swathika28 / mnist-classification

EX-3 Convolutional Deep Neural Network for Digit Classification

AIM

To Develop a convolutional deep neural network for digit classification and to verify the response for scanned handwritten images.

Problem Statement and Dataset

Digit classification and to verify the response for scanned handwritten images.

The MNIST dataset is a collection of handwritten digits. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The dataset has a collection of 60,000 handwrittend digits of size 28 X 28. Here we build a convolutional neural network model that is able to classify to it's appropriate numerical value.

Neural Network Model

Include the neural network model diagram.

DESIGN STEPS

STEP 1:

import tensorflow and preprocessing libraries

STEP 2:

Build a CNN model

STEP 3:

Compile and fit the model and then predict

PROGRAM

Name : SWATHIKA G
Reg No : 212221230113

import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import utils
from sklearn.metrics import classification_report,confusion_matrix
from tensorflow.keras.preprocessing import image

(xtrain, ytrain), (xtest, ytest) = mnist.load_data()

xtrain.shape

xtest.shape

single_image= xtrain[4]

single_image.shape

plt.imshow(single_image,cmap='gray')

ytrain.shape

xtrain.min()

xtrain.max()

xtrain_scaled = xtrain/255.0
xtest_scaled = xtest/255.0

xtrain_scaled.min()

xtrain_scaled.max()

ytrain[4]

ytrain_onehot = utils.to_categorical(ytrain,10)
ytest_onehot = utils.to_categorical(ytest,10)

type(ytrain_onehot)

ytrain_onehot.shape

single_image = xtrain[4]
plt.imshow(single_image,cmap='gray')

ytrain_onehot[4]

xtrain_scaled = xtrain_scaled.reshape(-1,28,28,1)
xtest_scaled = xtest_scaled.reshape(-1,28,28,1)

model= keras.Sequential()
model.add(layers.Input(shape=(28,28,1)))
model.add(layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(16,activation='relu'))
model.add(layers.Dense(32,activation='relu'))
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics='accuracy')

model.fit(xtrain_scaled ,ytrain_onehot, 
          epochs=8,batch_size=128, 
          validation_data=(xtest_scaled,ytest_onehot))

metrics = pd.DataFrame(model.history.history)
metrics.head()

metrics[['accuracy','val_accuracy']].plot()

metrics[['loss','val_loss']].plot()

xtest_predictions = np.argmax(model.predict(xtest_scaled), axis=1)

print(confusion_matrix(ytest,xtest_predictions))

print(classification_report(ytest,xtest_predictions))

img = image.load_img('dl.png')
type(img)
img = image.load_img('dl.png')
img_tensor = tf.convert_to_tensor(np.asarray(img))
img_28 = tf.image.resize(img_tensor,(28,28))
img_28_gray = tf.image.rgb_to_grayscale(img_28)
img_28_gray_scaled = img_28_gray.numpy()/255.0

x_single_prediction = np.argmax(model.predict(img_28_gray_scaled.reshape(1,28,28,1)),axis=1)

print(x_single_prediction)

plt.imshow(img_28_gray_scaled.reshape(28,28),cmap='gray')

img_28_gray_inverted = 255.0-img_28_gray
img_28_gray_inverted_scaled = img_28_gray_inverted.numpy()/255.0
plt.imshow(img_28_gray_inverted_scaled.reshape(28,28),cmap='gray')

x_single_prediction = np.argmax(model.predict(img_28_gray_inverted_scaled.reshape(1,28,28,1)), axis=1)

print(x_single_prediction)

OUTPUT

Training Loss, Validation Loss Vs Iteration Plot

Classification Report

Confusion Matrix

New Sample Data Prediction

RESULT :

Thus a convolutional deep neural network for digit classification and to verify the response for scanned handwritten images is written and executed successfully.