3DPyranet

3DPyranet is a deep pyramidal neural network, based on biological pyramidal neurons developed in [1];

Usage

Check train.py file for the correct pipeline and model usage. Code uses Sparse Softmax Cross Entropy as loss function, it doesn't need One Hot Encoding.

Documentation

Documentation can be found here:

https://3dpyranet.readthedocs.io/

Dependencies

• Python 3;
• Tensorflow 1.4+;
• TQDM;
• Numpy;

-> WARNING <-

In requirements.txt check which version of tensorflow do you need! By default tensorflow-gpu is enabled

FLAGS

Checkpoint and evaluation

Option	Type	Default	Description
evaluate_every	float	1	Number of epoch for each evaluation (decimals allowed)
test_milestones	list	15,25,50	Each epoch where performs test
save_checkpoint	boolean	False	Flag to save checkpoint or not
checkpoint_name	string	3dpyranet.ckpt	Name of checkpoint file

Input

Option	Type	Default	Description
train_path	string	//	Path to npy training set
train_labels_path	string	//	Path to npy training set labels
val_path	string	//	Path to npy val/test set
val_labels_path	string	//	Path to npy val/test set labels
save_path	string	//	Path where to save network model

Input parameters

Option	Type	Default	Description
batch_size	int	100	Batch size
depth_frames	int	16	Number of consecutive samples
height	int	100	Sample height
width	int	100	Sample width
in_channels	int	1	Sample channels
num_classes	int	6	Number of classes

Hyper-parameters settings

Option	Type	Default	Description
feature_maps	int	3	Number of maps to use (strict model shares the number of maps in each layer)
learning_rate	float	0.00015	Learning rate
decay_steps	int	15	Number of epoch for each decay
decay_rate	float	0.1	Learning rate decay
max_steps	int	50	Maximum number of epoch to perform
weight_decay	float	None	L2 regularization lambda

Optimization

Option	Type	Default	Description
optimizer	string	MOMENTUM	Optimization algorthim (GD - MOMENTUM - ADAM)
use_nesterov	boolean	False	Flag to use Nesterov Momentum (it works only with MOMENTUM optimizer)

References

[1] Ullah, Ihsan, and Alfredo Petrosino. "Spatiotemporal features learning with 3DPyraNet." International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, Cham, 2016.