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

Author: asRot0

machine_learning/neural_networks/fully_connected_mnist.py

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+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
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# Normalize the images from 0-255 to 0-1 by dividing by 255
+X_train, X_test = X_train / 255.0, X_test / 255.0
+
+# Print TensorFlow and Keras information
+print(f"TensorFlow Version: {tf.__version__}")
+print(f"Keras Layers Module: {layers.__name__}")
+print(f"Keras Models Module: {models.__name__}")
+
+# Build a simple Sequential model
+model = models.Sequential()
+
+# Flatten the 28x28 images into vectors of length 784
+model.add(layers.Flatten(input_shape=(28, 28)))
+
+# First hidden layer with 128 neurons and ReLU activation
+model.add(layers.Dense(128, activation='relu'))
+
+# Dropout layer to prevent overfitting (randomly drops 20% of neurons)
+model.add(layers.Dropout(0.2))
+
+# 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
+model.add(layers.Dense(10, activation='softmax'))
+
+# Compile the model
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model on the MNIST training data
+model.fit(X_train, y_train, epochs=5, batch_size=32)
+
+# Evaluate the model on the test set
+test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+print(f'\nTest accuracy: {test_acc}')
+
+# Make a prediction on a random test image
+random_index = np.random.randint(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)
+
+# Print the predicted result and actual label
+print(f'Predicted digit: {predicted_digit}, Actual digit: {y_test[random_index]}')
+