
import tensorflow as tf
from tensorflow.keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt

import warnings

warnings.filterwarnings('ignore')


(training_images, training_labels), (test_images, test_labels) = mnist.load_data()


training_images, test_images = training_images / 255.0, test_images / 255.0


for i in range(10):
    plt.subplot(2, 5, i + 1)
    plt.imshow(training_images[i], cmap='gray')
    plt.title(f"Label: {training_labels[i]}")
    plt.axis('off')
plt.show()


model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])


model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])


model.fit(training_images, training_labels, epochs=5, validation_data=(test_images, test_labels))

Epoch 1/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 798us/step - accuracy: 0.8584 - loss: 0.4745 - val_accuracy: 0.9593 - val_loss: 0.1410
Epoch 2/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 784us/step - accuracy: 0.9552 - loss: 0.1531 - val_accuracy: 0.9686 - val_loss: 0.1022
Epoch 3/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 780us/step - accuracy: 0.9681 - loss: 0.1046 - val_accuracy: 0.9730 - val_loss: 0.0861
Epoch 4/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 785us/step - accuracy: 0.9732 - loss: 0.0873 - val_accuracy: 0.9768 - val_loss: 0.0770
Epoch 5/5
1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 787us/step - accuracy: 0.9770 - loss: 0.0732 - val_accuracy: 0.9760 - val_loss: 0.0764

<keras.src.callbacks.history.History at 0x15a643f10>


image_path = 'digit.jpg'


from PIL import Image

image = Image.open(image_path)
image = image.resize((28, 28))
image = image.convert('L')


image


image = np.array(image) / 255.0
image = np.expand_dims(image, axis=0)


prediction = model.predict(image)

predicted_class = np.argmax(prediction, axis=1)[0]

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step


print("Predicted digit:", predicted_class)

Predicted digit: 8

Classifying Images¶

Set Up Test and Training Data¶

Design, Compile and Fit Model¶

Import Image to Classify Prediction¶