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Feat: Add MaxPooling2D and AveragePooling2D Layers #110

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Merged
ravin-d-27 merged 1 commit into from
Oct 15, 2025
Merged

Feat: Add MaxPooling2D and AveragePooling2D Layers #110

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18 changes: 10 additions & 8 deletions cnn_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,8 +30,7 @@ def load_and_preprocess_digits():
)
return X_train, y_train, X_test, y_test


# --- CNN Model Demo (Uses Multi_Layer_CNN) ---
# --- MAIN EXECUTION ---
if __name__ == "__main__":

print("Loading and preparing Digits dataset...")
Expand All @@ -44,18 +43,20 @@ def load_and_preprocess_digits():
y_train = y_train[:-100]

# --- DEFINE THE FULL SEQUENTIAL ARCHITECTURE ---
# Conv -> Conv -> Flatten -> Dense -> Dense
# Conv -> Pool -> Conv -> Pool -> Flatten -> Dense -> Dense
cnn_layers_config = [
# First convolutional layer: 8x8x1 -> 6x6x16
{'type': 'conv', 'out_channels': 16, 'kernel_size': 3, 'stride': 1, 'padding': 0},
# Pooling layer: 6x6x16 -> 3x3x16
{'type': 'maxpool', 'pool_size': 2, 'stride': 2},

# Second convolutional layer: 6x6x16 -> 4x4x32
# Second convolutional layer: 3x3x16 -> 1x1x32
{'type': 'conv', 'out_channels': 32, 'kernel_size': 3, 'stride': 1, 'padding': 0},

# Flatten: 4x4x32 -> 512
# Flatten: 1x1x32 -> 32
{'type': 'flatten'},

# First dense layer: 512 -> 64
# First dense layer: 32 -> 64
{'type': 'dense', 'neurons': 64, 'activation': 'relu'},

# Output layer: 64 -> 10 (digits 0-9)
Expand All @@ -64,11 +65,12 @@ def load_and_preprocess_digits():

print("\nInitializing Multi_Layer_CNN Model...")

# Instantiate the new integrated model
# Instantiate the new integrated model with the 'activations' argument
model = Multi_Layer_CNN(
layers_list=cnn_layers_config,
X_train=X_train,
Y_train=y_train,
activations=['relu', 'relu', 'relu', 'softmax'],
loss='categorical_crossentropy',
optimizer='adam'
)
Expand All @@ -91,7 +93,7 @@ def load_and_preprocess_digits():
y_pred_classes = np.argmax(y_pred, axis=1)
y_true_classes = np.argmax(y_test, axis=1)
test_accuracy = accuracy_score(y_true_classes, y_pred_classes) * 100
print(f"\n705 Final Test Accuracy: {test_accuracy:.2f}%")
print(f"\nFinal Test Accuracy: {test_accuracy:.2f}%")
# Optional: plot training history
plot_util = Plotting_Utils()
plot_util.plot_training_history(model.history, metrics=('loss', 'accuracy'), figure='training_history.png')
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