The Sequential model is Keras' simplest way to build neural networks. It creates a linear stack of layers where each layer has exactly one input and one output.
Perfect for beginners:
• Simple to understand - layers stacked one after another
• Easy to build - just add layers sequentially
• Most common use case - feedforward networks
• Great starting point - covers 90% of architectures
💡 Key Insight:
Think of Sequential as building blocks - each layer's output becomes the next layer's input, like a chain.
Common Layer Types
🧠 Dense (Fully Connected)
Each neuron connects to all neurons in previous layer
🖼️ Conv2D (Convolutional)
Applies filters to detect features in images
⬇️ MaxPooling2D
Reduces spatial dimensions, keeps important features
🎲 Dropout
Randomly turns off neurons to prevent overfitting
📏 Flatten
Converts 2D feature maps to 1D for Dense layers
Building Sequential Models
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout
# Method 1: Add layers one by one
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
# Method 2: Pass list of layers
model = Sequential([
Dense(128, activation='relu', input_shape=(784,)),
Dropout(0.2),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
# Compile the model
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
# View model architecture
model.summary()