Neural Networks: Siamese Networks Design

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Questions and Answers

What is the primary purpose of the Siamese network created in this lab?

To enhance the resolution of images.

To classify images into distinct categories.

To predict numerical values from data.

To determine the similarity between two input images. (correct)

What role does the Lambda layer play in the Siamese network architecture?

It calculates the absolute difference between two embeddings. (correct)

It alters the dimensionality of the input layers.

It flattens the input images before processing.

It applies a regularization technique.

What is the effect of using L2 regularization in the convolutional layers?

To prevent the model from overfitting. (correct)

To increase the learning rate of the optimizer.

To enhance the model's prediction accuracy directly.

To speed up the training process significantly.

How are positive and negative image pairs generated in the Siamese network?

By pairing identical labels for positive pairs and different labels for negative pairs. Signup and view all the answers

What is the function of BatchNormalization in the network architecture?

To standardize the inputs of the activation functions. Signup and view all the answers

What metric is used to compile the Siamese network?

Accuracy. Signup and view all the answers

Which optimizer is utilized for training the Siamese network?

Adam. Signup and view all the answers

What is the function of the Dense layer with a sigmoid activation in the output of the Siamese network?

To output probabilities for binary classification. Signup and view all the answers

During the training process, what is the expected output format of the labels for the Siamese network?

Binary values indicating pair similarity (0 or 1). Signup and view all the answers

What preprocessing is done to the input images before feeding them to the network?

Normalizing pixel values to a range of 0 to 1. Signup and view all the answers

What is the primary application of Siamese networks?

Facial similarity verification Signup and view all the answers

How do Siamese networks differ from traditional neural networks?

They consist of multiple identical subnetworks with shared weights. Signup and view all the answers

What is the function of contrastive loss in Siamese networks?

To evaluate similarity or difference between object pairs. Signup and view all the answers

What distinguishes triplet loss from contrastive loss?

Triplet loss considers distances between three objects simultaneously. Signup and view all the answers

Which is the first step in preparing data for training a Siamese network?

Gathering and preprocessing data. Signup and view all the answers

In the context of Siamese networks, what does 'anchor' refer to in triplet loss?

The primary object being compared. Signup and view all the answers

What is a key feature of the image pairs used in training Siamese networks?

Pairs can include a mixture of positive and negative examples. Signup and view all the answers

What is one of the common tasks where Siamese networks are utilized?

Finding similar images in large databases. Signup and view all the answers

What is the main goal when using contrastive loss with pairs of images?

To restrict the distance between similar images' feature vectors. Signup and view all the answers

Why is it important to normalize image pixels during data preparation for Siamese networks?

To ensure consistent input format across examples. Signup and view all the answers

Study Notes

Laboratory Work #7

Subject: Neural Networks
Topic: Siamese Networks Design
Variant: 9
Student: Pidhornyy B.V.

KMNIST Dataset Loading

Data: KMIST (or similar) dataset loaded
Source: Downloaded files
Methods: Python libraries (idx2numpy, tensorflow)
Data Preprocessing: Images normalized (divided by 255.0), data expanded to 4D (added a dimension to make it a suitable shape for the neural networks)

Pairs Creation Function

Purpose: Creates positive and negative image pairs
Input: Images (x), labels (y)
Output: Pairs of image arrays (array of pairs), corresponding labels (array of labels)
Process:
- Calculates number of unique labels (+1)
- Iterates through the images
- For each image, randomly selects a positive pair and a negative pair (ensuring the labels are different)
- Returns results as numpy arrays

Base Network Creation

Function: Creates a convolutional neural network to extract features from images
Input Shape: Shape of the image as input
Structure: Uses convolutional layers, max pooling, dense layers and batch normalization for feature extraction, applying L2 regularization to avoid overfitting
Output: A model that takes an image as input and returns feature embedding of the input image

Siamese Network Definition

input_a, input_b = Two inputs for the model to receive two images at the same time to compare them
processed_a, processed_b= processed outputs of the base network for input_a and input_b
distance = distance function for the feature embedding to find the differences, calculate the absolute value between the features
outputs = sigmoid activation function for the distance to classify whether the images are similar or different
- Model: Siamese network (model combines the 2 inputs – processed outputs, distance and sigmoid activation)

Model Compilation

Loss: Binary cross-entropy loss for comparing pairs of images
Optimizer: Adam optimization algorithm (specified with learning rate 0.0001)
Metrics: Accuracy measurement for calculating the accuracy of the model

Training the Model

Train data: Data is used to train the model to compare the image data
Validation data: Data is used to monitor the performance of the model during training
Batch size: 128
Epochs: 10 iterations
Result: Training and validation accuracy/loss measured during each epoch. (Results given in the attached output.)

Alternative Network Architectures

Additional information: (Output for a different base network architecture)
Structure: Different arrangement of layers and parameters

Testing the Result

Evaluation metrics collected during the training process were used to analyze model performance.

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Description

This quiz focuses on the design of Siamese Networks using the KMNIST dataset. It covers data loading, preprocessing, and the creation of image pairs essential for training the network. Students will also explore convolutional neural network creation for feature extraction.