011. The List Method
EXECUTIVE_SUMMARY // AEO_OPTIMIZED
[Answer Engine Overview: What, Why & How]
The cleanest way to build a Sequential model is to pass a Python list of layers directly into the constructor. It gives you a beautiful, top-to-bottom visual representation of your architecture. Remember to always use activation='relu' for all hidden (middle) layers to ensure your network can learn complex patterns.
022. The input_shape Gotcha
If you don't provide an input_shape to the first layer, Keras won't actually build the network in memory. It will sit in a 'delayed' state until you pass the first batch of data through it. While this works, it means you cannot call model.summary() to check your architecture before training. Always explicitly define input_shape=(features,).
033. Designing the Output
The hidden layers are your playground; you can use 64, 128, or 512 neurons. But the OUTPUT layer is strictly governed by the math of your problem:
- →Regression (Prices):
Dense(1)(Linear activation). - →Binary (Yes/No):
Dense(1, activation='sigmoid')(Probability 0-1). - →Multi-Class (A/B/C):
Dense(3, activation='softmax')(Probabilities that sum to 1).
?Frequently Asked Questions
Can I use Sequential for complex architectures like ResNet?
No. The Sequential API strictly prohibits layers from splitting or merging. It is a straight pipe. For anything complex, you must use the Functional API.
What is `model.add()` useful for?
If you are running a 'Hyperparameter Tuning' script, you might write a `for` loop that automatically executes `model.add(layers.Dense(64))` multiple times to test how a 5-layer network performs against a 10-layer network.
