A node based deep neural network library on top of CuDNN
A work in progress. Not mature enough for a release but cool enough for poking around.
- Node base like Tensorflow
- Multiple execution phases per graph
- Custom solver per variable
- Live weight/data display
- Create C++ code from Deepflow model
- NVIDIA Graphics Card
- GPU Only
- Visual Studio 2015
- CUDA 9.0
- OpenCV 3.0
- cuDNN v7.1
- Protocol Buffers
- glog
- gflags
- face_dcgan: A least-square generative adversarial network.
- face_ac: Deep auto-encoder on 128x128 CelebA dataset.
- face_vae: Deep variational auto-encoder on 128x128 CelebA dataset.
- mnist_ac: Deep auto-encoder on MNIST dataset.
- mnist_dcgan: Least-square gan (generative adversarial) on MNIST dataset.
- mnist_lenet
Nodes | Nodes | Nodes | Nodes |
---|---|---|---|
data_generator | variable | place_holder | conv2d |
image_batch_generator | pooling | convolution_2d | transposed_conv2d |
image_reader | add | square | matmult |
mnist_reader | subtract | bias_add | dropout |
argmax | argmin | reduce_max | reduce_min |
reduce_mean | reduce_sum | reduce_absmax | reduce_norm1 |
reduce_norm2 | leaky_relu | sigmoid | relu |
tanh | clipped_relu | elu | phaseplexer |
random_selector | softmax_loss | euclidean_loss | |
display | psnr | softmax | equal |
cast_float | accumulator | batch_normalization | logger |
negate | multiplexer | random_selector | restructure |
softmax | ... |
- AdaDelta
- Adam
- RMSProp
- SGD