My Blog

Author: saqibkhan

  • MongoDB Create Database

    To create a database in MongoDB, First create a MongoClient object and specify a connection URL with the correct ip address and the name of the database which you want to create.

    Note: MongoDB will automatically create the database if it does not exist, and make a connection to it.

    Example

    Create a folder named “MongoDatabase” as a database. Suppose you create it on Desktop. Create a js file named “createdatabase.js” within that folder and having the following code:

    var MongoClient = require('mongodb').MongoClient;  

var url = "mongodb://localhost:27017/MongoDatabase";  

MongoClient.connect(url, function(err, db) {  

if (err) throw err;  

console.log("Database created!");  

db.close();  

});  

      Now open the command terminal and set the path where MongoDatabase exists. Now execute the following command:

      Node createdatabase.js  
      Node.js Create database 1

    1. Create Connection with MongoDB

      MongoDb is a NoSQL database. It can be used with Node.js as a database to insert and retrieve data.

      Download MongoDB

      Open the Linux Command Terminal and execute the following command:

      apt-get install mongodb  

      It will download the latest MongoDB according to your system requirement.

      Node.js Create connection 1

      Install MongoDB

      After the complete download, use the following command to install MogoDB.

      npm install mongodb --save   

      Use the following command to start MongoDb services:

      service mongodb start  
      Node.js Create connection 2

    2. Use Data Augmentation to Avoid Overfitting

      • Data augmentation can improve generalization and avoid overfitting, especially when your dataset is small.pythonCopy codefrom tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest' )

    3. Node.js MySQL Drop Table

      The DROP TABLE command is used to delete or drop a table.

      Let’s drop a table named employee2.

      Create a js file named “delete” in DBexample folder and put the following data into it:

      var mysql = require('mysql');  

var con = mysql.createConnection({  

host: "localhost",  

user: "root",  

password: "12345",  

database: "javatpoint"  

});  

con.connect(function(err) {  

if (err) throw err;  

var sql = "DROP TABLE employee2";  

con.query(sql, function (err, result) {  

if (err) throw err;  

console.log("Table deleted");  

});  

});  

        Now open command terminal and run the following command:

        Node drop.js  
        Node.js drop table 1

        Verify that the table employee2 is no more in the database.

        Node.js drop table 2

      1. Leverage Pretrained Models

        • Keras offers many pretrained models that can save you time and resources when working on tasks like image classification or feature extraction.pythonCopy codefrom tensorflow.keras.applications import VGG16 base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

      2. Node.js MySQL SELECT Unique Record

        (WHERE Clause)

        Retrieve a unique data from the table “employees”.

        Create a js file named selectwhere.js having the following data in DBexample folder.

        var mysql = require('mysql');  

var con = mysql.createConnection({  

host: "localhost",  

user: "root",  

password: "12345",  

database: "javatpoint"  

});  

con.connect(function(err) {  

if (err) throw err;  

con.query("SELECT * FROM employees WHERE id = '1'", function (err, result) {  

if (err) throw err;  

console.log(result);  

});  

}); 

          Now open command terminal and run the following command:

          Node selectwhere.js  
          Node.js unique record 1

          Node.js MySQL Select Wildcard

          Retrieve a unique data by using wildcard from the table “employees”.

          Create a js file named selectwildcard.js having the following data in DBexample folder.

          var mysql = require('mysql');  

var con = mysql.createConnection({  

host: "localhost",  

user: "root",  

password: "12345",  

database: "javatpoint"  

});  

con.connect(function(err) {  

if (err) throw err;  

con.query("SELECT * FROM employees WHERE city LIKE 'A%'", function (err, result) {  

if (err) throw err;  

console.log(result);  

});  

});  

            Now open command terminal and run the following command:

            Node selectwildcard.js  

            It will retrieve the record where city start with A.

            Node.js unique record 2

          1. Node.js MySQL Select Records

            Example

            Retrieve all data from the table “employees”.

            Create a js file named select.js having the following data in DBexample folder.

            var mysql = require('mysql');  

var con = mysql.createConnection({  

host: "localhost",  

user: "root",  

password: "12345",  

database: "javatpoint"  

});  

con.connect(function(err) {  

if (err) throw err;  

con.query("SELECT * FROM employees", function (err, result) {  

if (err) throw err;  

console.log(result);  

});  

}); 

              Now open command terminal and run the following command:

              Node select.js  
              Node.js select record 1

              You can also use the statement:

              SELECT * FROM employees;  
              Node.js select record 2

            1. Use Callbacks

              • Callbacks can help improve your training process, monitor metrics, and avoid overfitting.
              • Common callbacks include:
                • EarlyStopping: Stop training when a monitored metric has stopped improving.
                • ModelCheckpoint: Save the best model during training.
                • ReduceLROnPlateau: Reduce the learning rate when a metric has stopped improving.
                pythonCopy codefrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau callbacks = [ EarlyStopping(monitor='val_loss', patience=3), ModelCheckpoint('best_model.h5', save_best_only=True), ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2) ] model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, callbacks=callb

            2. Set Random Seed for Reproducibility

              • To ensure that your results are reproducible, you can set a random seed at the start of your code:pythonCopy codeimport numpy as np import tensorflow as tf np.random.seed(42) tf.random.set_seed(42)

            3. Graph Neural Networks (GNN):

              • Graph Convolutional Networks (GCNs): Using GCNs for node classification, link prediction, and other graph-based tasks. GNNs operate directly on graph structures, learning from node features and their neighbors.
              pythonCopy codeimport tensorflow as tf
from tensorflow.keras import layers

class GraphConvolution(layers.Layer):

              def call(self, inputs):
    adjacency_matrix, features = inputs
    aggregated_features = tf.matmul(adjacency_matrix, features)
    return aggregated_features
              
inputs = layers.Input(shape=(num_nodes, feature_dim))
adj_matrix = layers.Input(shape=(num_nodes, num_nodes))
gcn_layer = GraphConvolution()([adj_matrix, inputs])
output = layers.Dense(num_classes, activation='softmax')(gcn_layer)

model = models.Model([inputs, adj_matrix], output)