- tensorflow 1.14
- matplotlib
- pillow
- tqdm
- numpy
- pandas
- seaborn
- scikit-learn
- scipy
- jupyter
- opencv-python
- imageio
- h5py
- requests
- python 3.6
To train the model, run python train.py with the following arguments:
usage: train.py [-h] [--epochs EPOCHS] [--batch_size BATCH_SIZE]
[--sample_interval SAMPLE_INTERVAL]
[--checkpoint_interval CHECKPOINT_INTERVAL]
[--checkpoint_dir CHECKPOINT_DIR] [--dataset DATASET]
[--data_dir DATA_DIR] [--image_size IMAGE_SIZE]
[--num_workers NUM_WORKERS] [--latent_dim LATENT_DIM]
[--lr LR] [--beta1 BETA1] [--beta2 BETA2] [--n_critic N_CRITIC]
[--clip_value CLIP_VALUE] [--img_channels IMG_CHANNELS]
[--img_shape IMG_SHAPE]
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS number of epochs of training
--batch_size BATCH_SIZE
size of the batches
--sample_interval SAMPLE_INTERVAL
interval between image sampling
--checkpoint_interval CHECKPOINT_INTERVAL
interval between saving model checkpoints
--checkpoint_dir CHECKPOINT_DIR
directory for saving model checkpoints
--dataset DATASET dataset to train on
--data_dir DATA_DIR directory containing the dataset
--image_size IMAGE_SIZE
size of each image dimension
--num_workers NUM_WORKERS
number of workers for dataloader
--latent_dim LATENT_DIM
dimensionality of the latent space
--lr LR learning rate
--beta1 BETA1 adam: decay of first order momentum of gradient
--beta2 BETA2 adam: decay of first order momentum of gradient
--n_critic N_CRITIC number of training steps for discriminator per iter
--clip_value CLIP_VALUE
lower and upper clip value for disc. weights
--img_channels IMG_CHANNELS
number of image channels
--img_shape IMG_SHAPE
shape of each imageTo test the model, run python test.py with the following arguments