fully_connected_mnist.py: For the basic fully connected neural network.

Author: asRot0

machine_learning/neural_networks/fully_connected_mnist.py

@@ -1,19 +1,24 @@
 """
 Fully Connected Neural Network for MNIST Classification
 
-Goal: This script implements a fully connected feed-forward neural network using TensorFlow and Keras to classify the
-    MNIST dataset (28x28 grayscale images of handwritten digits from 0 to 9). The network has two hidden layers with ReLU
-    activations and dropout for regularization.
+Goal: This script implements a fully connected feed-forward neural network
+    using TensorFlow and Keras to classify the MNIST dataset
+    (28x28 grayscale images of handwritten digits from 0 to 9).
+    The network has two hidden layers with ReLU activations and dropout
+    for regularization.
 
 Objectives:
 - Normalize and preprocess MNIST data.
 - Build a basic neural network with dense layers.
-- Train the model, evaluate its accuracy and loss at each epoch, and predict sample outputs.
+- Train the model, evaluate its accuracy and loss at each epoch,
+  and predict sample outputs.
+
 """
 
+import numpy as np
 import tensorflow as tf
 from tensorflow.keras import layers, models
-import numpy as np
+
 
 # Load the MNIST dataset from Keras
 mnist = tf.keras.datasets.mnist
@@ -42,12 +47,14 @@
 # Second hidden layer with 64 neurons and ReLU activation
 model.add(layers.Dense(64, activation="relu"))
 
-# Output layer with 10 neurons (one for each digit class 0-9), softmax for probabilities
+# Output layer with 10 neurons (one for each digit class 0-9),
+# softmax for probabilities
 model.add(layers.Dense(10, activation="softmax"))
 
 # Compile the model
 model.compile(
-    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+    optimizer="adam", loss="sparse_categorical_crossentropy",
+    metrics=["accuracy"]
 )
 
 # Train the model on the MNIST training data
@@ -58,7 +65,8 @@
 print(f"\nTest accuracy: {test_acc}")
 
 # Make a prediction on a random test image
-random_index = np.random.randint(0, len(X_test))
+rng = np.random.default_rng()
+random_index = rng.integers(0, len(X_test))
 random_image = np.expand_dims(X_test[random_index], axis=0)
 prediction = model.predict(random_image)
 predicted_digit = np.argmax(prediction)