mnist.py

# -*- coding: utf-8 -*-
"""MNIST.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/11Llu088xOJzB5R0RZta07OniSz_992i9
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report

(xtrain , ytrain) , (xtest , ytest) = tf.keras.datasets.mnist.load_data()

xtrain[1].shape

plt.imshow(xtrain[1])
plt.title(f"digit = {ytrain[1]}" )

xtrain,xtest = xtrain/255 , xtest/255

model = tf.keras.Sequential([
    tf.keras.Input(shape=(28,28)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(100,activation = 'relu'),
    tf.keras.layers.Dense(50,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

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

model.fit(xtrain,ytrain,epochs = 10,verbose = 1)

test_loss, test_accuracy = model.evaluate(xtest, ytest, verbose=1)
print(f"test_loss = {test_loss}")
print(f"test_accuracy = {test_accuracy}")

#CLASSIFICATION REPORT ON TEST DATA
y_pred = model.predict(xtest)
y_pred_classes = y_pred.argmax(axis=1)
print(classification_report(ytest, y_pred_classes))

#INCORRECT PREDICTIONS ON TEST DATA
misclassified_indices = [i for i, (true, pred) in enumerate(zip(ytest, y_pred_classes)) if true != pred]
count = len(misclassified_indices)
print(f"Total misclassified_indices are {count}")

for index in misclassified_indices[:5]:
    plt.imshow(xtest[index].reshape(28, 28), cmap='gray')
    plt.title(f"True: {ytest[index]}, Pred: {y_pred_classes[index]}")
    plt.show()

#predict the test data
plt.imshow(xtest[110])

predict = np.argmax(model.predict(xtest)[110])
print(f"Number present in the image is {predict}")

#TESTING THE MODEL ON NEW DATA
test_image = tf.keras.preprocessing.image.load_img("new_image_path")
test_image = tf.image.rgb_to_grayscale(test_image)
test_image = tf.keras.preprocessing.image.img_to_array(test_image)

test_image.shape

test_image = tf.image.resize(test_image,[28,28])

test_image = test_image.numpy().squeeze()
test_image.astype("float64")
test_image = test_image/255

plt.imshow(test_image)

v = np.argmax(model.predict(test_image.reshape(1,28,28)))
print(f"Number present in the image is {v}")