gokulramalingam2005 / mnist-classification

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

Include the neural network model diagram.

DESIGN STEPS

STEP 1:

->import libraries ->train the dataset

STEP 2:

-> create the model -> train the model

STEP 3:

-> upload the hand written image and predict the output

Write your own steps

PROGRAM

Developed by:Gokul R
Regno : 212222230039

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
y_test.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()
# Write your code here
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()

# Choose the appropriate parameters
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics='accuracy')
model.fit(X_train_scaled ,y_train_onehot, epochs=5,
          batch_size=64, 
          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('thre.jpeg')
type(img)
img = image.load_img('thre.jpeg')
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.numpy().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

A convolutional deep neural network for digit classification and to verify the response for scanned handwritten images is developed sucessfully.