Skip to content
View hsahovic's full-sized avatar

Highlights

  • Pro

Block or report hsahovic

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse

Pinned Loading

  1. poke-env poke-env Public

    A python interface for training Reinforcement Learning bots to battle on pokemon showdown

    Python 299 106

  2. Speech-to-maths Speech-to-maths Public

    Speech-recognition for Latex generation

    CSS 6 2

  3. Pokemon-Showdown Pokemon-Showdown Public

    Forked from smogon/pokemon-showdown

    Pokemon-Showdown fork optimized for RL training performance.

    TypeScript 11 7

  4. This function takes a tensorflow dat... This function takes a tensorflow dataset and returns a corresponding dataset implementing cutmix
    1
    def tf_ds_cutmix(ds, shuffling=1024):
    2
        ds_shuffled = ds.shuffle(shuffling)
    3
        
    4
        def cutmix(p1, p2):
    5
            img_1, label_1 = p1
  5. This snippet was used to recover a p... This snippet was used to recover a proper keras model from a saved model which contained a submodel (ie., one of its layers was actually another model), in order to apply model optimization a posteriori (quantization, pruning). It can be extended to handle more type of layers.
    1
    from tensorflow.keras.models import Sequential
    2
    import tensorflow.keras.layers as keras_layers
    3
    
                  
    4
    
                  
    5
    clone = Sequential()
  6. A custom tensorflow / keras loss imp... A custom tensorflow / keras loss implementing OHEM (https://arxiv.org/abs/1604.03540) with cross-entropy.
    1
    import tensorflow as tf
    2
    from tensorflow.keras.losses import categorical_crossentropy
    3
    
                  
    4
    @tf.function
    5
    def ohem_crossentropy_loss(y_true, y_pred):