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

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

Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import classification_report,confusion_matrix

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
from tensorflow.keras import utils
from tensorflow.keras.preprocessing import image

One Hot Encoding Outputs

y_train_onehot = utils.to_categorical(y_train,10)
y_test_onehot = utils.to_categorical(y_test,10)

Reshape Inputs

X_train_scaled = X_train_scaled.reshape(-1,28,28,1)
X_test_scaled = X_test_scaled.reshape(-1,28,28,1)

Build CNN Model

model = keras.Sequential()
input = keras.Input(shape=(28,28,1))
model.add(input)

model.add(layers.Conv2D(filters=32,kernel_size=(5,5),
			strides=(1,1),padding='valid',activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2,2)))
model.add(layers.Conv2D(filters=64,kernel_size=(5,5),
			strides=(1,1),padding='same',activation='relu'))
model.add(layers.MaxPool2D(pool_size=(2,2)))
model.add(layers.Flatten())
model.add(layers.Dense(20,activation='relu'))
model.add(layers.Dense(15,activation='relu'))
model.add(layers.Dense(5,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))

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

model.fit(X_train_scaled ,y_train_onehot, epochs=5,batch_size=64, 
          validation_data=(X_test_scaled,y_test_onehot))

Metrics

metrics = pd.DataFrame(model.history.history)
metrics.head()
metrics[['loss','val_loss']].plot()
metrics[['accuracy','val_accuracy']].plot()

x_test_predictions = np.argmax(model.predict(X_test_scaled), axis=1)
print(confusion_matrix(y_test,x_test_predictions))
print(classification_report(y_test,x_test_predictions))

Predict for own handwriting

img = image.load_img('/drive/MyDrive/Colab Notebooks/Deep Learning/Lab/Exp 3/eight.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_inverted = 255.0-img_28_gray
img_28_gray_inverted_scaled = img_28_gray_inverted.numpy()/255.0
x_single_prediction = np.argmax(
   model.predict(img_28_gray_inverted_scaled.reshape(1,28,28,1)),axis=1)
plt.imshow(img_28_gray_inverted_scaled.reshape(28,28),cmap='gray')
print(x_single_prediction)