Summary

The provided web content offers an explanation of the DenseNet architecture for image classification, including code examples using Keras and TensorFlow.

Abstract

The article delves into the DenseNet architecture, a state-of-the-art deep learning model for image classification. It introduces the concept of Dense Blocks, where each layer is connected to every other layer in a feed-forward fashion, enhancing information flow and feature reuse. The architecture incorporates Bottleneck Layers to reduce computational complexity. The article also provides Python code examples to illustrate how to implement DenseNet using Keras and TensorFlow, demonstrating the construction of a simple neural network with two Dense Blocks. Additionally, the author encourages readers to subscribe for future publications and promotes their book on machine learning for those interested in a more comprehensive understanding of the field.

Opinions

The author suggests that readers with some knowledge in machine learning can benefit from starting with their previous articles on convolutional networks.
For beginners, the author recommends their book as a complete guide to start with classic machine learning, even without coding knowledge.
The author emphasizes the effectiveness of DenseNet in image classification tasks since its introduction in 2017.
The article implies that DenseNet's architecture, with its dense connections and bottleneck layers, is superior in terms of efficiency and performance compared to previous architectures.
The author provides a subjective call to action, inviting readers to subscribe, follow, and consider purchasing their book to support their work and further their understanding of machine learning.

DenseNet Architecture Explained with Code Examples

Using Keras and TensorFlow

Densenet is the last architecture we will explore in this series of articles about convolutional networks and image classification. If you already have some knowledge about classic Machine Learning, you can start here, and read all articles in the same order they were published. If you are a total beginner, don’t worry, you can buy my book from Amazon where you will find a complete guide to start with Classic Machine Learning, even if you don’t know how to code.

DenseNet, short for Densely Connected Convolutional Networks, is a deep learning architecture for image classification tasks introduced in 2017 with a paper with the same name.

The core idea behind DenseNet is the Dense Blocks, where the output of a layer is connected to each other layer that follows it in the same block. Therefore, a Dense Block consists of multiple layers, where each layer receives inputs from all previous layers and passes its own feature map to all subsequent layers.

Each layer within the Dense Block can also include a Bottleneck Layer, which is a 1x1 convolution used to reduce the number of channels, and this way reduces the computational cost. It helps in dimensionality reduction before the subsequent 3x3 convolution.

Let’s take a look at a DenseBlock schema, in this example with 4 layers, but it can include as many layers as you want:

The previous image can be further simplified as:

Where each layer is:

We can transform this schema into Python code:

def dense_block(x, num_layers, growth_rate):
    # Create a dense block with 'num_layers' layers
    for _ in range(num_layers):
        #Store the initial values of x to be concatenated 
        x_input = x
        # BatchNormalization and ReLU activation
        x = BatchNormalization()(x)
        x = ReLU()(x)
        
        # 1x1 bottleneck convolution
        x = Conv2D(growth_rate, (1, 1), padding='same')(x)
        
        # 3x3 convolution
        x = Conv2D(growth_rate, (3, 3), padding='same')(x)
        
        # Concatenate with the input
        x = Concatenate(axis=-1)([x_input, x])
    
    return x

To build a full DenseNet model we just need to include the DenseBlocks in our desired architecture. We can see a simple neural Dense Network example with only two Dense Blocks as follows:

Which we can transform into Python code:

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, ReLU, Concatenate, AveragePooling2D, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model


def dense_block(x, num_layers, growth_rate):
    # Create a dense block with 'num_layers' layers
    for _ in range(num_layers):
        #Store the initial values of x to be concatenated 
        x_input = x
        # BatchNormalization and ReLU activation
        x = BatchNormalization()(x)
        x = ReLU()(x)
        
        # 1x1 bottleneck convolution
        x = Conv2D(growth_rate, (1, 1), padding='same')(x)
        
        # 3x3 convolution
        x = Conv2D(growth_rate, (3, 3), padding='same')(x)
        
        # Concatenate with the input
        x = Concatenate(axis=-1)([x_input, x])
    
    return x

# Hyperparameters
input_shape = (224, 224, 3)  # Example input shape for RGB images
num_classes = 10  # Number of output classes
growth_rate = 32  # Growth rate controls the number of channels in each layer within a DenseBlock
num_layers_per_block = 4  # Number of layers in each DenseBlock

# Input layer
input_layer = Input(shape=input_shape)

# Initial convolution layer
x = Conv2D(64, (7, 7), strides=(2, 2), padding='same')(input_layer)
x = BatchNormalization()(x)
x = ReLU()(x)

# First DenseBlock
x = dense_block(x, num_layers_per_block, growth_rate)

# Transition layer (reduce spatial dimensions)
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(128, (1, 1), padding='same')(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)

# Second DenseBlock
x = dense_block(x, num_layers_per_block, growth_rate)

# Global Average Pooling
x = GlobalAveragePooling2D()(x)

# Fully connected layer for classification
x = Dense(num_classes, activation='softmax')(x)

# Create the model
model = Model(inputs=input_layer, outputs=x)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Print the model summary
model.summary()

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