Building the CNN Model for Sketch Recognition

Welcome to the exciting world of Convolutional Neural Networks (CNNs) for sketch recognition! In this lesson, you will learn how to build a CNN model specifically designed to recognize hand-drawn sketches. This is a crucial step in our journey to understanding how machines can interpret and classify visual data. Whether you're new to CNNs or need a refresher, this lesson will guide you through the process of constructing a simple yet effective model.

In this lesson, we will focus on the architecture of a CNN model tailored for sketch recognition. You will learn how to decide the number of layers and their types, which are essential for building a robust model. We will implement the CNN model using Keras and TensorFlow, two powerful libraries for machine learning.

Here's a sneak peek at the code you'll be working with:

import tensorflow as tf
categories = ['cat', 'house', 'airplane', 'apple', 'bicycle']

def build_simple_cnn():
    model = tf.keras.Sequential([
        tf.keras.layers.Input(shape=(28,28,1)),
        tf.keras.layers.Conv2D(32, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Conv2D(64, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(len(categories), activation='softmax')
    ])

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

# Build the model
model = build_simple_cnn()

# Print model summary
model.summary()

This code defines a simple CNN model with layers that help in recognizing different sketch categories.

Let's break down the layers in the CNN model and explain their roles:

• Input Layer (tf.keras.layers.Input(shape=(28,28,1))): This layer defines the shape of the input images. Here, each sketch is a 28x28 pixel grayscale image (the 1 indicates a single color channel).

• First Convolutional Layer (tf.keras.layers.Conv2D(32, 3, activation='relu')): This layer applies 32 filters (small 3x3 grids) to the input image to detect simple features like edges and lines. The relu activation introduces non-linearity, helping the network learn complex patterns.

• First Max Pooling Layer (tf.keras.layers.MaxPooling2D()): This layer reduces the spatial size of the feature maps by taking the maximum value in each 2x2 window. This helps to make the model more efficient and reduces the risk of overfitting.

• Second Convolutional Layer (tf.keras.layers.Conv2D(64, 3, activation='relu')): This layer uses 64 filters to detect more complex features by building on the patterns found by the previous layer.

• Second Max Pooling Layer (tf.keras.layers.MaxPooling2D()): Again, this reduces the size of the feature maps, making the computation more manageable and focusing on the most important features.

• Flatten Layer (tf.keras.layers.Flatten()): This layer converts the 2D feature maps into a 1D vector, preparing the data for the dense (fully connected) layers.

• Dense Layer (tf.keras.layers.Dense(128, activation='relu')): This fully connected layer with 128 neurons learns to combine the features extracted by the convolutional layers to make predictions.

• Output Layer (tf.keras.layers.Dense(len(categories), activation='softmax')): This layer outputs a probability for each sketch category using the softmax activation, allowing the model to classify the input sketch into one of the defined categories.

Each layer in this architecture plays a specific role in transforming the raw pixel data into meaningful features that can be used to accurately recognize hand-drawn sketches.