cuDNN LSTM Engine Now Supported to learn Shakespeare 5x faster!

In our latest release, version 0.10.0.140, we have added CUDNN engine support to the LSTM layer to solve the Char-RNN 5x faster than when using the CAFFE engine. As described in our last post, the CAFFE version (originally created by Donahue et al. [1]) uses an internal Unrolled Net to implement the recurrent nature of …

Recurrent Learning Now Supported with cuDNN 7.4.1 on Char-RNN to learn Shakespeare!

In our latest release, version 0.10.0.122, we now support Recurrent Learning with both the LSTM [1] and LSTM_SIMPLE [2] layers to solve the Char-RNN as described by [3] and inspired by adepierre [4] and create a Shakespeare sonnet, and do so with the recently released CUDA 10.0.130/cuDNN 7.4.1. The thought of his but is the …

Policy Gradient Reinforcement Learning Now Supported with cuDNN 7.3.1 on an ATARI Gym!

In our latest release, version 0.10.0.76, we now support multi-threaded, Policy Gradient Reinforcement Learning on the Arcade-Learning-Environment [4] (based on the ATARI 2600 emulator [5]) as described by Andrej Karpathy[1][2][3], and do so with the recently released CUDA 10.0.130/cuDNN 7.3.1. Using the simple Sigmoid based policy gradient reinforcement learning model shown below… … the SignalPop …

Softmax based Policy Gradient Reinforcement Learning Now Supported with CUDA 10!

In our latest release, version 0.10.0.24, we now support multi-threaded, SoftMax based Policy Gradient Reinforcement Learning as described by Andrej Karpathy[1][2][3], and do so with the recently released CUDA 10.0.130/cuDNN 7.3. Using the simple SoftMax based policy gradient reinforcement learning model shown below… … the SignalPop AI Designer uses the MyCaffeTrainerRL to train the model …

Policy Gradient Reinforcement Learning Now Supported!

In our latest release, version 0.9.2.188, we now support Policy Gradient Reinforcement Learning as described by Andrej Karpathy[1][2][3], and do so with the recently released CUDA 9.2.148 (p1)/cuDNN 7.2.1. For training, we have also added a new Gym infrastructure to the SignalPop AI Designer, where the dataset in each project can either be a standard …

Deep Convolutional Auto-Encoders for MNIST Now Supported!

In our latest release, version 0.9.2.122, we now support deep convolutional auto-encoders with pooling as described by [1], and do so with the new ly released CUDA 9.2.148/cuDNN 7.1.4. Auto-encoders are models that learn how to re-create the input fed into them.  In our example shown here, the MNIST dataset is fed into our model,… …

Domain-Adversarial Neural Network support added to the SignalPop AI Designer!

Our latest SignalPop AI Designer release, version 0.9.1.70, now supports Domain-Adversarial Neural Networks (DANN) as described by [1]. By adding image overlay support to an updated MNIST Dataset Creator, you can now create both source and target datasets. Using the new source and target dataset support you can now easily create DANN networks that use …