deep learning image classification

DEEP LEARNING, AAN, IMAGE CLASSIFICATION

Step 1: Import Libraries

import numpy as np import matplotlib.pyplot as plt import seaborn as sns from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Dense, Dropout from tensorflow.keras.utils import to_categorical from sklearn.metrics import confusion_matrix, classification_report

Step 2: Load the Data

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

Step 3: Preprocess the Data

X_train = X_train / 255.0 # Normalize pixel values to [0,1] X_test = X_test / 255.0 y_train_cat = to_categorical(y_train, 10) # One-hot encode labels y_test_cat = to_categorical(y_test, 10)

Plot some digits from dataset

selected_indices = [10, 25, 75, 300, 501, 999, 1234, 1500, 1999] # Choose which image indices to display

plt.figure(figsize=(8, 8))

for i, idx in enumerate(selected_indices): plt.subplot(3, 3, i + 1) plt.imshow(X_train[idx], cmap=’gray’) plt.title(f”Label: {y_train[idx]} (Index: {idx})”) plt.axis(‘off’)

plt.tight_layout() plt.show()

Step 4: Build the ANN Model

model = Sequential([ Flatten(input_shape=(28, 28)), Dense(128, activation=’relu’), Dropout(0.3), Dense(64, activation=’relu’), Dropout(0.3), Dense(10, activation=’softmax’) ])

Step 5: Compile the Model

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

Step 6: Train the Model

history = model.fit(X_train, y_train_cat, epochs=10, batch_size=128, validation_split=0.1)

Step 7: Evaluate on Test Set

test_loss, test_accuracy = model.evaluate(X_test, y_test_cat) print(f”Test Accuracy: {test_accuracy:.4f}”)

Step 8: Visualize Training History

plt.plot(history.history[‘accuracy’], label=’Train Acc’) plt.plot(history.history[‘val_accuracy’], label=’Val Acc’) plt.title(‘Model Accuracy’) plt.xlabel(‘Epoch’) plt.ylabel(‘Accuracy’) plt.legend() plt.show()

Step 9: Confusion Matrix

y_pred = model.predict(X_test) y_pred_classes = np.argmax(y_pred, axis=1)

cm = confusion_matrix(y_test, y_pred_classes) plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, fmt=’d’, cmap=’Blues’) plt.xlabel(‘Predicted’) plt.ylabel(‘True’) plt.title(‘Confusion Matrix’) plt.show()

Step 10: Classification Report

print(classification_report(y_test, y_pred_classes))

Step 11: Save and Reload the Model

model.save(“mnist_ann_model.h5”)

from tensorflow.keras.models import load_model reloaded_model = load_model(“mnist_ann_model.h5”) reloaded_model.evaluate(X_test, y_test_cat)