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

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

Developed By: Jegathish S
Register No: 212221230041

import numpy as np
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
import pandas as pd
from sklearn.metrics import classification_report,confusion_matrix
from tensorflow.keras.preprocessing import image

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train.shape
X_test.shape
single_image= X_train[0]
single_image.shape
plt.imshow(single_image,cmap='gray')
y_train.shape
X_train.min()
X_train.max()
X_train_scaled = X_train/255.0
X_test_scaled = X_test/255.0
X_train_scaled.min()
X_train_scaled.max()
y_train[0]
y_train_onehot = utils.to_categorical(y_train,10)
y_test_onehot = utils.to_categorical(y_test,10)
type(y_train_onehot)
y_train_onehot.shape
single_image = X_train[500]
plt.imshow(single_image,cmap='gray')
y_train_onehot[500]
X_train_scaled = X_train_scaled.reshape(-1,28,28,1)
X_test_scaled = X_test_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(32,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))

model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics='accuracy')
model.fit(X_train_scaled ,y_train_onehot, epochs=8,batch_size=128, validation_data=(X_test_scaled,y_test_onehot))

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

img = image.load_img('ei.png')
type(img)
img = image.load_img('ei.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)