Keras

CNN Pipeline

April 22, 2026
AI, Deep-Learning
Deep Learning, Cnn, Keras

CNN Pipeline: Preprocessing & Models #

  • Understand CNN concepts deeply
  • Build CNN models step-by-step
  • Apply CNNs in assignments using Keras

Think of CNN as a pipeline: Image → Features → Patterns → Prediction

1. Image Representation #

\[ X \in \mathbb{R}^{H \times W \times C} \]
  • H = Height
  • W = Width
  • C = Channels

2. Convolution Operation #

\[ Z(i,j) = \sum_{m,n} X(i+m, j+n) \cdot K(m,n) \]
  • Sliding filter extracts features
  • Produces feature maps

3. Stride & Padding #

\[ Output = \frac{N - F + 2P}{S} + 1 \]

4. Activation (ReLU) #

\[ ReLU(x) = max(0, x) \]

5. Pooling #

  • Max Pooling → strongest feature
  • Average Pooling → smooth

6. Global Average Pooling #

\[ y_k = \frac{1}{HW} \sum_{i,j} x_{i,j,k} \]

7. Loss Function #

\[ L = - \sum y \log(\hat{y}) \]

8. CNN Architecture #

graph LR
A[Input Image] --> B[Conv]
B --> C[ReLU]
C --> D[Pooling]
D --> E[Conv Layers]
E --> F[Flatten / GAP]
F --> G[Dense]
G --> H[Output]

9. Training #

  • Forward pass
  • Loss computation
  • Backpropagation
  • Weight update

10. Keras Implementation #

Model #

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten

model = Sequential()

model.add(Conv2D(32, (3,3), activation='relu', input_shape=(64,64,3)))
model.add(MaxPooling2D((2,2)))

model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPooling2D((2,2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

Compile #

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train #

model.fit(X_train, y_train, epochs=10, batch_size=32)

Predict #

pred = model.predict(X_test)

11. Tips #

  • Normalize images
  • Use small filters
  • Avoid too many dense layers

12. Summary #

CNN = Automatic feature extractor + classifier