AI News, R Interface to the Keras Deep Learning Library

R Interface to the Keras Deep Learning Library

To show the power of neural networks we need a larger dataset to make use of.

A popular first dataset for applying neural networks is the MNIST Handwriting dataset, consisting of small black and white scans of handwritten numeric digits (0-9).

The values are pixel intensities between 0 and 255, so we will also normalize the values to be between 0 and 1: Finally, we want to process the response vector y into a different format as well.

These are fairly well-known choices for a simple dense neural network and allow us to show off many of the possibilities within the kerasR interface: We then compile the model with the “categorical_crossentropy” loss and fit it on the training data: Now that the model is trained, we could use the function keras_predict once again, however this would give us an output matrix with 10 columns.

It’s possible to get slightly higher with strictly dense layers by employing additional tricks and using larger models with more regularization.

Build your First Deep Learning Neural Network Model using Keras in Python

I have chosen my today’s topic as Neural Network because it is most the fascinating learning model in the world of data science and starters in Data Science think that Neural Network is difficult and its understanding requires knowledge of neurons, perceptron and blaahhhh…There is nothing like that, I have been working on Neural Network for quite a few month now and realized that it is so easy.

The fact of the matter is Keras is built on top of Tensorflow and Theano so this 2 insane library will be running in back-end whenever you run the program in Keras.

Deep understanding of NN(you can skip this if you don’t want to learn in depth) Now you can see that Country names are replaced by 0,1 and 2 while male and female are replaced by 0 and 1.

Dummy variable is difficult concept if you read in depth but don’t take tension, I have found this simple resource which will help you in understanding.

In Machine Learning, we always divide our data into training and testing part meaning that we train our model on training data and then we check the accuracy of a model on testing data.

Here we are using rectifier(relu) function in our hidden layer and Sigmoid function in our output layer as we want binary result from output layer but if the number of categories in output layer is more than 2 then use SoftMax function.

First argument is Optimizer, this is nothing but the algorithm you wanna use to find optimal set of weights(Note that in step 9 we just initialized weights now we are applying some sort of algorithm which will optimize weights in turn making out neural network more powerful.

Since out dependent variable is binary, we will have to use logarithmic loss function called ‘binary_crossentropy’, if our dependent variable has more than 2 categories in output then use ‘categorical_crossentropy’.

Training, test, and validation sets

Most approaches that search through training data for empirical relationships tend to overfit the data, meaning that they can identify apparent relationships in the training data that do not hold in general.

For example, if the most suitable classifier for the problem is sought, the training dataset is used to train the candidate algorithms, the validation dataset is used to compare their performances and decide which one to take and, finally, the test dataset is used to obtain[citation needed]

Since our goal is to find the network having the best performance on new data, the simplest approach to the comparison of different networks is to evaluate the error function using data which is independent of that used for training.

The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set is selected.

Since this procedure can itself lead to some overfitting to the validation set, the performance of the selected network should be confirmed by measuring its performance on a third independent set of data called a test set.

An application of this process is in early stopping, where the candidate models are successive iterations of the same network, and training stops when the error on the validation set grows, choosing the previous model (the one with minimum error).

These repeated partitions can be done in various ways, such as dividing into 2 equal datasets and using them as training/validation, and then validation/training, or repeatedly selecting a random subset as a validation dataset[citation needed].

For example, for an image classifier, if you have a series images in the dataset that are similar and you put half in test and half in training, you will end up inflating the performance metrics of your classifier.

Another example of parameter adjustment is hierarchical classification (sometimes referred to as instance space decomposition [11]), which splits a complete multi-class problem into a set of smaller classification problems.

on the validation set one can see which classes are most frequently mutually confused by the system and then the instance space decomposition is done as follows: firstly, the classification is done among well recognizable classes, and the difficult to separate classes are treated as a single joint class, and finally, as a second classification step the joint class is classified into the two initially mutually confused classes.

Using Keras Pre-trained Deep Learning models for your own dataset

#100DaysOfMLCode In this blog post, we will quickly understand how to use state-of-the-art Deep Learning models in Keras to solve a supervised image classification problem using our own dataset with/without GPU acceleration.

Instead of creating and training deep neural nets from scratch (which takes time and involves many iterations), what if we use the pre-trained weights of these deep neural net architectures (trained on ImageNet dataset) and use it for our own dataset?

Developed by François Chollet, it offers simple understandable functions and syntax to start building Deep Neural Nets right away instead of worrying too much on the programming part.

These activations will be acting as the feature vector for a machine learning model (classifier) which further learns to classify it.

This type of approach is well suited for Image Classification problems, where instead of training a CNN from scratch (which is time-consuming and tedious), a pre-trained CNN could be used as a Feature Extractor - Transfer Learning.

Below is the table that shows image size, weights size, top-1 accuracy, top-5 accuracy, no.of.parameters and depth of each deep neural net architecture available in Keras.

To build our training dataset, we need to create a master folder named dataset, inside which we need to create two more folders namely train and test.

To automate this task, I have a script that takes in input path that has all the 1360 images and dumps 17 folders inside train folder.

You can extract features from any arbitrary layer using the layer name (eg: flatten), by checking the .py file of each of the model residing inside the applications directory of Keras.

The below table shows the feature vector size for each image for a particular deep neural net model that I used.

The below tables shows the accuracies obtained for each pretrained model used to extract features from FLOWERS17 dataset using different configuration settings.

This could mainly be due to the presence of 9 network-in-a-network modules codenamed as Inception modules which applies different convolutional filter sizes parallely to an input volume and concatenates the result at output.

Our system is able to obtain much higher accuracies than state-of-the-art accuracies (which mostly used hand-crafted features for shape, color and texture representations) in this FLOWERS17 dataset.

Using this procedure, you could use these pretrained models for your own image dataset and reduce the time consumed to construct a deep neural net from scratch.

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