ISR Traininig error Colab

[Leprechault]

I tried to use the Colab ISR Traininig tutorial.ipynb, despite the modification in the first line !pip install ISR to !pip install ISR --no-deps and install tensorflow before (!pip install tensorflow), and change nothing more, just only replicate the example in step Give the models to the Trainer:

from ISR.train import Trainer
loss_weights = {
  'generator': 0.0,
  'feature_extractor': 0.0833,
  'discriminator': 0.01
}
losses = {
  'generator': 'mae',
  'feature_extractor': 'mse',
  'discriminator': 'binary_crossentropy'
} 

log_dirs = {'logs': './logs', 'weights': './weights'}

learning_rate = {'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30}

flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}

trainer = Trainer(
    generator=rrdn,
    discriminator=discr,
    feature_extractor=f_ext,
    lr_train_dir='div2k/DIV2K_train_LR_bicubic/X2/',
    hr_train_dir='div2k/DIV2K_train_HR/',
    lr_valid_dir='div2k/DIV2K_train_LR_bicubic/X2/',
    hr_valid_dir='div2k/DIV2K_train_HR/',
    loss_weights=loss_weights,
    learning_rate=learning_rate,
    flatness=flatness,
    dataname='div2k',
    log_dirs=log_dirs,
    weights_generator=None,
    weights_discriminator=None,
    n_validation=40,
)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-6-5b1979e121e0>](https://localhost:8080/#) in <cell line: 19>()
     17 flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}
     18 
---> 19 trainer = Trainer(
     20     generator=rrdn,
     21     discriminator=discr,

4 frames
[/usr/local/lib/python3.10/dist-packages/ISR/train/trainer.py](https://localhost:8080/#) in __init__(self, generator, discriminator, feature_extractor, lr_train_dir, hr_train_dir, lr_valid_dir, hr_valid_dir, loss_weights, log_dirs, fallback_save_every_n_epochs, dataname, weights_generator, weights_discriminator, n_validation, flatness, learning_rate, adam_optimizer, losses, metrics)
    103             self.metrics['generator'] = PSNR
    104         self._parameters_sanity_check()
--> 105         self.model = self._combine_networks()
    106 
    107         self.settings = {}

[/usr/local/lib/python3.10/dist-packages/ISR/train/trainer.py](https://localhost:8080/#) in _combine_networks(self)
    197         combined = Model(inputs=lr, outputs=outputs)
    198         # https://stackoverflow.com/questions/42327543/adam-optimizer-goes-haywire-after-200k-batches-training-loss-grows
--> 199         optimizer = Adam(
    200             beta_1=self.adam_optimizer['beta1'],
    201             beta_2=self.adam_optimizer['beta2'],

[/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/adam.py](https://localhost:8080/#) in __init__(self, learning_rate, beta_1, beta_2, epsilon, amsgrad, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, loss_scale_factor, gradient_accumulation_steps, name, **kwargs)
     60         **kwargs,
     61     ):
---> 62         super().__init__(
     63             learning_rate=learning_rate,
     64             name=name,

[/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/optimizer.py](https://localhost:8080/#) in __init__(self, *args, **kwargs)
     20 
     21     def __init__(self, *args, **kwargs):
---> 22         super().__init__(*args, **kwargs)
     23         self._distribution_strategy = tf.distribute.get_strategy()
     24 

[/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/base_optimizer.py](https://localhost:8080/#) in __init__(self, learning_rate, weight_decay, clipnorm, clipvalue, global_clipnorm, use_ema, ema_momentum, ema_overwrite_frequency, loss_scale_factor, gradient_accumulation_steps, name, **kwargs)
     35             )
     36         if kwargs:
---> 37             raise ValueError(f"Argument(s) not recognized: {kwargs}")
     38 
     39         if name is None:

ValueError: Argument(s) not recognized: {'lr': 0.0004}

Many errors occur. Please, any help with it?