This tensorflow implementation code is based on Densely Connected Convolutional Networks.

FEATURES:

• Using tf.Dataset and tf.estimator which has better computational efficiency and more concise code
• Using the tf.keras.layers(not include dropout and batch_normalization) which has a good visualization in Tensorboard
• Add the Compute time Graph

Result

1. Adding a compute graph which inclding time and memory
2. Its accuracy rate can reach 94.33% using data augmentation in cifar10.

Manual

you only run experiment.py to get the result.

python experiment.py

Notice: Maybe it will have a error like "Couldn't open CUDA library libcupti.so.8.0 ".You need to add /usr/local/cuda/extras/CUPTI/lib64/ to your LD_LIBRARY_PATH. For detail, you can find there