Dicanio Rol

Dicanio Rol

1601274642

Keras Loss Functions: Everything You Need To Know

You’ve created a deep learning model in Keras, you prepared the data and now you are wondering which loss you should choose for your problem.

We’ll get to that in a second but first what is a loss function?

In deep learning, the loss is computed to get the gradients with respect to model weights and update those weights accordingly via backpropagation. Loss is calculated and the network is updated after every iteration until model updates don’t bring any improvement in the desired evaluation metric.

So while you keep using the same evaluation metric like f1 score or AUC on the validation set during (long parts) of your machine learning project, the loss can be changed, adjusted and modified to get the best evaluation metric performance.

You can think of the loss function just like you think about the model architecture or the optimizer and it is important to put some thought into choosing it. In this piece we’ll look at:

  • **loss functions available in Keras **and how to use them,
  • how you can define your own custom loss function in Keras,
  • how to add** sample weighing** to create observation-sensitive losses,
  • how to avoid nans in the loss,
  • **how you can monitor the loss function **via plotting and callbacks.

Let’s get into it!

#keras

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Buddha Community

Keras Loss Functions: Everything You Need To Know

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

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 Tutorial

Introduction to Keras

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.

Characteristics of Keras

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.

Benefits of using Keras

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.

Keras Installation

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.

Starting with Keras

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.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

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

Tia  Gottlieb

Tia Gottlieb

1598258520

Activation Functions, Optimization Techniques, and Loss Functions

Activation Functions:

A significant piece of a neural system Activation function is numerical conditions that decide the yield of a neural system. The capacity is joined to every neuron in the system and decides if it ought to be initiated (“fired”) or not, founded on whether every neuron’s info is applicable for the model’s expectation. Initiation works likewise help standardize the yield of every neuron to a range somewhere in the range of 1 and 0 or between — 1 and 1.

Progressively, neural systems use linear and non-linear activation functions, which can enable the system to learn complex information, figure and adapt practically any capacity speaking to an inquiry, and give precise forecasts.

Linear Activation Functions:

**Step-Up: **Activation functions are dynamic units of neural systems. They figure the net yield of a neural node. In this, Heaviside step work is one of the most widely recognized initiation work in neural systems. The capacity produces paired yield. That is the motivation behind why it is additionally called paired advanced capacity.

The capacity produces 1 (or valid) when info passes edge limit though it produces 0 (or bogus) when information doesn’t pass edge. That is the reason, they are extremely valuable for paired order studies. Every rationale capacity can be actualized by neural systems. In this way, step work is usually utilized in crude neural systems without concealed layer or generally referred to name as single-layer perceptions.

#machine-learning #activation-functions #loss-function #optimization-algorithms #towards-data-science #function

Vincent Lab

Vincent Lab

1605017502

The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences
this.name = "Bob";

const person = {
name: “Jon”,

<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);
},

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);
}

}

person.func1(); // Call the Regular function
// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}

person.func2(); // Call the Arrow function
// Output: {name:“Bob”}

The new keyword with an arrow function
const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions

Chumarat Pat

Chumarat Pat

1595233073

Keras Loss Functions: Everything You Need To Know

You’ve created a deep learning model in keras, you prepared the data and now you are wondering which loss you should choose for your problem.

We’ll get to that in a second but first what is a loss function?

In deep learning, the loss is computed to get the gradients with respect to model weights and update those weights accordingly via backpropagation. Loss is calculated and the network is updated after every iteration until model updates don’t bring any improvement in the desired evaluation metric.

So while you keep using the same evaluation metric like f1 score or AUC on the validation set during (long parts) of your machine learning project, the loss can be changed, adjusted and modified to get the best evaluation metric performance.

You can think of the loss function just like you think about the model architecture or the optimizer and it is important to put some thought into choosing it. In this piece we’ll look at:

  • **loss functions available in Keras **and how to use them,
  • how you can define your own custom loss function in Keras,
  • how to add** sample weighing** to create observation-sensitive losses
  • how to avoid nans in the loss
  • **how you can monitor the loss function **via plotting and callbacks.

Let’s get into it!

Keras Loss Functions 101

In Keras, loss functions are passed during the compile stage as shown below.

In this example, we’re defining the loss function by creating an instance of the loss class. Using the class is advantageous because you can pass some additional parameters.

from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential()
model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(layers.Activation('softmax'))

loss_function = keras.losses.SparseCategoricalCrossentropy(from_logits=False)
model.compile(loss=loss_function, optimizer='adam')

If you want to use a loss function that is built into Keras without specifying any parameters you can just use the string alias as shown below:

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')

You might be wondering, how does one decide on which loss function to use?

There are various loss functions available in Keras. Other times you might have to implement your own custom loss functions.

Let’s dive into all those scenarios.

#deep learning #keras

Dicanio Rol

Dicanio Rol

1601274642

Keras Loss Functions: Everything You Need To Know

You’ve created a deep learning model in Keras, you prepared the data and now you are wondering which loss you should choose for your problem.

We’ll get to that in a second but first what is a loss function?

In deep learning, the loss is computed to get the gradients with respect to model weights and update those weights accordingly via backpropagation. Loss is calculated and the network is updated after every iteration until model updates don’t bring any improvement in the desired evaluation metric.

So while you keep using the same evaluation metric like f1 score or AUC on the validation set during (long parts) of your machine learning project, the loss can be changed, adjusted and modified to get the best evaluation metric performance.

You can think of the loss function just like you think about the model architecture or the optimizer and it is important to put some thought into choosing it. In this piece we’ll look at:

  • **loss functions available in Keras **and how to use them,
  • how you can define your own custom loss function in Keras,
  • how to add** sample weighing** to create observation-sensitive losses,
  • how to avoid nans in the loss,
  • **how you can monitor the loss function **via plotting and callbacks.

Let’s get into it!

#keras