Pros of keras-contrib

• Larger community and more active development
• Broader range of advanced Keras layers and functionalities
• Better integration with the Keras ecosystem

Cons of keras-contrib

• Less focused on capsule networks specifically
• May require more setup and configuration for capsule network implementations

Code Comparison

keras-contrib example:

from keras_contrib.layers import CapsuleLayer

model.add(CapsuleLayer(num_capsule=10, dim_capsule=16, routings=3))

capsule-networks example:

from capsule_layer import CapsuleLayer

model.add(CapsuleLayer(num_capsule=10, dim_capsule=16, num_routing=3))

The code snippets show that both repositories provide implementations of capsule layers, but with slightly different parameter naming conventions. keras-contrib uses routings, while capsule-networks uses num_routing.

keras-contrib offers a more extensive collection of advanced Keras layers and functionalities, making it suitable for a wider range of deep learning projects. However, capsule-networks is more focused on capsule network implementations specifically, which may be beneficial for projects centered around this architecture.

While keras-contrib benefits from a larger community and more active development, capsule-networks provides a more specialized approach to capsule networks. The choice between the two repositories depends on the specific requirements of your project and whether you need a broader range of Keras extensions or a more focused capsule network implementation.

Pros of CapsNet-Tensorflow

• More comprehensive documentation and explanations
• Includes additional features like dynamic routing and reconstruction
• Supports multiple datasets (MNIST, CIFAR10, SVHN)

Cons of CapsNet-Tensorflow

• Less frequently updated compared to capsule-networks
• May have higher computational requirements due to additional features
• Potentially more complex for beginners to understand and modify

Code Comparison

CapsNet-Tensorflow:

def squash(vector):
    vector_squared_norm = tf.reduce_sum(tf.square(vector), -2, keepdims=True)
    scalar_factor = vector_squared_norm / (1 + vector_squared_norm) / tf.sqrt(vector_squared_norm + epsilon)
    return scalar_factor * vector

capsule-networks:

def squash(s, axis=-1, epsilon=1e-7):
    squared_norm = torch.sum(s**2, dim=axis, keepdim=True)
    scale = squared_norm / (1 + squared_norm)
    return scale * s / (torch.sqrt(squared_norm) + epsilon)

Both implementations showcase the squash function, a key component in capsule networks. CapsNet-Tensorflow uses TensorFlow operations, while capsule-networks uses PyTorch. The overall structure is similar, but CapsNet-Tensorflow includes an additional scalar_factor calculation.

Pros of models

• More comprehensive, covering a wider range of machine learning models and techniques
• Better documentation and examples for various use cases
• More active development and community support

Cons of models

• Larger and more complex codebase, potentially harder to navigate
• May include unnecessary components for those specifically interested in capsule networks
• Potentially slower to implement specific capsule network architectures

Code Comparison

capsule-networks:

class CapsuleLayer(nn.Module):
    def __init__(self, num_capsules, num_route_nodes, in_channels, out_channels, kernel_size=None, stride=None, num_iterations=3):
        super(CapsuleLayer, self).__init__()
        self.num_route_nodes = num_route_nodes
        self.num_iterations = num_iterations
        self.num_capsules = num_capsules

models:

class CapsuleNet(nn.Module):
    def __init__(self):
        super(CapsuleNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=256, kernel_size=9, stride=1)
        self.primary_capsules = PrimaryCaps(256, 32, 8, kernel_size=9, stride=2)
        self.digit_capsules = DigitCaps(32*6*6, 10, 16, 8)