Simone Spaccarotella simonespa
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| # https://www.tensorflow.org/tutorials/distribute/keras | |
| logical_device_names = [logical_device.name for logical_device in tf.config.list_logical_devices()] | |
| if 'GPU' in ''.join(logical_device_names): | |
| distribution_strategy = tf.distribute.MirroredStrategy() | |
| elif 'TPU' in ''.join(logical_device_names): | |
| tf.tpu.experimental.initialize_tpu_system() | |
| tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='/device:TPU_SYSTEM:0') | |
| distribution_strategy = tf.distribute.experimental.TPUStrategy(tpu) |
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| ## To setup a project | |
| # Create an empty environment specifying the Python version | |
| conda create --name ml python=3.12 | |
| # Activate the environment | |
| conda activate ml | |
| # Add a new channel | |
| conda config --env --add channels conda-forge | |
| # Install a list of packages | |
| conda install ... |
simonespa
/ neuralnet.py
Created
May 17, 2024 09:23
Simple Feed-Forward Fully Connected Neural Network
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| import tensorflow as tf | |
| from tensorflow import keras | |
| nn = keras.Sequential(name='CovidClassification') | |
| nn.add(keras.Input(shape=(X_train.shape[1],))) | |
| nn.add(keras.layers.Dense( | |
| units=4, | |
| activation='leaky_relu', | |
| kernel_regularizer=keras.regularizers.L2(), | |
| bias_regularizer=keras.regularizers.L2(), |
simonespa
/ hashing_encoder.py
Created
April 17, 2024 09:42
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| from category_encoders import HashingEncoder | |
| N = unique[column] | |
| encoder = HashingEncoder( | |
| cols=[column], | |
| n_components=math.ceil(math.log2(N)), # the number of bits required to encode N elements | |
| hash_method='sha256' # https://docs.python.org/3/library/hashlib.html#constructors | |
| ) | |
| df = encoder.fit_transform(df) |
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| # Splits the values and expands them in multiple numbered columns | |
| temp_df = df[column].str.split("|", expand=True).fillna('') | |
| # One-Hot encodes all the values for each column | |
| temp_df = pd.get_dummies(temp_df).astype('uint8') | |
| # Removes the "N_" prefixe for each column to expose duplicates | |
| temp_df = remove_prefixes(temp_df) | |
| # Merges the duplicate columns |
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| ffmpeg -hide_banner -i image.jpeg -compression_level 100 out/image.jpeg |
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| def merge_columns(df: pd.DataFrame) -> pd.DataFrame: | |
| unique_columns = df.columns.unique() | |
| total_n_columns = len(df.columns) | |
| unique_n_columns = len(unique_columns) | |
| print(f"There are {total_n_columns} columns, {unique_n_columns} are unique") | |
| # if there are duplicates at all | |
| if total_n_columns != unique_n_columns: | |
| # get each unique colummn name |
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| ## | |
| # This method is quite slow for a large number of files (GBs) | |
| # Just wanted to save some parts of the entire snippet for reference | |
| # of some useful stuff like picking a row from a Dask dataframe | |
| # and appending it to a Pandas Dataframe. | |
| for id in unique_ids: | |
| # select all the rows across the partitions for the specified ID | |
| subset = catalogue[catalogue.id == id] | |
| # select only the one row with the latest date and time (max timestamp) |
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| <html> | |
| <head> | |
| <script type="text/javascript"> | |
| // uniformo la chiamata a getUserMedia rispetto ai vari prefissi sperimentali | |
| navigator.getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia; | |
| // uniformo la chiamata a window.URL sempre rispetto ai prefissi sperimentali | |
| window.URL = window.URL || window.webkitURL; | |
| // invoco la richiesta di accedere sia al flusso video che quello audio, la funzione |
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| object(box). object(monkey). object(banana). |