DenseNet_tensorflow

tensorflow implementation of Densely Connected Convolutional Networks

Requirements

• Tensorflow 1.x - GPU version recommended
• Python 3.x

Dataset

Please download dataset from this link Both Cifar10 and MNIST dataset are converted into tfrecords format for conveinence. Put train.tfrecords, test.tfrecords files into dataset/cifar10, dataset/mnist

You can create tfrecord file with your own dataset with dataset/dataset_generator.py.

python dataset_generator.py --image_dir ./cifar10/test/images --label_dir ./cifar10/test/labels --output_dir ./cifar10 --output_filename test.tfrecord

Options:

• --image_dir (str) - directory of your image files. it is recommended to set the name of images to integers like 0.png
• --label_dir (str) - directory of your label files. it is recommended to set the name of images to integers like 0.txt. label text file must contain class label in integer like 8.
• --output_dir (str) - directory for output tfrecord file.
• --outpuf_filename (str) - filename of output tfrecord file.

Training

Cifar 10

python train.py --class_num 10 --image_shape 32 32 3 --blocks 3 --layers 12 12 12 growth_rate 12 --dropout_rate 0.2 --compression_factor 1.0 --init_subsample False --learning_rate 0.1 --label_smoothing 0.1 --momentum 0.9 --weight_decay 0.0001 --train_set_size 50000 --val_set_size 10000 --batch_size 100 --epochs 60 --checkpoint_dir ./checkpoint --checkpoint_name densenet_cifar10 --train_record_dir ./dataset/cifar10/train.tfrecord --val_record_dir ./dataset/cifar10/test.tfrecord

Options:

• --class_num (int) - output number of class. Cifar10 has 10 classes.
• --image_shape (int nargs) - shape of input image. Cifar10 has 32 32 3 shape.
• --blocks (int) - the number of dense blocks
• --layers (int nargs) - the number of layers for each block. you need to provide them for each block
• --growth_rate (int) - growth rate of densenet
• --dropout_rate (float) - dropout rate
• --compression_factor (float) - compression factor for transition layer. 1.0 for no compressing.
• --init_subsample (bool) - do subsampling (striding) if true
• --learning_rate (float) - initial learning rate
• --label_smoothing (float) - label smoothing factor
• --momentum (float) - momentum from momentum optimizer
• --weight_decay (float) - weight decay factor
• --train_set_size (int) - number of training data. Cifar10 has 50000 data.
• --val_set_size (int) - number of validating data. I used test data for validation, so there are 10000 data.
• --batch_size (int) - size of mini batch
• --epochs (int) - number of epoch
• --checkpoint_dir (str) - directory to save checkpoint
• --checkpoint_name (str) - file name of checkpoint
• --train_record_dir (str) - file location of training set tfrecord
• --test_record_dir (str) - file location of test set tfrecord (for validation)

MNIST

python train.py --class_num 10 --image_shape 28 28 1 --blocks 3 --layers 12 12 12 growth_rate 12 --dropout_rate 0.0 --compression_factor 1.0 --init_subsample False --learning_rate 0.1 --label_smoothing 0.1 --momentum 0.9 --weight_decay 0.0001 --train_set_size 50000 --val_set_size 10000 --batch_size 100 --epochs 60 --checkpoint_dir ./checkpoint --checkpoint_name densenet_mnist --train_record_dir ./dataset/mnist/train.tfrecord --val_record_dir ./dataset/mnist/test.tfrecord

Options:

• options are same as Cifar10

Cifar100

python train.py --class_num 100 --image_shape 32 32 3 --blocks 3 --layers 12 12 12 growth_rate 12 --dropout_rate 0.0 --compression_factor 1.0 --init_subsample False --learning_rate 0.1 --label_smoothing 0.1 --momentum 0.9 --weight_decay 0.0001 --train_set_size 50000 --val_set_size 10000 --batch_size 100 --epochs 60 --checkpoint_dir ./checkpoint --checkpoint_name densenet_cifar100 --train_record_dir ./dataset/cifar100/train.tfrecord --val_record_dir ./dataset/cifar100/test.tfrecord

Options:

• options are same as Cifar10

Testing

python test.py --class_num 10 --checkpoint_dir ./checkpoint/best --test_record_dir ./dataset/cifar10/test.tfrecord --batch_size 256

Options:

• --class_num (int) - the number of classes
• --checkpoint_dir (str) - directory for the checkpoint you want to load and test
• --test_record_dir (str) - directory for the test dataset
• --batch_size (int) - batch size for testing

test.py loads network graph and tensors from meta data and evalutes.