What is Softmax Categorical Crossentropy in TF learn explain with example

This recipe explains what is Softmax Categorical Crossentropy TF learn with example

Recipe Objective

This recipe explains what is Softmax Categorical Crossentropy.

Sentiment Analysis Project on eCommerce Product Reviews with Source Code

Step 1: Importing Libraries

We'll import tflearn, tensorflow as tf and tflearn.datasets.mnist as mnist.

import tflearn
import tensorflow as tf
import tflearn.datasets.mnist as mnist
from __future__ import division, print_function, absolute_import

Step 2: Building Model

We have combined TFLearn built-in ops with Tensorflow graph. We have built this using MNIST Dataset.
To create a multilayer perceptron we have used TFLearn PReLU activations ops.

with tf.Graph().as_default():

    x = tf.placeholder("float", [None, 392])
    y = tf.placeholder("float", [None, 5])

    u = tf.Variable(tf.random_normal([392, 128]))
    v = tf.Variable(tf.random_normal([128, 128]))
    w = tf.Variable(tf.random_normal([128, 5]))
    a = tf.Variable(tf.random_normal([128]))
    b = tf.Variable(tf.random_normal([128]))
    c = tf.Variable(tf.random_normal([5]))

    def net(X):
       X = tflearn.prelu(tf.add(tf.matmul(X, u), a))
       tflearn.summaries.monitor_activation(x)
       X = tflearn.prelu(tf.add(tf.matmul(X, v), b))
       tflearn.summaries.monitor_activation(x)
       X = tf.nn.softmax(tf.add(tf.matmul(X, w), c))
       return X

    my_net = net(x)

Step 3: Softmax Categorical Crossentropy

It's syntax is : tflearn.objectives.softmax_categorical_crossentropy (y_pred, y_true). Its argument are y_pred which is predicted values and y_true which is a probability distribution.
It Computes softmax cross entropy between my_net (logits) and y (labels) and the probability error in indivisual classification tasks in which each entry is in exactly one class.

Loss = tflearn.softmax_categorical_crossentropy(my_net, y)
accuracy = tflearn.metrics.accuracy_op(my_net, y)

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Build a CNN Model with PyTorch for Image Classification
In this deep learning project, you will learn how to build an Image Classification Model using PyTorch CNN

End-to-End Snowflake Healthcare Analytics Project on AWS-1
In this Snowflake Healthcare Analytics Project, you will leverage Snowflake on AWS to predict patient length of stay (LOS) in hospitals. The prediction of LOS can help in efficient resource allocation, lower the risk of staff/visitor infections, and improve overall hospital functioning.

Build a Autoregressive and Moving Average Time Series Model
In this time series project, you will learn to build Autoregressive and Moving Average Time Series Models to forecast future readings, optimize performance, and harness the power of predictive analytics for sensor data.

AWS Project to Build and Deploy LSTM Model with Sagemaker
In this AWS Sagemaker Project, you will learn to build a LSTM model on Sagemaker for sales forecasting while analyzing the impact of weather conditions on Sales.

PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

BERT Text Classification using DistilBERT and ALBERT Models
This Project Explains how to perform Text Classification using ALBERT and DistilBERT

Digit Recognition using CNN for MNIST Dataset in Python
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.