image-super-resolution | 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks | Machine Learning library

class Discriminator(tf.keras.Model):
    def __init__(self, data_format='channels_last'):
        super(Discriminator, self).__init__(name='')

        if data_format == 'channels_first':
            self._input_shape = [-1, 3, 128, 128]
              

Geosr
├── src
│   └── data_dir
├── dataset
│   └── save_dir
│       ├── all.csv
│       ├── train.csv
│       ├── test.csv
│       ├── val.csv
│       ├── statistic.csv
│       └── image
│           ├── train
│           ├── test
│           └── val
  

class Generator(tf.keras.Model):
    def __init__(self, data_format='channels_last'):
        super(Generator, self).__init__(name='')

        if data_format == 'channels_first':
            self._input_shape = [-1, 3, 32, 32]
            self.bn_ax