NDV_Code_By_NandiniM_DigitRecognition

NDV_Code_By_NandiniM_DigitRecognition/DigitRecognition.py

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+#Digit Recognition
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+X_train = X_train / 255.0
+X_test = X_test / 255.0
+
+X_train = X_train.reshape(-1, 28, 28, 1)
+X_test = X_test.reshape(-1, 28, 28, 1)
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
+    MaxPooling2D(pool_size=(2, 2)),
+    Flatten(),
+    Dense(64, activation='relu'),
+    Dense(10, activation='softmax')  # 10 classes for digits 0–9
+])
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+model.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test))
+
+loss, accuracy = model.evaluate(X_test, y_test)
+print(f"Test Accuracy: {accuracy:.2f}")
+
+
+plt.imshow(X_test[0].reshape(28, 28), cmap='gray')
+plt.title(f"True Label: {y_test[0]}")
+plt.show()
+
+prediction = model.predict(np.expand_dims(X_test[0], axis=0))
+print("Predicted Label:", np.argmax(prediction))