Creating a Custom Convolutional Neural Network (CNN) Model for Image Processing: A Step-by-Step Guide

Creating a Custom Convolutional Neural Network (CNN) Model for Image Processing: A Step-by-Step Guide🔍🖌️📸️⚙️

Why Create a Custom CNN?

Creating a custom CNN model allows you to tailor your neural network to specific image processing tasks. It enables you to optimize performance and improve the accuracy of your predictions.

Structure of Our CNN Model

1. Convolutional Layer (32 filters, 3x3 window): Input size: 150x150x3
2. Max Pooling Layer (2x2 window)
3. Convolutional Layer (64 filters, 3x3 window)
4. Max Pooling Layer (2x2 window)
5. Convolutional Layer (128 features, 3x3 window)
6. Max Pooling Layer (2x2 window)
7. Convolutional Layer (128 filters, 3x3 window)
8. Flatten
9. Dense Layer (512 neurons)
10. Output Layer (1 neuron): Since we only want to predict two classes.

Key Considerations

• The number of filters increases with each convolutional layer.
• Image size decreases with each max pooling layer.

Why These Choices?

The number of filters is gradually increased throughout the network to enhance its learning capacity. The image size is reduced through max pooling, which reduces computation and helps the model focus on relevant features.

Coding the CNN Model

To code our custom CNN model, we will use Keras' layers without specifying Keras.layers.

Compiling the Model

Once our CNN model is coded, it needs to be compiled with an appropriate optimizer (in this case, RMSprop).

Training the Model

To train our custom CNN model, we will use a training generator that loads images in batches of 20. We'll need to specify the number of steps for each epoch (i.e., 100 steps for 2000 images).

By following these steps, you can create your own custom CNN model for image processing tasks. Happy coding!🤖✨🎉