs, verbose, validation_data and so on. model.fit(X_train, y_train, batch_size=128, epochs=2, verbose=1, validation_data=(X_test, y_test)

Step 6 - Evaluating the model

After fitting a model we want to evaluate the model. Here we are using model.evaluate to evaluate the model and it will give us the loss and the accuracy. Here we have also printed the score. score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])

Step 7 - Predicting the output

Finally we are predicting the output for this we are using another part of the data that we get from test_train_split i.e. test data. We will use it and predict the output. y_pred = model.predict(X_test) print(y_pred) As an output we get:

Epoch 1/2
469/469 [==============================] - 7s 14ms/step - loss: 0.3174 - accuracy: 0.9033 - val_loss: 0.1212 - val_accuracy: 0.9630
Epoch 2/2
469/469 [==============================] - 6s 14ms/step - loss: 0.1560 - accuracy: 0.9534 - val_loss: 0.0918 - val_accuracy: 0.9720
Test loss: 0.09184003621339798
Test accuracy: 0.972000002861023

[[8.92436292e-10 1.32853462e-09 6.39653945e-06 ... 9.99989152e-01
  1.79315840e-09 2.44941958e-07]
 [9.11153306e-11 1.03196271e-05 9.99982357e-01 ... 1.89035987e-09
  9.82423032e-09 8.40081246e-14]
 [1.10766098e-06 9.99514341e-01 1.26151179e-04 ... 1.44331687e-04
  4.99823145e-05 6.05678633e-06]
 ...
 [2.03985762e-09 1.29704825e-08 2.95020914e-08 ... 1.23884201e-05
  6.87194824e-06 1.75449488e-04]
 [5.91818647e-08 1.97798578e-08 7.46679774e-10 ... 5.06311437e-09
  1.96506153e-04 1.14137793e-08]
 [1.13083731e-09 5.45665553e-12 2.54836174e-09 ... 3.70580059e-13
  6.02386641e-10 3.15489106e-12]]

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.

Build a Text Classification Model with Attention Mechanism NLP
In this NLP Project, you will learn to build a multi class text classification model with attention mechanism.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

Deep Learning Project for Text Detection in Images using Python
CV2 Text Detection Code for Images using Python -Build a CRNN deep learning model to predict the single-line text in a given image.

Linear Regression Model Project in Python for Beginners Part 2
Machine Learning Linear Regression Project for Beginners in Python to Build a Multiple Linear Regression Model on Soccer Player Dataset.

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

Recommender System Machine Learning Project for Beginners-2
Recommender System Machine Learning Project for Beginners Part 2- Learn how to build a recommender system for market basket analysis using association rule mining.

Topic modelling using Kmeans clustering to group customer reviews
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.