Building Neural Networks with Libraries
While you can build neural networks from scratch, using libraries like TensorFlow/Keras makes it much easier. These libraries handle the complex math, optimization, and training loops for you.
In this lesson, we'll learn how to structure and build neural networks using high-level APIs. You'll see how to define layers, set up the network architecture, and configure training parameters.
Network Architecture Design
Designing a neural network involves choosing layers, sizes, and connections:
print("Neural Network Architecture Components:")
print("=" * 50")
print("\n1. Input Layer:")
print(" - Defines input shape (number of features)")
print(" - Example: Input(shape=(10,)) for 10 features")
print("\n2. Dense (Fully Connected) Layers:")
print(" - Each neuron connects to all neurons in next layer")
print(" - Example: Dense(64, activation='relu')")
print(" - 64 neurons")
print(" - ReLU activation")
print("\n3. Output Layer:")
print(" - Number of neurons = number of classes/outputs")
print(" - Binary: Dense(1, activation='sigmoid')")
print(" - Multi-class: Dense(num_classes, activation='softmax')")
print("\nExample Architecture:")
print(" Input(shape=(10,))")
print(" ↓")
print(" Dense(64, activation='relu')")
print(" ↓")
print(" Dense(32, activation='relu')")
print(" ↓")
print(" Dense(1, activation='sigmoid') # Binary classification")
Compiling and Training
Once the network is built, you need to compile and train it:
print("Network Compilation and Training:")
print("=" * 50")
print("\n1. Compile the Model:")
print(" model.compile(")
print(" optimizer='adam',")
print(" loss='binary_crossentropy',")
print(" metrics=['accuracy']")
print(" )")
print(" "))
print(" Optimizer: How to update weights (adam, sgd, etc.)")
print(" Loss: What to minimize (binary_crossentropy, mse, etc.)")
print(" Metrics: How to evaluate (accuracy, precision, etc.)")
print("\n2. Train the Model:")
print(" history = model.fit(")
print(" X_train, y_train,")
print(" epochs=10,")
print(" batch_size=32,")
print(" validation_data=(X_val, y_val)")
print(" )")
print("\nTraining Parameters:")
print(" - epochs: Number of times to see entire dataset")
print(" - batch_size: Number of samples per update")
print(" - validation_data: Test set for monitoring")
print("\n3. Make Predictions:")
print(" predictions = model.predict(X_test)")
Exercise: Build a Neural Network
Complete the exercise on the right side:
- Task 1: Define network architecture (input size, hidden layers, output)
- Task 2: Simulate forward pass through the network
- Task 3: Calculate network parameters (total weights, biases)
- Task 4: Simulate training step (forward pass, loss, weight update)
Write your code to build and understand neural network structure!
💡 Learning Tip
Practice is essential. Try modifying the code examples, experiment with different parameters, and see how changes affect the results. Hands-on experience is the best teacher!
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Lesson Complete!
Great work! Continue to the next lesson.