Random Neural Network Model Creation with Keras

This function creates a random neural network model based on Keras, which accepts input shape and number of classes as parameters and returns the compiled model.

Technology Stack : Keras, Numpy

Code Type : Create a random neural network model

Code Difficulty : Intermediate

                
                    
import numpy as np
from keras.layers import Input, Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical

def create_random_model(input_shape, num_classes):
    # Initialize the model
    model = Sequential()
    
    # Add an input layer
    model.add(Input(shape=input_shape))
    
    # Add a dense layer with 128 units and ReLU activation
    model.add(Dense(128, activation='relu'))
    
    # Add a dropout layer for regularization
    model.add(Dropout(0.5))
    
    # Flatten the output of the previous layer
    model.add(Flatten())
    
    # Add a dense layer with the number of classes as units and softmax activation
    model.add(Dense(num_classes, activation='softmax'))
    
    # Compile the model
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    return model