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In this Keras article, we will learn about Implementing Dropout Regularization in Deep Learning Models with Keras. Dropout is a simple and powerful regularization technique for neural networks and deep learning models.

In this post, you will discover the Dropout regularization technique and how to apply it to your models in Python with Keras.

After reading this post, you will know:

- How the Dropout regularization technique works
- How to use Dropout on your input layers
- How to use Dropout on your hidden layers
- How to tune the dropout level on your problem

Dropout is a regularization technique for neural network models proposed by Srivastava et al. in their 2014 paper “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” (download the PDF).

Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped out” randomly. This means that their contribution to the activation of downstream neurons is temporally removed on the forward pass, and any weight updates are not applied to the neuron on the backward pass.

As a neural network learns, neuron weights settle into their context within the network. Weights of neurons are tuned for specific features, providing some specialization. Neighboring neurons come to rely on this specialization, which, if taken too far, can result in a fragile model too specialized for the training data. This reliance on context for a neuron during training is referred to as complex co-adaptations.

You can imagine that if neurons are randomly dropped out of the network during training, other neurons will have to step in and handle the representation required to make predictions for the missing neurons. This is believed to result in multiple independent internal representations being learned by the network.

The effect is that the network becomes less sensitive to the specific weights of neurons. This, in turn, results in a network capable of better generalization and less likely to overfit the training data.

Dropout is easily implemented by randomly selecting nodes to be dropped out with a given probability (e.g., 20%) in each weight update cycle. This is how Dropout is implemented in Keras. Dropout is only used during the training of a model and is not used when evaluating the skill of the model.

Next, let’s explore a few different ways of using Dropout in Keras.

The examples will use the Sonar dataset. This is a binary classification problem that aims to correctly identify rocks and mock-mines from sonar chirp returns. It is a good test dataset for neural networks because all the input values are numerical and have the same scale.

The dataset can be downloaded from the UCI Machine Learning repository. You can place the sonar dataset in your current working directory with the file name *sonar.csv*.

You will evaluate the developed models using scikit-learn with 10-fold cross validation in order to tease out differences in the results better.

There are 60 input values and a single output value. The input values are standardized before being used in the network. The baseline neural network model has two hidden layers, the first with 60 units and the second with 30. Stochastic gradient descent is used to train the model with a relatively low learning rate and momentum.

The full baseline model is listed below:

```
# Baseline Model on the Sonar Dataset
from pandas import read_csv
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD
from scikeras.wrappers import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# load dataset
dataframe = read_csv("sonar.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# baseline
def create_baseline():
# create model
model = Sequential()
model.add(Dense(60, input_shape=(60,), activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
sgd = SGD(learning_rate=0.01, momentum=0.8)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(model=create_baseline, epochs=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
```

**Note**: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example generates an estimated classification accuracy of 86%.

`Baseline: 86.04% (4.58%)`

Dropout can be applied to input neurons called the visible layer.

In the example below, a new Dropout layer between the input (or visible layer) and the first hidden layer was added. The dropout rate is set to 20%, meaning one in five inputs will be randomly excluded from each update cycle.

Additionally, as recommended in the original paper on Dropout, a constraint is imposed on the weights for each hidden layer, ensuring that the maximum norm of the weights does not exceed a value of 3. This is done by setting the kernel_constraint argument on the Dense class when constructing the layers.

The learning rate was lifted by one order of magnitude, and the momentum was increased to 0.9. These increases in the learning rate were also recommended in the original Dropout paper.

Continuing from the baseline example above, the code below exercises the same network with input dropout:

```
# Example of Dropout on the Sonar Dataset: Visible Layer
from pandas import read_csv
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.constraints import MaxNorm
from tensorflow.keras.optimizers import SGD
from scikeras.wrappers import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# load dataset
dataframe = read_csv("sonar.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# dropout in the input layer with weight constraint
def create_model():
# create model
model = Sequential()
model.add(Dropout(0.2, input_shape=(60,)))
model.add(Dense(60, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dense(30, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dense(1, activation='sigmoid'))
# Compile model
sgd = SGD(learning_rate=0.1, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(model=create_model, epochs=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Visible: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
```

**Note**: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Running the example provides a slight drop in classification accuracy, at least on a single test run.

`Visible: 83.52% (7.68%)`

Dropout can be applied to hidden neurons in the body of your network model.

In the example below, Dropout is applied between the two hidden layers and between the last hidden layer and the output layer. Again a dropout rate of 20% is used as is a weight constraint on those layers.

```
# Example of Dropout on the Sonar Dataset: Hidden Layer
from pandas import read_csv
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.constraints import MaxNorm
from tensorflow.keras.optimizers import SGD
from scikeras.wrappers import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# load dataset
dataframe = read_csv("sonar.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# dropout in hidden layers with weight constraint
def create_model():
# create model
model = Sequential()
model.add(Dense(60, input_shape=(60,), activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
# Compile model
sgd = SGD(learning_rate=0.1, momentum=0.9)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasClassifier(model=create_model, epochs=300, batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Hidden: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
```

**Note**: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

You can see that for this problem and the chosen network configuration, using Dropout in the hidden layers did not lift performance. In fact, performance was worse than the baseline.

It is possible that additional training epochs are required or that further tuning is required to the learning rate.

`Hidden: 83.59% (7.31%)`

Dropout will randomly reset some of the input to zero. If you wonder what happens after you have finished training, the answer is nothing! In Keras, a layer can tell if the model is running in training mode or not. The Dropout layer will randomly reset some input only when the model runs for training. Otherwise, the Dropout layer works as a scaler to multiply all input by a factor such that the next layer will see input similar in scale. Precisely, if the dropout rate is r, the input will be scaled by a factor of 1−r.

The original paper on Dropout provides experimental results on a suite of standard machine learning problems. As a result, they provide a number of useful heuristics to consider when using Dropout in practice.

- Generally, use a small dropout value of 20%-50% of neurons, with 20% providing a good starting point. A probability too low has minimal effect, and a value too high results in under-learning by the network.
- Use a larger network. You are likely to get better performance when Dropout is used on a larger network, giving the model more of an opportunity to learn independent representations.
- Use Dropout on incoming (visible) as well as hidden units. Application of Dropout at each layer of the network has shown good results.
- Use a large learning rate with decay and a large momentum. Increase your learning rate by a factor of 10 to 100 and use a high momentum value of 0.9 or 0.99.
- Constrain the size of network weights. A large learning rate can result in very large network weights. Imposing a constraint on the size of network weights, such as max-norm regularization, with a size of 4 or 5 has been shown to improve results.

Original article sourced at: https://machinelearningmastery.com

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1595422560

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Keras has the following characteristics:

- It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
- Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
- Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
- It offers a consistent API that provides necessary feedback when an error occurs.
- Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

The following major benefits of using Keras over other deep learning frameworks are:

- The simple API structure of Keras is designed for both new developers and experts.
- The Keras interface is very user friendly and is pretty optimized for general use cases.
- In Keras, you can write custom blocks to extend it.
- Keras is the second most popular deep learning framework after TensorFlow.
- Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

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The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

**Also Read:** Why Deep Learning DevCon Comes At The Right Time

**By Dipanjan Sarkar**

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

**By Divye Singh**

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

**By Dongsuk Hong**

**About:** This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

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The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

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This article is the spotlight on the need for python deep learning library, Keras. Keras offers a uniform face for various deep learning frameworks including Tensorflow, Theano, and MXNet. Let us see why you should choose and learn keras now.

Keras makes deep learning accessible and local on your computer.It also acts as a frontend for other big cloud providers. It is the most voted recommendation for beginners who want to start their journey in machine learning. It provides a minimal approach to run neural networks. This allows students to learn complex features from input data sequentially.

Let us see some of the features of keras that make you learn Keras.

Keras is the most easy to use the library for machine learning for beginners. Being simple helps it to bring machine learning from imaginations to reality. It provides an infrastructure that can be learned in very less time. Using Keras, you will be able to stack layers like experts.

Python is the most popular library for machine learning and Data Science. The compatibility with python allows Keras to have many useful features. Writing less code, easy to debug, easy to deploy, extensibility is due to the support of Keras with python 2.7 and python 3.6.

Keras being a high-level API provides support for multiple popular and powerful backend frameworks. Tensorflow, theano, CNTK are very dominant for backend computations and Keras supports all of them.

The importance of Keras leads to many other innovative tools to explore deep learning. These tools are built on top of Keras making Keras as the base. The following tools are:

- Deepjazz: This is deep learning-driven jazz built using Keras and theano, available on github.
- Eclipse Picasso: It is a visualization tool that works with Keras checkpoints.
- Auto Keras: It is built upon Keras and used for machine learning model automation.

- Keras allows us to switch between the backends as per the requirement of our applications. It acts as a wrapper that gives us the privilege to use either TensorFlow, theano, or any other framework.
- Keras is very easy and enjoyable to use. It uses great guiding principles like extensibility, python nativeness, and modularity.
- The ability of Keras to create the state of the art implementations of common deep neural networks. These are fast and it is easy to get them running using Keras.
- Being Keras user, you will be more faster and productive, you will have the ability to try more ideas.
- Keras provides Multi-GPU and strong distributed support. We can run our deep learning models on large GPU clusters.
- We can deploy Keras deep learning models on multiple platforms. For example, We can deploy in the browser using tensorflow.js, on the server using either TensorFlow serving or using Node.js runtime. On mobile devices i.e in android or IOS, we can deploy using TensorFlow Lite.
- Keras has a large ecosystem of products to support your deep learning development. Some of the popular products are Tensorflow Cloud, Keras Tuner, Tensorflow Lite,Tensorflow.js, and Tensorflow Model Optimizatio

#keras tutorials #importance of keras #keras features #learn keras #deep learning