The codebase for the Recurrent Sparse Memory (RSM) project.
- PAGI Framework >= 0.1
Ensure that you have pagi
installed and that its accessible via the command-line by running pagi --help
. Clone this
repository and install the package using pip install -e .
In addition to the hyperparameters available below (supplemental to the paper), the JSON definition files for these experiments that can be run with our framework are also available under the definitions directory.
Hyperparameter | Value |
---|---|
Batch size | 400 |
g (groups) |
200 |
c (cells per group) |
6 |
k (sparsity) |
25 |
gamma (inhibition decay rate) |
0.98 |
epsilon (recurrent input decay rate) |
0 |
Classifier hidden layer size | 500 |
Hyperparameter | Value |
---|---|
Batch size | 300 |
g (groups) |
200 |
c (cells per group) |
6 |
k (sparsity) |
25 |
gamma (inhibition decay rate) |
0.5 |
epsilon (recurrent input decay rate) |
0 |
Classifier hidden layer size | 1200 |
We have uploaded a video of the generated dynamics from the RSM 2-layer + GAN algorithm compared to RTRBM [1]. Unfortunately, there is no available video of the generative dynamics from the PGN method [2]. In the video, our algorithm is first primed with 50 frames and then switched to self-looping mode for 150 frames, feeding the GAN-rectified prediction back into the RSM. The video is located at: https://www.youtube.com/watch?v=-lBFW1gbokg
Hyperparameter | Value |
---|---|
Batch size | 256 |
ConvRSM Layer 1 | |
g (groups) |
64 |
c (cells per group) |
8 |
k (sparsity) |
3 |
gamma (inhibition decay rate) |
0 |
epsilon (recurrent input decay rate) |
0 |
receptive field | 5x5 |
pool size | 2 |
strides | 2 |
ConvRSM layer 2 | |
g (groups) |
128 |
c (cells per group) |
8 |
k (sparsity) |
5 |
gamma (inhibition decay rate) |
0 |
epsilon (recurrent input decay rate) |
0 |
receptive field | 3x3 |
strides | 2 |
GAN Generator [convolutional AE] | |
Enc. receptive field | 5x5 |
Enc. filters | 64, 128, 256 |
Enc. strides | 1, 2, 1 |
Enc. nonlinearity | Leaky ReLU |
Dec. receptive field | 5x5 |
Dec. filters | 128, 64, 1 |
Dec. strides | 2, 1, 1 |
Dec. hidden nonlinearity | Leaky ReLU |
Dec. output nonlinearity | Sigmoid |
GAN Discriminator [fully connected] | |
Hidden layer size | 128 |
Hidden layer nonlinearity | Leaky ReLU |
Output layer nonlinearity | Sigmoid |
Hyperparameter | Value |
---|---|
Batch size | 300 |
g (groups) |
600 |
c (cells per group) |
8 |
k (sparsity) |
20 |
gamma (inhibition decay rate) |
0.8 |
epsilon (recurrent input decay rate) |
0.85 |
Classifier hidden layer size | 1200 |
- I. Sutskever, G. E. Hinton, and G. W. Taylor, “The recurrent tempo-ral restricted boltzmann machine,” inAdvances in neural informationprocessing systems, 2009, pp. 1601–1608.
- W. Lotter, G. Kreiman, and D. Cox, “Unsupervised learning of vi-sual structure using predictive generative networks,”arXiv preprintarXiv:1511.06380, 2015.