MNIST DATASET, TENSOWFLOW, KERAS, SEQUENTIAL MODEL, SIMPLE NEURAL NETWORK, MULTIPLE DENSE LAYERS

Table of Contents

• Introduction
• Get, split and shape the Data
• Model Types
• Sequential Model
• Functional Model

Introduction

I have played with MNIST dataset using Tensowflow and Keras. I have coded for sequential and functional, Neural Networks in different files. This Neural Network is simple. It has just Flatten Layer, 2 Dense Layers and Output Layers.

At the starting part of the coding we need to call the libraries.

Code:

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np

Get, split and shape the Data

Following code doenload MNIST dataset and split training and testing data

Code:

mydata = tf.keras.datasets.mnist

(x_train, y_train), (x_test,y_test) = mydata.load_data()
print (x_train.shape)
x_train, x_test = x_train/255, x_test/255

Model Types

You can cretae model by two ways.

• Sequential Way - Sequential Model.
• Functional Way - Functional Model.

Sequential Model

Sequential Model Code should be like below. I have created a separate file for Sequential Model (tf_mnist_sequential_model.py).

Code:

--- Create a sequential model
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')    
    ]) 

Functional Model

Functional Model Code should be like below. I have created a separate file for Functional Model (tf_mnist_functional_model.py).

Code:

--- Create a Functional model
--- Functional model has 3 parts. Input, Layers and Model

- Define input to the model
input_layer = tf.keras.layers.Input(shape=(28,28))

- Define a set of interconnected layers on the input
flatten_layer = tf.keras.layers.Flatten()(input_layer)
dense_layer1 = tf.keras.layers.Dense(128, activation=tf.nn.relu)(flatten_layer)
dense_layer2 = tf.keras.layers.Dense(64, activation=tf.nn.relu)(dense_layer1)
output_layer = tf.keras.layers.Dense(10, activation=tf.nn.softmax)(dense_layer2)

- Define the Model using input and output layers
functional_model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)

functional_model.summary()