Files
2024-12-10 23:34:07 +01:00

788 KiB

In [1]:
!apt-get update
!apt-get install graphviz -y

!pip install tensorflow
!pip install numpy
!pip install pandas

!pip install keras
!pip install scikit-learn
!pip install matplotlib
!pip install joblib
!pip install pyarrow
!pip install fastparquet
!pip install scipy
!pip install seaborn
!pip install tqdm
!pip install pydot
!pip install tensorflow-io
!pip install tensorflow-addons
Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease
Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]      
Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]    
Get:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]
Fetched 384 kB in 1s (519 kB/s)                                   
Reading package lists... Done
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graphviz is already the newest version (2.42.2-6ubuntu0.1).
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In [2]:
import tensorflow as tf
import keras

print(f"Keras version: {keras.__version__}")
print(f"TensorFlow version: {tf.__version__}")
print(f"TensorFlow version: {tf.__version__}")
print(f"CUDA available: {tf.test.is_built_with_cuda()}")
print(f"GPU devices: {tf.config.list_physical_devices('GPU')}")

# GPU configuration
import tensorflow as tf
import os

# Limita la crescita della memoria GPU
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Imposta la crescita di memoria dinamica
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
            
        # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)
        # tf.config.experimental.set_virtual_device_configuration(
        #     gpus[0],
        #     [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)]  # 4GB
        # )
        
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        print(e)
        
# Imposta le opzioni di logging
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Riduce i messaggi di log
        
# Configura la modalità mista di precisione
tf.keras.mixed_precision.set_global_policy('float32')

# Imposta il seed per la riproducibilità
##tf.random.set_seed(42)
2024-12-07 07:35:27.011449: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-12-07 07:35:27.011494: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-12-07 07:35:27.011539: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-07 07:35:27.020703: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Keras version: 2.14.0
TensorFlow version: 2.14.0
TensorFlow version: 2.14.0
CUDA available: True
GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
1 Physical GPUs, 1 Logical GPUs
2024-12-07 07:35:29.539283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9725 MB memory:  -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import tensorflow_addons as tfa
from datetime import datetime
import os
import joblib
import re
from typing import List

random_state_value = None
execute_name = datetime.now().strftime("%Y-%m-%d_%H-%M")

base_project_dir = './'
data_dir = '../../sources/'
models_project_dir = base_project_dir

os.makedirs(base_project_dir, exist_ok=True)
os.makedirs(models_project_dir, exist_ok=True)
/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: 

TensorFlow Addons (TFA) has ended development and introduction of new features.
TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.
Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). 

For more information see: https://github.com/tensorflow/addons/issues/2807 

  warnings.warn(
In [4]:
def clean_column_name(name: str) -> str:
    """
    Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.

    Parameters
    ----------
    name : str
        Nome della colonna da pulire

    Returns
    -------
    str
        Nome della colonna pulito
    """
    # Rimuove caratteri speciali
    name = re.sub(r'[^a-zA-Z0-9\s]', '', name)
    # Converte in snake_case
    name = name.lower().replace(' ', '_')

    # Abbreviazioni comuni
    abbreviations = {
        'production': 'prod',
        'percentage': 'pct',
        'hectare': 'ha',
        'tonnes': 't',
        'litres': 'l',
        'minimum': 'min',
        'maximum': 'max',
        'average': 'avg'
    }

    for full, abbr in abbreviations.items():
        name = name.replace(full, abbr)

    return name


def clean_column_names(df: pd.DataFrame) -> List[str]:
    """
    Pulisce tutti i nomi delle colonne in un DataFrame.

    Parameters
    ----------
    df : pd.DataFrame
        DataFrame con le colonne da pulire

    Returns
    -------
    list
        Lista dei nuovi nomi delle colonne puliti
    """
    new_columns = []

    for col in df.columns:
        # Usa regex per separare le varietà
        varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)
        if varieties:
            new_columns.append(f"{varieties[0][0]}_{varieties[0][1]}")
        else:
            new_columns.append(col)

    return new_columns
In [5]:
def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):
    os.makedirs(output_dir, exist_ok=True)
    filename = "".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()
    filename = filename.replace(' ', '_').lower()
    filepath = os.path.join(output_dir, f"{filename}.png")
    plt.savefig(filepath, bbox_inches='tight', dpi=300)
    print(f"Plot salvato come: {filepath}")


def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):
    if not os.path.exists(mapping_path):
        raise FileNotFoundError(f"Mapping not found at {mapping_path}. Run create_technique_mapping first.")

    technique_mapping = joblib.load(mapping_path)

    # Trova tutte le colonne delle tecniche
    tech_columns = [col for col in df.columns if col.endswith('_tech')]

    # Applica il mapping a tutte le colonne delle tecniche
    for col in tech_columns:
        df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)

    return df


def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):
    if not os.path.exists(mapping_path):
        raise FileNotFoundError(f"Mapping not found at {mapping_path}")

    technique_mapping = joblib.load(mapping_path)
    reverse_mapping = {v: k for k, v in technique_mapping.items()}
    reverse_mapping[0] = ''

    return reverse_mapping.get(technique_value, '')


def prepare_comparison_data(simulated_data, olive_varieties):
    # Pulisci i nomi delle colonne
    df = simulated_data.copy()

    df.columns = clean_column_names(df)
    df = encode_techniques(df)

    all_varieties = olive_varieties['Varietà di Olive'].unique()
    varieties = [clean_column_name(variety) for variety in all_varieties]
    comparison_data = []

    for variety in varieties:
        olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)
        oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)
        tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)
        water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)

        if olive_prod_col and oil_prod_col and tech_col and water_need_col:
            variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]
            variety_data = variety_data[variety_data[tech_col] != 0]  # Esclude le righe dove la tecnica è 0

            if not variety_data.empty:
                avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()
                avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()
                avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()
                efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0
                water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0

                comparison_data.append({
                    'Variety': variety,
                    'Avg Olive Production (kg/ha)': avg_olive_prod,
                    'Avg Oil Production (L/ha)': avg_oil_prod,
                    'Avg Water Need (m³/ha)': avg_water_need,
                    'Oil Efficiency (L/kg)': efficiency,
                    'Water Efficiency (L oil/m³ water)': water_efficiency
                })

    return pd.DataFrame(comparison_data)


def plot_variety_comparison(comparison_data, metric):
    plt.figure(figsize=(12, 6))
    bars = plt.bar(comparison_data['Variety'], comparison_data[metric])
    plt.title(f'Comparison of {metric} across Olive Varieties')
    plt.xlabel('Variety')
    plt.ylabel(metric)
    plt.xticks(rotation=45, ha='right')

    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width() / 2., height,
                 f'{height:.2f}',
                 ha='center', va='bottom')

    plt.tight_layout()
    plt.show()
    save_plot(plt, f'variety_comparison_{metric.lower().replace(" ", "_").replace("/", "_").replace("(", "").replace(")", "")}')
    plt.close()


def plot_efficiency_vs_production(comparison_data):
    plt.figure(figsize=(10, 6))

    plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],
                comparison_data['Oil Efficiency (L/kg)'],
                s=100)

    for i, row in comparison_data.iterrows():
        plt.annotate(row['Variety'],
                     (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),
                     xytext=(5, 5), textcoords='offset points')

    plt.title('Oil Efficiency vs Olive Production by Variety')
    plt.xlabel('Average Olive Production (kg/ha)')
    plt.ylabel('Oil Efficiency (L oil / kg olives)')
    plt.tight_layout()
    save_plot(plt, 'efficiency_vs_production')
    plt.close()


def plot_water_efficiency_vs_production(comparison_data):
    plt.figure(figsize=(10, 6))

    plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],
                comparison_data['Water Efficiency (L oil/m³ water)'],
                s=100)

    for i, row in comparison_data.iterrows():
        plt.annotate(row['Variety'],
                     (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),
                     xytext=(5, 5), textcoords='offset points')

    plt.title('Water Efficiency vs Olive Production by Variety')
    plt.xlabel('Average Olive Production (kg/ha)')
    plt.ylabel('Water Efficiency (L oil / m³ water)')
    plt.tight_layout()
    plt.show()
    save_plot(plt, 'water_efficiency_vs_production')
    plt.close()


def plot_water_need_vs_oil_production(comparison_data):
    plt.figure(figsize=(10, 6))

    plt.scatter(comparison_data['Avg Water Need (m³/ha)'],
                comparison_data['Avg Oil Production (L/ha)'],
                s=100)

    for i, row in comparison_data.iterrows():
        plt.annotate(row['Variety'],
                     (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),
                     xytext=(5, 5), textcoords='offset points')

    plt.title('Oil Production vs Water Need by Variety')
    plt.xlabel('Average Water Need (m³/ha)')
    plt.ylabel('Average Oil Production (L/ha)')
    plt.tight_layout()
    plt.show()
    save_plot(plt, 'water_need_vs_oil_production')
    plt.close()


def analyze_by_technique(simulated_data, olive_varieties):
    # Pulisci i nomi delle colonne
    df = simulated_data.copy()

    df.columns = clean_column_names(df)
    df = encode_techniques(df)
    all_varieties = olive_varieties['Varietà di Olive'].unique()
    varieties = [clean_column_name(variety) for variety in all_varieties]

    technique_data = []

    for variety in varieties:
        olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)
        oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)
        tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)
        water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)

        if olive_prod_col and oil_prod_col and tech_col and water_need_col:
            variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]
            variety_data = variety_data[variety_data[tech_col] != 0]

            if not variety_data.empty:
                for tech in variety_data[tech_col].unique():
                    tech_data = variety_data[variety_data[tech_col] == tech]

                    avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()
                    avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()
                    avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()

                    efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0
                    water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0

                    technique_data.append({
                        'Variety': variety,
                        'Technique': tech,
                        'Technique String': decode_single_technique(tech),
                        'Avg Olive Production (kg/ha)': avg_olive_prod,
                        'Avg Oil Production (L/ha)': avg_oil_prod,
                        'Avg Water Need (m³/ha)': avg_water_need,
                        'Oil Efficiency (L/kg)': efficiency,
                        'Water Efficiency (L oil/m³ water)': water_efficiency
                    })

    return pd.DataFrame(technique_data)
In [6]:
def calculate_real_error(model, test_data, test_targets, scaler_y):
    # Fare predizioni
    predictions = model.predict(test_data)

    # Denormalizzare predizioni e target
    predictions_real = scaler_y.inverse_transform(predictions)
    targets_real = scaler_y.inverse_transform(test_targets)

    # Calcolare errore percentuale per ogni target
    percentage_errors = []
    absolute_errors = []

    for i in range(predictions_real.shape[1]):
        mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))
        mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100
        percentage_errors.append(mape)
        absolute_errors.append(mae)

    # Stampa risultati per ogni target
    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']

    print("\nErrori per target:")
    print("-" * 50)
    for i, target in enumerate(target_names):
        print(f"{target}:")
        print(f"MAE assoluto: {absolute_errors[i]:.2f}")
        print(f"Errore percentuale medio: {percentage_errors[i]:.2f}%")
        print(f"Precisione: {100 - percentage_errors[i]:.2f}%")
        print("-" * 50)

    return percentage_errors, absolute_errors
In [7]:
simulated_data = pd.read_parquet(f"{data_dir}olive_training_dataset.parquet")
olive_varieties = pd.read_parquet(f"{data_dir}olive_varieties.parquet")
# Esecuzione dell'analisi
comparison_data = prepare_comparison_data(simulated_data, olive_varieties)

# Genera i grafici
plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')
plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')
plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')
plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')
plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')
plot_efficiency_vs_production(comparison_data)
plot_water_efficiency_vs_production(comparison_data)
plot_water_need_vs_oil_production(comparison_data)

# Analisi per tecnica
technique_data = analyze_by_technique(simulated_data, olive_varieties)

print(technique_data)

# Stampa un sommario statistico
print("Comparison by Variety:")
print(comparison_data.set_index('Variety'))
print("\nBest Varieties by Water Efficiency:")
print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())
Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_olive_production_kg_ha.png
Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_oil_production_l_ha.png
Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_water_need_m³_ha.png
Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_oil_efficiency_l_kg.png
Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_water_efficiency_l_oil_m³_water.png
Plot salvato come: .//2024-12-07_07-35_plots/efficiency_vs_production.png
Plot salvato come: .//2024-12-07_07-35_plots/water_efficiency_vs_production.png
Plot salvato come: .//2024-12-07_07-35_plots/water_need_vs_oil_production.png
               Variety  Technique Technique String  \
0   nocellara_delletna          3     tradizionale   
1   nocellara_delletna          1        intensiva   
2   nocellara_delletna          2   superintensiva   
3              leccino          1        intensiva   
4              leccino          2   superintensiva   
5              leccino          3     tradizionale   
6             frantoio          2   superintensiva   
7             frantoio          3     tradizionale   
8             frantoio          1        intensiva   
9             coratina          1        intensiva   
10            coratina          2   superintensiva   
11            coratina          3     tradizionale   
12           taggiasca          3     tradizionale   
13           taggiasca          2   superintensiva   
14           taggiasca          1        intensiva   
15           pendolino          1        intensiva   
16           pendolino          2   superintensiva   
17           pendolino          3     tradizionale   
18            moraiolo          2   superintensiva   
19            moraiolo          1        intensiva   
20            moraiolo          3     tradizionale   

    Avg Olive Production (kg/ha)  Avg Oil Production (L/ha)  \
0                    9564.638687                2088.362004   
1                   13699.079622                2991.183032   
2                   17826.710664                3892.059753   
3                   16432.379678                3229.053194   
4                   20528.499013                4033.942398   
5                   10937.982122                2149.449585   
6                   24621.040119                6047.876212   
7                   13740.739760                3375.103688   
8                   20550.900635                5047.942655   
9                   16429.706879                4215.265516   
10                  19164.700743                4916.649709   
11                  12318.510310                3160.037128   
12                   6839.506230                1381.247995   
13                  16433.741502                3319.210170   
14                  10968.603159                2215.371493   
15                  13705.431414                2468.678455   
16                  19183.689269                3455.879324   
17                  10960.549241                1974.357984   
18                  17793.971752                3885.415851   
19                  13144.222436                2870.020002   
20                   8765.195655                1913.745255   

    Avg Water Need (m³/ha)  Oil Efficiency (L/kg)  \
0             32997.227891               0.218342   
1             33079.012125               0.218349   
2             33118.708645               0.218327   
3             25013.303736               0.196506   
4             24989.459147               0.196504   
5             24981.219100               0.196512   
6             28874.473543               0.245639   
7             29003.452741               0.245628   
8             28921.261327               0.245631   
9             38270.638622               0.256564   
10            38264.650562               0.256547   
11            38253.676395               0.256528   
12            26219.134374               0.201951   
13            26253.317778               0.201975   
14            26284.027794               0.201974   
15            26154.359691               0.180124   
16            26153.199618               0.180147   
17            26152.823801               0.180133   
18            32561.911109               0.218356   
19            32577.899255               0.218348   
20            32594.860153               0.218335   

    Water Efficiency (L oil/m³ water)  
0                            0.063289  
1                            0.090425  
2                            0.117518  
3                            0.129093  
4                            0.161426  
5                            0.086043  
6                            0.209454  
7                            0.116369  
8                            0.174541  
9                            0.110144  
10                           0.128491  
11                           0.082607  
12                           0.052681  
13                           0.126430  
14                           0.084286  
15                           0.094389  
16                           0.132140  
17                           0.075493  
18                           0.119324  
19                           0.088097  
20                           0.058713  
Comparison by Variety:
                    Avg Olive Production (kg/ha)  Avg Oil Production (L/ha)  \
Variety                                                                       
nocellara_delletna                  13696.683690                2990.507461   
leccino                             15971.162702                3138.439782   
frantoio                            19648.631813                4826.360700   
coratina                            15974.164423                4098.136472   
taggiasca                           11412.636779                2305.011278   
pendolino                           14617.432649                2633.129635   
moraiolo                            13232.961913                2889.399172   

                    Avg Water Need (m³/ha)  Oil Efficiency (L/kg)  \
Variety                                                             
nocellara_delletna            33064.983905               0.218338   
leccino                       24994.676451               0.196507   
frantoio                      28932.932409               0.245633   
coratina                      38262.995517               0.256548   
taggiasca                     26252.184893               0.201970   
pendolino                     26153.461822               0.180136   
moraiolo                      32578.228327               0.218349   

                    Water Efficiency (L oil/m³ water)  
Variety                                                
nocellara_delletna                           0.090443  
leccino                                      0.125564  
frantoio                                     0.166812  
coratina                                     0.107104  
taggiasca                                    0.087803  
pendolino                                    0.100680  
moraiolo                                     0.088691  

Best Varieties by Water Efficiency:
              Variety  Avg Olive Production (kg/ha)  \
2            frantoio                  19648.631813   
1             leccino                  15971.162702   
3            coratina                  15974.164423   
5           pendolino                  14617.432649   
0  nocellara_delletna                  13696.683690   

   Avg Oil Production (L/ha)  Avg Water Need (m³/ha)  Oil Efficiency (L/kg)  \
2                4826.360700            28932.932409               0.245633   
1                3138.439782            24994.676451               0.196507   
3                4098.136472            38262.995517               0.256548   
5                2633.129635            26153.461822               0.180136   
0                2990.507461            33064.983905               0.218338   

   Water Efficiency (L oil/m³ water)  
2                           0.166812  
1                           0.125564  
3                           0.107104  
5                           0.100680  
0                           0.090443  
In [8]:
def prepare_transformer_data(df, olive_varieties_df):
    # Crea una copia del DataFrame per evitare modifiche all'originale
    df = df.copy()

    # Ordina per zona e anno
    df = df.sort_values(['zone', 'year'])

    # Definisci le feature
    temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']
    static_features = ['ha']  # Feature statiche base
    target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']

    # Ottieni le varietà pulite
    all_varieties = olive_varieties_df['Varietà di Olive'].unique()
    varieties = [clean_column_name(variety) for variety in all_varieties]

    # Crea la struttura delle feature per ogni varietà
    variety_features = [
        'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',
        'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',
        'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'
    ]

    # Prepara dizionari per le nuove colonne
    new_columns = {}

    # Prepara le feature per ogni varietà
    for variety in varieties:
        # Feature esistenti
        for feature in variety_features:
            col_name = f"{variety}_{feature}"
            if col_name in df.columns:
                if feature != 'tech':  # Non includere la colonna tech direttamente
                    static_features.append(col_name)

        # Feature binarie per le tecniche di coltivazione
        for technique in ['tradizionale', 'intensiva', 'superintensiva']:
            col_name = f"{variety}_{technique}"
            new_columns[col_name] = df[f"{variety}_tech"].notna() & (
                    df[f"{variety}_tech"].str.lower() == technique
            ).fillna(False)
            static_features.append(col_name)

    # Aggiungi tutte le nuove colonne in una volta sola
    new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)

    # Ordiniamo per zona e anno per mantenere la continuità temporale
    df_sorted = new_df.sort_values(['zone', 'year'])

    # Definiamo la dimensione della finestra temporale
    window_size = 41

    # Liste per raccogliere i dati
    temporal_sequences = []
    static_features_list = []
    targets_list = []

    # Iteriamo per ogni zona
    for zone in df_sorted['zone'].unique():
        zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)

        if len(zone_data) >= window_size:  # Verifichiamo che ci siano abbastanza dati
            # Creiamo sequenze temporali scorrevoli
            for i in range(len(zone_data) - window_size + 1):
                # Sequenza temporale
                temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values
                # Verifichiamo che non ci siano valori NaN
                if not np.isnan(temporal_window).any():
                    temporal_sequences.append(temporal_window)

                    # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)
                    static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)

                    # Target (prendiamo quelli dell'ultimo timestep della finestra)
                    targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)

    # Convertiamo in array numpy
    X_temporal = np.array(temporal_sequences)
    X_static = np.array(static_features_list)
    y = np.array(targets_list)

    print(f"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}")

    # Split dei dati (usando indici casuali per una migliore distribuzione)
    indices = np.random.permutation(len(X_temporal))

    #train_idx = int(len(indices) * 0.7)        # 70% training
    #val_idx = int(len(indices) * 0.85)         # 15% validation
    # Il resto rimane 15% test

    train_idx = int(len(indices) * 0.65)        # 65% training
    val_idx = int(len(indices) * 0.85)          # 20% validation
    # Il resto rimane 15% test

    #train_idx = int(len(indices) * 0.60)       # 60% training
    #val_idx = int(len(indices) * 0.85)         # 25% validation
    # Il resto rimane 15% test

    train_indices = indices[:train_idx]
    val_indices = indices[train_idx:val_idx]
    test_indices = indices[val_idx:]

    # Split dei dati
    X_temporal_train = X_temporal[train_indices]
    X_temporal_val = X_temporal[val_indices]
    X_temporal_test = X_temporal[test_indices]

    X_static_train = X_static[train_indices]
    X_static_val = X_static[val_indices]
    X_static_test = X_static[test_indices]

    y_train = y[train_indices]
    y_val = y[val_indices]
    y_test = y[test_indices]

    # Standardizzazione
    scaler_temporal = StandardScaler()
    scaler_static = StandardScaler()
    scaler_y = StandardScaler()

    # Standardizzazione dei dati temporali
    X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)
    X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)
    X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)

    # Standardizzazione dei dati statici
    X_static_train = scaler_static.fit_transform(X_static_train)
    X_static_val = scaler_static.transform(X_static_val)
    X_static_test = scaler_static.transform(X_static_test)

    # Standardizzazione dei target
    y_train = scaler_y.fit_transform(y_train)
    y_val = scaler_y.transform(y_val)
    y_test = scaler_y.transform(y_test)

    print("\nShape dopo lo split e standardizzazione:")
    print(f"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}")
    print(f"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}")
    print(f"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}")

    # Prepara i dizionari di input
    train_data = {'temporal': X_temporal_train, 'static': X_static_train}
    val_data = {'temporal': X_temporal_val, 'static': X_static_val}
    test_data = {'temporal': X_temporal_test, 'static': X_static_test}

    joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))
    joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))
    joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))

    return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)
In [9]:
simulated_data = pd.read_parquet(f"{data_dir}olive_training_dataset.parquet")
olive_varieties = pd.read_parquet(f"{data_dir}olive_varieties.parquet")

(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)

scaler_temporal, scaler_static, scaler_y = scalers

print("Temporal data shape:", train_data['temporal'].shape)
print("Static data shape:", train_data['static'].shape)
print("Target shape:", train_targets.shape)
Dataset completo - Temporal: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)

Shape dopo lo split e standardizzazione:
Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)
Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)
Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)
Temporal data shape: (2548000, 41, 3)
Static data shape: (2548000, 113)
Target shape: (2548000, 5)
In [10]:
@keras.saving.register_keras_serializable()
class DataAugmentation(tf.keras.layers.Layer):
    """Custom layer per l'augmentation dei dati"""

    def __init__(self, noise_stddev=0.03, **kwargs):
        super().__init__(**kwargs)
        self.noise_stddev = noise_stddev

    def call(self, inputs, training=None):
        if training:
            return inputs + tf.random.normal(
                shape=tf.shape(inputs),
                mean=0.0,
                stddev=self.noise_stddev
            )
        return inputs

    def get_config(self):
        config = super().get_config()
        config.update({"noise_stddev": self.noise_stddev})
        return config


@keras.saving.register_keras_serializable()
class PositionalEncoding(tf.keras.layers.Layer):
    """Custom layer per l'encoding posizionale"""

    def __init__(self, d_model, **kwargs):
        super().__init__(**kwargs)
        self.d_model = d_model

    def build(self, input_shape):
        _, seq_length, _ = input_shape

        # Crea la matrice di encoding posizionale
        position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]
        div_term = tf.exp(
            tf.range(0, self.d_model, 2, dtype=tf.float32) *
            (-tf.math.log(10000.0) / self.d_model)
        )

        # Calcola sin e cos
        pos_encoding = tf.zeros((1, seq_length, self.d_model))
        pos_encoding_even = tf.sin(position * div_term)
        pos_encoding_odd = tf.cos(position * div_term)

        # Assegna i valori alle posizioni pari e dispari
        pos_encoding = tf.concat(
            [tf.expand_dims(pos_encoding_even, -1),
             tf.expand_dims(pos_encoding_odd, -1)],
            axis=-1
        )
        pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))
        pos_encoding = pos_encoding[:, :, :self.d_model]

        # Salva l'encoding come peso non trainabile
        self.pos_encoding = self.add_weight(
            shape=(1, seq_length, self.d_model),
            initializer=tf.keras.initializers.Constant(pos_encoding),
            trainable=False,
            name='positional_encoding'
        )

        super().build(input_shape)

    def call(self, inputs):
        # Broadcast l'encoding posizionale sul batch
        batch_size = tf.shape(inputs)[0]
        pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])
        return inputs + pos_encoding_tiled

    def get_config(self):
        config = super().get_config()
        config.update({"d_model": self.d_model})
        return config


@keras.saving.register_keras_serializable()
class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
    """Custom learning rate schedule with linear warmup and exponential decay."""

    def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):
        super().__init__()
        self.initial_learning_rate = initial_learning_rate
        self.warmup_steps = warmup_steps
        self.decay_steps = decay_steps

    def __call__(self, step):
        warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps
        warmup_lr = self.initial_learning_rate * warmup_pct
        decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)
        decayed_lr = self.initial_learning_rate * decay_factor
        return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)

    def get_config(self):
        return {
            'initial_learning_rate': self.initial_learning_rate,
            'warmup_steps': self.warmup_steps,
            'decay_steps': self.decay_steps
        }


def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,
                                 d_model=128, num_heads=8, ff_dim=256,
                                 num_transformer_blocks=4, mlp_units=None,
                                 dropout=0.2):
    """
    Crea un transformer per la predizione della produzione di olio d'oliva.
    """
    # Input layers
    if mlp_units is None:
        mlp_units = [256, 128, 64]

    temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')
    static_input = tf.keras.layers.Input(shape=static_shape, name='static')

    # === TEMPORAL PATH ===
    x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)
    x = DataAugmentation()(x)

    # Temporal projection
    x = tf.keras.layers.Dense(
        d_model // 2,
        activation='swish',
        kernel_regularizer=tf.keras.regularizers.l2(1e-5)
    )(x)
    x = tf.keras.layers.Dropout(dropout)(x)
    x = tf.keras.layers.Dense(
        d_model,
        activation='swish',
        kernel_regularizer=tf.keras.regularizers.l2(1e-5)
    )(x)

    # Positional encoding
    x = PositionalEncoding(d_model)(x)

    # Transformer blocks
    skip_connection = x
    for _ in range(num_transformer_blocks):
        # Self-attention
        attention_output = tf.keras.layers.MultiHeadAttention(
            num_heads=num_heads,
            key_dim=d_model // num_heads,
            value_dim=d_model // num_heads
        )(x, x)
        attention_output = tf.keras.layers.Dropout(dropout)(attention_output)

        # Residual connection con pesi addestrabili
        residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)
        x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])
        x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)

        # Feed-forward network
        ffn = tf.keras.layers.Dense(ff_dim, activation="swish")(x)
        ffn = tf.keras.layers.Dropout(dropout)(ffn)
        ffn = tf.keras.layers.Dense(d_model)(ffn)
        ffn = tf.keras.layers.Dropout(dropout)(ffn)

        # Second residual connection
        x = tfa.layers.StochasticDepth()([x, ffn])
        x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)

    # Add final skip connection
    x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])

    # Temporal pooling
    attention_pooled = tf.keras.layers.MultiHeadAttention(
        num_heads=num_heads,
        key_dim=d_model // 4
    )(x, x)
    attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)

    # Additional pooling operations
    avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)
    max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)

    # Combine pooling results
    temporal_features = tf.keras.layers.Concatenate()(
        [attention_pooled, avg_pooled, max_pooled]
    )

    # === STATIC PATH ===
    static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)
    for units in [256, 128, 64]:
        static_features = tf.keras.layers.Dense(
            units,
            activation='swish',
            kernel_regularizer=tf.keras.regularizers.l2(1e-5)
        )(static_features)
        static_features = tf.keras.layers.Dropout(dropout)(static_features)

    # === FEATURE FUSION ===
    combined = tf.keras.layers.Concatenate()([temporal_features, static_features])

    # === MLP HEAD ===
    x = combined
    for units in mlp_units:
        x = tf.keras.layers.BatchNormalization()(x)
        x = tf.keras.layers.Dense(
            units,
            activation="swish",
            kernel_regularizer=tf.keras.regularizers.l2(1e-5)
        )(x)
        x = tf.keras.layers.Dropout(dropout)(x)

    # Output layer
    outputs = tf.keras.layers.Dense(
        num_outputs,
        activation='linear',
        kernel_regularizer=tf.keras.regularizers.l2(1e-5)
    )(x)

    # Create model
    model = tf.keras.Model(
        inputs={'temporal': temporal_input, 'static': static_input},
        outputs=outputs,
        name='OilTransformer'
    )

    return model


def create_transformer_callbacks(target_names, val_data, val_targets):
    """
    Crea i callbacks per il training del modello.
    
    Parameters:
    -----------
    target_names : list
        Lista dei nomi dei target per il monitoraggio specifico
    val_data : dict
        Dati di validazione
    val_targets : array
        Target di validazione
    
    Returns:
    --------
    list
        Lista dei callbacks configurati
    """

    # Custom Metric per target specifici
    class TargetSpecificMetric(tf.keras.callbacks.Callback):
        def __init__(self, validation_data, target_names):
            super().__init__()
            self.validation_data = validation_data
            self.target_names = target_names

        def on_epoch_end(self, epoch, logs={}):
            x_val, y_val = self.validation_data
            y_pred = self.model.predict(x_val, verbose=0)

            for i, name in enumerate(self.target_names):
                mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))
                logs[f'val_{name}_mae'] = mae


    callbacks = [
        # Early Stopping
        tf.keras.callbacks.EarlyStopping(
            monitor='val_loss',
            patience=20,
            restore_best_weights=True,
            min_delta=0.0005,
            mode='min'
        ),

        # Model Checkpoint
        tf.keras.callbacks.ModelCheckpoint(
            filepath=f'{execute_name}_best_oil_model.h5',
            monitor='val_loss',
            save_best_only=True,
            mode='min',
            save_weights_only=True
        ),

        # Metric per target specifici
        TargetSpecificMetric(
            validation_data=(val_data, val_targets),
            target_names=target_names
        ),

        # Reduce LR on Plateau
        tf.keras.callbacks.ReduceLROnPlateau(
            monitor='val_loss',
            factor=0.5,
            patience=10,
            min_lr=1e-6,
            verbose=1
        ),

        # TensorBoard logging
        tf.keras.callbacks.TensorBoard(
            log_dir=f'./logs_{execute_name}',
            histogram_freq=1,
            write_graph=True,
            update_freq='epoch'
        )
    ]

    return callbacks


def compile_model(model, learning_rate=1e-3):
    """
    Compila il modello con le impostazioni standard.
    """
    lr_schedule = WarmUpLearningRateSchedule(
        initial_learning_rate=learning_rate,
        warmup_steps=500,
        decay_steps=5000
    )

    model.compile(
        optimizer=tf.keras.optimizers.AdamW(
            learning_rate=lr_schedule,
            weight_decay=0.01
        ),
        loss=tf.keras.losses.Huber(),
        metrics=['mae']
    )

    return model


def setup_transformer_training(train_data, train_targets, val_data, val_targets):
    """
    Configura e prepara il transformer con dimensioni dinamiche basate sui dati.
    """
    # Estrai le shape dai dati
    temporal_shape = (train_data['temporal'].shape[1], train_data['temporal'].shape[2])
    static_shape = (train_data['static'].shape[1],)
    num_outputs = train_targets.shape[1]

    print(f"Shape rilevate:")
    print(f"- Temporal shape: {temporal_shape}")
    print(f"- Static shape: {static_shape}")
    print(f"- Numero di output: {num_outputs}")

    # Target names basati sul numero di output
    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']

    # Assicurati che il numero di target names corrisponda al numero di output
    assert len(target_names) == num_outputs, \
        f"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})"

    # Crea il modello con le dimensioni rilevate
    model = create_olive_oil_transformer(
        temporal_shape=temporal_shape,
        static_shape=static_shape,
        num_outputs=num_outputs
    )

    # Compila il modello
    model = compile_model(model)

    # Crea i callbacks
    callbacks = create_transformer_callbacks(target_names, val_data, val_targets)

    return model, callbacks, target_names


def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):
    """
    Funzione principale per l'addestramento del transformer con ottimizzazioni.
    """
    # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria
    train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\
        .cache()\
        .shuffle(buffer_size=1024)\
        .batch(batch_size)\
        .prefetch(tf.data.AUTOTUNE)

    val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\
        .cache()\
        .batch(batch_size)\
        .prefetch(tf.data.AUTOTUNE)

    # Setup del modello
    strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()
    
    with strategy.scope():
        model, callbacks, target_names = setup_transformer_training(
            train_data, train_targets, val_data, val_targets
        )

    # Mostra il summary del modello
    model.summary()
    
    try:
        keras.utils.plot_model(model, f"{execute_name}_{save_name}.png", show_shapes=True)
    except Exception as e:
        print(f"Warning: Could not create model plot: {e}")

    # Training con gestione degli errori
    try:
        history = model.fit(
            train_dataset,
            validation_data=val_dataset,
            epochs=epochs,
            callbacks=callbacks,
            verbose=1,
            workers=4,
            use_multiprocessing=True
        )
    except tf.errors.ResourceExhaustedError:
        print("Memoria GPU esaurita, riprovo con batch size più piccolo...")
        # Riprova con batch size più piccolo
        batch_size = batch_size // 2
        train_dataset = train_dataset.unbatch().batch(batch_size)
        val_dataset = val_dataset.unbatch().batch(batch_size)
        history = model.fit(
            train_dataset,
            validation_data=val_dataset,
            epochs=epochs,
            callbacks=callbacks,
            verbose=1
        )

    # Salva il modello finale
    try:
        save_path = f'{execute_name}_{save_name}.keras'
        model.save(save_path, save_format='keras')
        
        os.makedirs(f'{execute_name}/weights', exist_ok=True)
        model.save_weights(f'{execute_name}/weights')
        print(f"\nModello salvato in: {save_path}")
    except Exception as e:
        print(f"Warning: Could not save model: {e}")

    return model, history
In [ ]:
model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)
Shape rilevate:
- Temporal shape: (41, 3)
- Static shape: (113,)
- Numero di output: 5
2024-12-07 08:38:26.936272: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
Model: "OilTransformer"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 temporal (InputLayer)       [(None, 41, 3)]              0         []                            
                                                                                                  
 layer_normalization (Layer  (None, 41, 3)                6         ['temporal[0][0]']            
 Normalization)                                                                                   
                                                                                                  
 data_augmentation (DataAug  (None, 41, 3)                0         ['layer_normalization[0][0]'] 
 mentation)                                                                                       
                                                                                                  
 dense (Dense)               (None, 41, 64)               256       ['data_augmentation[0][0]']   
                                                                                                  
 dropout (Dropout)           (None, 41, 64)               0         ['dense[0][0]']               
                                                                                                  
 dense_1 (Dense)             (None, 41, 128)              8320      ['dropout[0][0]']             
                                                                                                  
 positional_encoding (Posit  (None, 41, 128)              5248      ['dense_1[0][0]']             
 ionalEncoding)                                                                                   
                                                                                                  
 multi_head_attention (Mult  (None, 41, 128)              66048     ['positional_encoding[0][0]', 
 iHeadAttention)                                                     'positional_encoding[0][0]'] 
                                                                                                  
 dense_2 (Dense)             (None, 41, 128)              16512     ['positional_encoding[0][0]'] 
                                                                                                  
 dropout_1 (Dropout)         (None, 41, 128)              0         ['multi_head_attention[0][0]']
                                                                                                  
 tf.math.multiply (TFOpLamb  (None, 41, 128)              0         ['dense_2[0][0]',             
 da)                                                                 'dropout_1[0][0]']           
                                                                                                  
 stochastic_depth (Stochast  (None, 41, 128)              0         ['positional_encoding[0][0]', 
 icDepth)                                                            'tf.math.multiply[0][0]']    
                                                                                                  
 layer_normalization_1 (Lay  (None, 41, 128)              256       ['stochastic_depth[0][0]']    
 erNormalization)                                                                                 
                                                                                                  
 dense_3 (Dense)             (None, 41, 256)              33024     ['layer_normalization_1[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_2 (Dropout)         (None, 41, 256)              0         ['dense_3[0][0]']             
                                                                                                  
 dense_4 (Dense)             (None, 41, 128)              32896     ['dropout_2[0][0]']           
                                                                                                  
 dropout_3 (Dropout)         (None, 41, 128)              0         ['dense_4[0][0]']             
                                                                                                  
 stochastic_depth_1 (Stocha  (None, 41, 128)              0         ['layer_normalization_1[0][0]'
 sticDepth)                                                         , 'dropout_3[0][0]']          
                                                                                                  
 layer_normalization_2 (Lay  (None, 41, 128)              256       ['stochastic_depth_1[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 multi_head_attention_1 (Mu  (None, 41, 128)              66048     ['layer_normalization_2[0][0]'
 ltiHeadAttention)                                                  , 'layer_normalization_2[0][0]
                                                                    ']                            
                                                                                                  
 dense_5 (Dense)             (None, 41, 128)              16512     ['layer_normalization_2[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_4 (Dropout)         (None, 41, 128)              0         ['multi_head_attention_1[0][0]
                                                                    ']                            
                                                                                                  
 tf.math.multiply_1 (TFOpLa  (None, 41, 128)              0         ['dense_5[0][0]',             
 mbda)                                                               'dropout_4[0][0]']           
                                                                                                  
 stochastic_depth_2 (Stocha  (None, 41, 128)              0         ['layer_normalization_2[0][0]'
 sticDepth)                                                         , 'tf.math.multiply_1[0][0]'] 
                                                                                                  
 layer_normalization_3 (Lay  (None, 41, 128)              256       ['stochastic_depth_2[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 dense_6 (Dense)             (None, 41, 256)              33024     ['layer_normalization_3[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_5 (Dropout)         (None, 41, 256)              0         ['dense_6[0][0]']             
                                                                                                  
 dense_7 (Dense)             (None, 41, 128)              32896     ['dropout_5[0][0]']           
                                                                                                  
 dropout_6 (Dropout)         (None, 41, 128)              0         ['dense_7[0][0]']             
                                                                                                  
 stochastic_depth_3 (Stocha  (None, 41, 128)              0         ['layer_normalization_3[0][0]'
 sticDepth)                                                         , 'dropout_6[0][0]']          
                                                                                                  
 layer_normalization_4 (Lay  (None, 41, 128)              256       ['stochastic_depth_3[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 multi_head_attention_2 (Mu  (None, 41, 128)              66048     ['layer_normalization_4[0][0]'
 ltiHeadAttention)                                                  , 'layer_normalization_4[0][0]
                                                                    ']                            
                                                                                                  
 dense_8 (Dense)             (None, 41, 128)              16512     ['layer_normalization_4[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_7 (Dropout)         (None, 41, 128)              0         ['multi_head_attention_2[0][0]
                                                                    ']                            
                                                                                                  
 tf.math.multiply_2 (TFOpLa  (None, 41, 128)              0         ['dense_8[0][0]',             
 mbda)                                                               'dropout_7[0][0]']           
                                                                                                  
 stochastic_depth_4 (Stocha  (None, 41, 128)              0         ['layer_normalization_4[0][0]'
 sticDepth)                                                         , 'tf.math.multiply_2[0][0]'] 
                                                                                                  
 layer_normalization_5 (Lay  (None, 41, 128)              256       ['stochastic_depth_4[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 dense_9 (Dense)             (None, 41, 256)              33024     ['layer_normalization_5[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_8 (Dropout)         (None, 41, 256)              0         ['dense_9[0][0]']             
                                                                                                  
 dense_10 (Dense)            (None, 41, 128)              32896     ['dropout_8[0][0]']           
                                                                                                  
 dropout_9 (Dropout)         (None, 41, 128)              0         ['dense_10[0][0]']            
                                                                                                  
 stochastic_depth_5 (Stocha  (None, 41, 128)              0         ['layer_normalization_5[0][0]'
 sticDepth)                                                         , 'dropout_9[0][0]']          
                                                                                                  
 layer_normalization_6 (Lay  (None, 41, 128)              256       ['stochastic_depth_5[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 multi_head_attention_3 (Mu  (None, 41, 128)              66048     ['layer_normalization_6[0][0]'
 ltiHeadAttention)                                                  , 'layer_normalization_6[0][0]
                                                                    ']                            
                                                                                                  
 dense_11 (Dense)            (None, 41, 128)              16512     ['layer_normalization_6[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_10 (Dropout)        (None, 41, 128)              0         ['multi_head_attention_3[0][0]
                                                                    ']                            
                                                                                                  
 tf.math.multiply_3 (TFOpLa  (None, 41, 128)              0         ['dense_11[0][0]',            
 mbda)                                                               'dropout_10[0][0]']          
                                                                                                  
 stochastic_depth_6 (Stocha  (None, 41, 128)              0         ['layer_normalization_6[0][0]'
 sticDepth)                                                         , 'tf.math.multiply_3[0][0]'] 
                                                                                                  
 layer_normalization_7 (Lay  (None, 41, 128)              256       ['stochastic_depth_6[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 dense_12 (Dense)            (None, 41, 256)              33024     ['layer_normalization_7[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_11 (Dropout)        (None, 41, 256)              0         ['dense_12[0][0]']            
                                                                                                  
 dense_13 (Dense)            (None, 41, 128)              32896     ['dropout_11[0][0]']          
                                                                                                  
 static (InputLayer)         [(None, 113)]                0         []                            
                                                                                                  
 dropout_12 (Dropout)        (None, 41, 128)              0         ['dense_13[0][0]']            
                                                                                                  
 layer_normalization_9 (Lay  (None, 113)                  226       ['static[0][0]']              
 erNormalization)                                                                                 
                                                                                                  
 stochastic_depth_7 (Stocha  (None, 41, 128)              0         ['layer_normalization_7[0][0]'
 sticDepth)                                                         , 'dropout_12[0][0]']         
                                                                                                  
 dense_14 (Dense)            (None, 256)                  29184     ['layer_normalization_9[0][0]'
                                                                    ]                             
                                                                                                  
 layer_normalization_8 (Lay  (None, 41, 128)              256       ['stochastic_depth_7[0][0]']  
 erNormalization)                                                                                 
                                                                                                  
 dropout_13 (Dropout)        (None, 256)                  0         ['dense_14[0][0]']            
                                                                                                  
 stochastic_depth_8 (Stocha  (None, 41, 128)              0         ['layer_normalization_8[0][0]'
 sticDepth)                                                         , 'positional_encoding[0][0]']
                                                                                                  
 dense_15 (Dense)            (None, 128)                  32896     ['dropout_13[0][0]']          
                                                                                                  
 multi_head_attention_4 (Mu  (None, 41, 128)              131968    ['stochastic_depth_8[0][0]',  
 ltiHeadAttention)                                                   'stochastic_depth_8[0][0]']  
                                                                                                  
 dropout_14 (Dropout)        (None, 128)                  0         ['dense_15[0][0]']            
                                                                                                  
 global_average_pooling1d (  (None, 128)                  0         ['multi_head_attention_4[0][0]
 GlobalAveragePooling1D)                                            ']                            
                                                                                                  
 global_average_pooling1d_1  (None, 128)                  0         ['stochastic_depth_8[0][0]']  
  (GlobalAveragePooling1D)                                                                        
                                                                                                  
 global_max_pooling1d (Glob  (None, 128)                  0         ['stochastic_depth_8[0][0]']  
 alMaxPooling1D)                                                                                  
                                                                                                  
 dense_16 (Dense)            (None, 64)                   8256      ['dropout_14[0][0]']          
                                                                                                  
 concatenate (Concatenate)   (None, 384)                  0         ['global_average_pooling1d[0][
                                                                    0]',                          
                                                                     'global_average_pooling1d_1[0
                                                                    ][0]',                        
                                                                     'global_max_pooling1d[0][0]']
                                                                                                  
 dropout_15 (Dropout)        (None, 64)                   0         ['dense_16[0][0]']            
                                                                                                  
 concatenate_1 (Concatenate  (None, 448)                  0         ['concatenate[0][0]',         
 )                                                                   'dropout_15[0][0]']          
                                                                                                  
 batch_normalization (Batch  (None, 448)                  1792      ['concatenate_1[0][0]']       
 Normalization)                                                                                   
                                                                                                  
 dense_17 (Dense)            (None, 256)                  114944    ['batch_normalization[0][0]'] 
                                                                                                  
 dropout_16 (Dropout)        (None, 256)                  0         ['dense_17[0][0]']            
                                                                                                  
 batch_normalization_1 (Bat  (None, 256)                  1024      ['dropout_16[0][0]']          
 chNormalization)                                                                                 
                                                                                                  
 dense_18 (Dense)            (None, 128)                  32896     ['batch_normalization_1[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_17 (Dropout)        (None, 128)                  0         ['dense_18[0][0]']            
                                                                                                  
 batch_normalization_2 (Bat  (None, 128)                  512       ['dropout_17[0][0]']          
 chNormalization)                                                                                 
                                                                                                  
 dense_19 (Dense)            (None, 64)                   8256      ['batch_normalization_2[0][0]'
                                                                    ]                             
                                                                                                  
 dropout_18 (Dropout)        (None, 64)                   0         ['dense_19[0][0]']            
                                                                                                  
 dense_20 (Dense)            (None, 5)                    325       ['dropout_18[0][0]']          
                                                                                                  
==================================================================================================
Total params: 972077 (3.71 MB)
Trainable params: 965165 (3.68 MB)
Non-trainable params: 6912 (27.00 KB)
__________________________________________________________________________________________________
Epoch 1/150
2024-12-07 08:38:44.061185: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ade632071d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-12-07 08:38:44.061220: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9
2024-12-07 08:38:44.066715: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-07 08:38:44.130163: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905
2024-12-07 08:38:44.261917: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
   5/4977 [..............................] - ETA: 3:14 - loss: 0.7255 - mae: 1.1227   WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0329s vs `on_train_batch_end` time: 0.0391s). Check your callbacks.
4977/4977 [==============================] - 329s 62ms/step - loss: 0.0547 - mae: 0.2020 - val_loss: 0.0149 - val_mae: 0.0886 - val_olive_prod_mae: 0.0985 - val_min_oil_prod_mae: 0.0962 - val_max_oil_prod_mae: 0.0947 - val_avg_oil_prod_mae: 0.0915 - val_total_water_need_mae: 0.0625 - lr: 1.0111e-04
Epoch 2/150
4977/4977 [==============================] - 297s 59ms/step - loss: 0.0254 - mae: 0.1444 - val_loss: 0.0135 - val_mae: 0.0853 - val_olive_prod_mae: 0.0954 - val_min_oil_prod_mae: 0.0936 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0893 - val_total_water_need_mae: 0.0550 - lr: 1.0219e-05
Epoch 3/150
4977/4977 [==============================] - 295s 59ms/step - loss: 0.0245 - mae: 0.1423 - val_loss: 0.0133 - val_mae: 0.0847 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0545 - lr: 1.0328e-06
Epoch 4/150
4977/4977 [==============================] - 302s 61ms/step - loss: 0.0244 - mae: 0.1421 - val_loss: 0.0133 - val_mae: 0.0849 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0554 - lr: 1.0438e-07
Epoch 5/150
2024-12-07 08:59:07.216568: W tensorflow/tsl/framework/bfc_allocator.cc:485] Allocator (GPU_0_bfc) ran out of memory trying to allocate 26.27MiB (rounded to 27541504)requested by op OilTransformer/multi_head_attention_3/einsum/Einsum
If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. 
Current allocation summary follows.
Current allocation summary follows.
2024-12-07 08:59:07.216654: I tensorflow/tsl/framework/bfc_allocator.cc:1039] BFCAllocator dump for GPU_0_bfc
2024-12-07 08:59:07.216677: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (256): 	Total Chunks: 197, Chunks in use: 196. 49.2KiB allocated for chunks. 49.0KiB in use in bin. 3.5KiB client-requested in use in bin.
2024-12-07 08:59:07.216687: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (512): 	Total Chunks: 171, Chunks in use: 166. 90.5KiB allocated for chunks. 87.5KiB in use in bin. 83.4KiB client-requested in use in bin.
2024-12-07 08:59:07.216695: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1024): 	Total Chunks: 63, Chunks in use: 59. 75.2KiB allocated for chunks. 70.0KiB in use in bin. 66.5KiB client-requested in use in bin.
2024-12-07 08:59:07.216702: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2048): 	Total Chunks: 6, Chunks in use: 3. 12.5KiB allocated for chunks. 6.0KiB in use in bin. 5.8KiB client-requested in use in bin.
2024-12-07 08:59:07.216711: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4096): 	Total Chunks: 1, Chunks in use: 0. 4.0KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-12-07 08:59:07.216720: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8192): 	Total Chunks: 1, Chunks in use: 1. 10.0KiB allocated for chunks. 10.0KiB in use in bin. 10.0KiB client-requested in use in bin.
2024-12-07 08:59:07.216727: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16384): 	Total Chunks: 1, Chunks in use: 1. 20.5KiB allocated for chunks. 20.5KiB in use in bin. 20.5KiB client-requested in use in bin.
2024-12-07 08:59:07.216735: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (32768): 	Total Chunks: 16, Chunks in use: 16. 579.5KiB allocated for chunks. 579.5KiB in use in bin. 512.0KiB client-requested in use in bin.
2024-12-07 08:59:07.216742: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (65536): 	Total Chunks: 98, Chunks in use: 98. 6.74MiB allocated for chunks. 6.74MiB in use in bin. 6.54MiB client-requested in use in bin.
2024-12-07 08:59:07.216749: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (131072): 	Total Chunks: 66, Chunks in use: 66. 9.26MiB allocated for chunks. 9.26MiB in use in bin. 8.52MiB client-requested in use in bin.
2024-12-07 08:59:07.216756: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (262144): 	Total Chunks: 7, Chunks in use: 7. 2.51MiB allocated for chunks. 2.51MiB in use in bin. 2.47MiB client-requested in use in bin.
2024-12-07 08:59:07.216763: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (524288): 	Total Chunks: 3, Chunks in use: 3. 1.76MiB allocated for chunks. 1.76MiB in use in bin. 1.44MiB client-requested in use in bin.
2024-12-07 08:59:07.216770: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1048576): 	Total Chunks: 1, Chunks in use: 1. 1.28MiB allocated for chunks. 1.28MiB in use in bin. 1.28MiB client-requested in use in bin.
2024-12-07 08:59:07.216777: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2097152): 	Total Chunks: 6, Chunks in use: 5. 14.89MiB allocated for chunks. 12.81MiB in use in bin. 12.81MiB client-requested in use in bin.
2024-12-07 08:59:07.216785: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4194304): 	Total Chunks: 6, Chunks in use: 6. 33.15MiB allocated for chunks. 33.15MiB in use in bin. 28.19MiB client-requested in use in bin.
2024-12-07 08:59:07.216792: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8388608): 	Total Chunks: 42, Chunks in use: 42. 437.22MiB allocated for chunks. 437.22MiB in use in bin. 430.50MiB client-requested in use in bin.
2024-12-07 08:59:07.216801: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16777216): 	Total Chunks: 12, Chunks in use: 12. 263.74MiB allocated for chunks. 263.74MiB in use in bin. 252.20MiB client-requested in use in bin.
2024-12-07 08:59:07.216810: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (33554432): 	Total Chunks: 1, Chunks in use: 1. 34.48MiB allocated for chunks. 34.48MiB in use in bin. 26.27MiB client-requested in use in bin.
2024-12-07 08:59:07.216819: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (67108864): 	Total Chunks: 1, Chunks in use: 1. 97.20MiB allocated for chunks. 97.20MiB in use in bin. 97.20MiB client-requested in use in bin.
2024-12-07 08:59:07.216826: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-12-07 08:59:07.216833: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (268435456): 	Total Chunks: 12, Chunks in use: 12. 8.62GiB allocated for chunks. 8.62GiB in use in bin. 8.62GiB client-requested in use in bin.
2024-12-07 08:59:07.216839: I tensorflow/tsl/framework/bfc_allocator.cc:1062] Bin for 26.27MiB was 16.00MiB, Chunk State: 
2024-12-07 08:59:07.216845: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 1608187904
2024-12-07 08:59:07.216855: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdd6000000 of size 385728000 next 704
2024-12-07 08:59:07.216862: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdecfdbe00 of size 354368000 next 653
2024-12-07 08:59:07.216867: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe021cf800 of size 385728000 next 455
2024-12-07 08:59:07.216873: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe191ab600 of size 10747904 next 89
2024-12-07 08:59:07.216880: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe19beb600 of size 10747904 next 563
2024-12-07 08:59:07.216886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1a62b600 of size 16793600 next 544
2024-12-07 08:59:07.216892: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1b62f600 of size 27541504 next 40
2024-12-07 08:59:07.216897: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d073600 of size 83968 next 667
2024-12-07 08:59:07.216903: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d087e00 of size 10747904 next 615
2024-12-07 08:59:07.216909: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dac7e00 of size 83968 next 759
2024-12-07 08:59:07.216915: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dadc600 of size 5373952 next 103
2024-12-07 08:59:07.216921: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dffc600 of size 2686976 next 713
2024-12-07 08:59:07.216927: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e28c600 of size 83968 next 525
2024-12-07 08:59:07.216932: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2a0e00 of size 83968 next 705
2024-12-07 08:59:07.216938: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2b5600 of size 83968 next 471
2024-12-07 08:59:07.216944: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2c9e00 of size 83968 next 645
2024-12-07 08:59:07.216949: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2de600 of size 83968 next 458
2024-12-07 08:59:07.216955: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2f2e00 of size 83968 next 621
2024-12-07 08:59:07.216960: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7abe1e307600 of size 2183168 next 703
2024-12-07 08:59:07.216966: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e51c600 of size 10747904 next 715
2024-12-07 08:59:07.216972: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1ef5c600 of size 21495808 next 675
2024-12-07 08:59:07.216978: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe203dc600 of size 21495808 next 750
2024-12-07 08:59:07.216983: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2185c600 of size 10747904 next 588
2024-12-07 08:59:07.216989: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2229c600 of size 10747904 next 497
2024-12-07 08:59:07.216996: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe22cdc600 of size 10747904 next 722
2024-12-07 08:59:07.217001: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2371c600 of size 10747904 next 479
2024-12-07 08:59:07.217007: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2415c600 of size 10747904 next 121
2024-12-07 08:59:07.217012: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe24b9c600 of size 10747904 next 419
2024-12-07 08:59:07.217018: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe255dc600 of size 10747904 next 507
2024-12-07 08:59:07.217023: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2601c600 of size 2686976 next 721
2024-12-07 08:59:07.217029: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe262ac600 of size 8060928 next 599
2024-12-07 08:59:07.217034: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe26a5c600 of size 10747904 next 541
2024-12-07 08:59:07.217039: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2749c600 of size 16793600 next 669
2024-12-07 08:59:07.217045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe284a0600 of size 27541504 next 719
2024-12-07 08:59:07.217051: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe29ee4600 of size 10747904 next 517
2024-12-07 08:59:07.217056: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2a924600 of size 10747904 next 681
2024-12-07 08:59:07.217062: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2b364600 of size 10747904 next 432
2024-12-07 08:59:07.217067: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2bda4600 of size 10747904 next 95
2024-12-07 08:59:07.217072: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2c7e4600 of size 21495808 next 565
2024-12-07 08:59:07.217078: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2dc64600 of size 21495808 next 724
2024-12-07 08:59:07.217084: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2f0e4600 of size 10747904 next 66
2024-12-07 08:59:07.217089: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fb24600 of size 2686976 next 747
2024-12-07 08:59:07.217094: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fdb4600 of size 10747904 next 533
2024-12-07 08:59:07.217100: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe307f4600 of size 10747904 next 571
2024-12-07 08:59:07.217106: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31234600 of size 10747904 next 488
2024-12-07 08:59:07.217111: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31c74600 of size 10747904 next 710
2024-12-07 08:59:07.217116: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe326b4600 of size 10747904 next 552
2024-12-07 08:59:07.217122: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe330f4600 of size 10747904 next 46
2024-12-07 08:59:07.217127: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe33b34600 of size 36157952 next 18446744073709551615
2024-12-07 08:59:07.217133: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296
2024-12-07 08:59:07.217139: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad3f2000000 of size 2507232000 next 4
2024-12-07 08:59:07.217145: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad487715300 of size 101920000 next 5
2024-12-07 08:59:07.217151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848000 of size 256 next 6
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2024-12-07 08:59:07.218065: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc400 of size 1280 next 676
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2024-12-07 08:59:07.218967: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a7dd00 of size 65536 next 629
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2024-12-07 08:59:07.219114: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7ad4e7bc3800 of size 512 next 580
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2024-12-07 08:59:07.221311: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ad2800 of size 65536 next 495
2024-12-07 08:59:07.221319: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ae2800 of size 65536 next 490
2024-12-07 08:59:07.221328: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714af2800 of size 65536 next 670
2024-12-07 08:59:07.221336: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b02800 of size 131072 next 509
2024-12-07 08:59:07.221343: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b22800 of size 65536 next 54
2024-12-07 08:59:07.221350: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b32800 of size 131072 next 440
2024-12-07 08:59:07.221356: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b52800 of size 131072 next 618
2024-12-07 08:59:07.221374: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b72800 of size 65536 next 547
2024-12-07 08:59:07.221381: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b82800 of size 99584 next 738
2024-12-07 08:59:07.221388: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b9ad00 of size 65536 next 105
2024-12-07 08:59:07.221396: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714baad00 of size 131072 next 561
2024-12-07 08:59:07.221403: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bcad00 of size 65536 next 511
2024-12-07 08:59:07.221410: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bdad00 of size 65536 next 516
2024-12-07 08:59:07.221417: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bead00 of size 65536 next 485
2024-12-07 08:59:07.221423: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bfad00 of size 131072 next 551
2024-12-07 08:59:07.221431: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c1ad00 of size 65536 next 104
2024-12-07 08:59:07.221438: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c2ad00 of size 131072 next 720
2024-12-07 08:59:07.221444: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c4ad00 of size 65536 next 739
2024-12-07 08:59:07.221451: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c5ad00 of size 65536 next 708
2024-12-07 08:59:07.221457: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c6ad00 of size 65536 next 87
2024-12-07 08:59:07.221464: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c7ad00 of size 131072 next 694
2024-12-07 08:59:07.221471: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c9ad00 of size 246784 next 425
2024-12-07 08:59:07.221479: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714cd7100 of size 65536 next 765
2024-12-07 08:59:07.221487: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ce7100 of size 131072 next 655
2024-12-07 08:59:07.221495: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07100 of size 1792 next 422
2024-12-07 08:59:07.221502: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07800 of size 1024 next 530
2024-12-07 08:59:07.221511: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07c00 of size 65536 next 454
2024-12-07 08:59:07.221519: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d17c00 of size 165888 next 62
2024-12-07 08:59:07.221528: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d40400 of size 131072 next 448
2024-12-07 08:59:07.221537: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d60400 of size 131072 next 120
2024-12-07 08:59:07.221545: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80400 of size 1024 next 671
2024-12-07 08:59:07.221551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80800 of size 256 next 687
2024-12-07 08:59:07.221559: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80900 of size 256 next 449
2024-12-07 08:59:07.221566: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80a00 of size 2048 next 513
2024-12-07 08:59:07.221573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d81200 of size 231424 next 434
2024-12-07 08:59:07.221582: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714db9a00 of size 83968 next 733
2024-12-07 08:59:07.221589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dce200 of size 2048 next 591
2024-12-07 08:59:07.221596: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dcea00 of size 272640 next 59
2024-12-07 08:59:07.221604: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e11300 of size 65536 next 585
2024-12-07 08:59:07.221611: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e21300 of size 131072 next 636
2024-12-07 08:59:07.221618: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e41300 of size 131072 next 638
2024-12-07 08:59:07.221625: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e61300 of size 458752 next 512
2024-12-07 08:59:07.221632: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed1300 of size 10240 next 85
2024-12-07 08:59:07.221639: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed3b00 of size 83968 next 61
2024-12-07 08:59:07.221645: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ee8300 of size 131072 next 483
2024-12-07 08:59:07.221656: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f08300 of size 131072 next 572
2024-12-07 08:59:07.221664: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f28300 of size 32768 next 622
2024-12-07 08:59:07.221673: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f30300 of size 124928 next 101
2024-12-07 08:59:07.221680: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f4eb00 of size 231424 next 520
2024-12-07 08:59:07.221687: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f87300 of size 251904 next 668
2024-12-07 08:59:07.221694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714fc4b00 of size 524288 next 128
2024-12-07 08:59:07.221701: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad715044b00 of size 604672 next 71
2024-12-07 08:59:07.221708: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150d8500 of size 131072 next 573
2024-12-07 08:59:07.221715: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150f8500 of size 141056 next 444
2024-12-07 08:59:07.221722: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71511ac00 of size 27541504 next 608
2024-12-07 08:59:07.221728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad716b5ec00 of size 21495808 next 640
2024-12-07 08:59:07.221736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad717fdec00 of size 21495808 next 84
2024-12-07 08:59:07.221742: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71945ec00 of size 10747904 next 133
2024-12-07 08:59:07.221750: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad719e9ec00 of size 10747904 next 744
2024-12-07 08:59:07.221757: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71a8dec00 of size 10747904 next 77
2024-12-07 08:59:07.221763: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71b31ec00 of size 10747904 next 510
2024-12-07 08:59:07.221769: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71bd5ec00 of size 10747904 next 150
2024-12-07 08:59:07.221776: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71c79ec00 of size 10747904 next 673
2024-12-07 08:59:07.221782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71d1dec00 of size 14816256 next 18446744073709551615
2024-12-07 08:59:07.221789: I tensorflow/tsl/framework/bfc_allocator.cc:1100]      Summary of in-use Chunks by size: 
2024-12-07 08:59:07.221801: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 196 Chunks of size 256 totalling 49.0KiB
2024-12-07 08:59:07.221809: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 148 Chunks of size 512 totalling 74.0KiB
2024-12-07 08:59:07.221817: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 18 Chunks of size 768 totalling 13.5KiB
2024-12-07 08:59:07.221825: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 37 Chunks of size 1024 totalling 37.0KiB
2024-12-07 08:59:07.221833: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1280 totalling 12.5KiB
2024-12-07 08:59:07.221841: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 1536 totalling 3.0KiB
2024-12-07 08:59:07.221849: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1792 totalling 17.5KiB
2024-12-07 08:59:07.221857: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 2048 totalling 6.0KiB
2024-12-07 08:59:07.221866: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 10240 totalling 10.0KiB
2024-12-07 08:59:07.221875: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 20992 totalling 20.5KiB
2024-12-07 08:59:07.221882: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 13 Chunks of size 32768 totalling 416.0KiB
2024-12-07 08:59:07.221890: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 43520 totalling 42.5KiB
2024-12-07 08:59:07.221898: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 59648 totalling 58.2KiB
2024-12-07 08:59:07.221907: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 64256 totalling 62.8KiB
2024-12-07 08:59:07.221916: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 77 Chunks of size 65536 totalling 4.81MiB
2024-12-07 08:59:07.221923: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 12 Chunks of size 83968 totalling 984.0KiB
2024-12-07 08:59:07.221930: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 97024 totalling 94.8KiB
2024-12-07 08:59:07.221938: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 99584 totalling 97.2KiB
2024-12-07 08:59:07.221945: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 101376 totalling 99.0KiB
2024-12-07 08:59:07.221954: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 115712 totalling 452.0KiB
2024-12-07 08:59:07.221962: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 124928 totalling 122.0KiB
2024-12-07 08:59:07.221971: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 130560 totalling 127.5KiB
2024-12-07 08:59:07.221978: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 53 Chunks of size 131072 totalling 6.62MiB
2024-12-07 08:59:07.221986: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 141056 totalling 137.8KiB
2024-12-07 08:59:07.221993: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 164608 totalling 160.8KiB
2024-12-07 08:59:07.222001: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 165888 totalling 162.0KiB
2024-12-07 08:59:07.222008: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 181248 totalling 177.0KiB
2024-12-07 08:59:07.222015: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 186368 totalling 182.0KiB
2024-12-07 08:59:07.222023: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 198656 totalling 194.0KiB
2024-12-07 08:59:07.222030: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 231424 totalling 452.0KiB
2024-12-07 08:59:07.222038: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 246784 totalling 482.0KiB
2024-12-07 08:59:07.222045: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 251904 totalling 492.0KiB
2024-12-07 08:59:07.222052: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 261120 totalling 255.0KiB
2024-12-07 08:59:07.222060: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 262144 totalling 512.0KiB
2024-12-07 08:59:07.222071: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 272640 totalling 266.2KiB
2024-12-07 08:59:07.222078: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 458752 totalling 1.75MiB
2024-12-07 08:59:07.222086: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 524288 totalling 512.0KiB
2024-12-07 08:59:07.222094: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 604672 totalling 590.5KiB
2024-12-07 08:59:07.222102: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 720384 totalling 703.5KiB
2024-12-07 08:59:07.222110: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 1343488 totalling 1.28MiB
2024-12-07 08:59:07.222118: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 5 Chunks of size 2686976 totalling 12.81MiB
2024-12-07 08:59:07.222125: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 5206016 totalling 4.96MiB
2024-12-07 08:59:07.222132: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 5373952 totalling 20.50MiB
2024-12-07 08:59:07.222140: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 8060928 totalling 7.69MiB
2024-12-07 08:59:07.222148: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 39 Chunks of size 10747904 totalling 399.75MiB
2024-12-07 08:59:07.222155: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12059136 totalling 11.50MiB
2024-12-07 08:59:07.222162: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12410880 totalling 11.84MiB
2024-12-07 08:59:07.222170: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 14816256 totalling 14.13MiB
2024-12-07 08:59:07.222177: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 16793600 totalling 32.03MiB
2024-12-07 08:59:07.222185: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 6 Chunks of size 21495808 totalling 123.00MiB
2024-12-07 08:59:07.222192: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 27541504 totalling 78.80MiB
2024-12-07 08:59:07.222199: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 31360000 totalling 29.91MiB
2024-12-07 08:59:07.222206: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 36157952 totalling 34.48MiB
2024-12-07 08:59:07.222212: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 101920000 totalling 97.20MiB
2024-12-07 08:59:07.222220: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 354368000 totalling 1.32GiB
2024-12-07 08:59:07.222228: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 385728000 totalling 1.44GiB
2024-12-07 08:59:07.222236: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 708736000 totalling 675.90MiB
2024-12-07 08:59:07.222243: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 771456000 totalling 735.72MiB
2024-12-07 08:59:07.222250: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 2303392000 totalling 2.14GiB
2024-12-07 08:59:07.222259: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 2507232000 totalling 2.33GiB
2024-12-07 08:59:07.222267: I tensorflow/tsl/framework/bfc_allocator.cc:1107] Sum Total of in-use chunks: 9.50GiB
2024-12-07 08:59:07.222275: I tensorflow/tsl/framework/bfc_allocator.cc:1109] Total bytes in pool: 10198122496 memory_limit_: 10198122496 available bytes: 0 curr_region_allocation_bytes_: 17179869184
2024-12-07 08:59:07.222287: I tensorflow/tsl/framework/bfc_allocator.cc:1114] Stats: 
Limit:                     10198122496
InUse:                     10195919872
MaxInUse:                  10195920896
NumAllocs:                    43119401
MaxAllocSize:               2507232000
Reserved:                            0
PeakReserved:                        0
LargestFreeBlock:                    0

2024-12-07 08:59:07.222309: W tensorflow/tsl/framework/bfc_allocator.cc:497] ****************************************************************************************************
2024-12-07 08:59:07.222350: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at einsum_op_impl.h:604 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[512,8,41,41] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
2024-12-07 08:59:07.222417: W tensorflow/tsl/framework/bfc_allocator.cc:485] Allocator (GPU_0_bfc) ran out of memory trying to allocate 10.25MiB (rounded to 10747904)requested by op OilTransformer/dense_11/Tensordot/MatMul
If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. 
Current allocation summary follows.
Current allocation summary follows.
2024-12-07 08:59:07.222532: I tensorflow/tsl/framework/bfc_allocator.cc:1039] BFCAllocator dump for GPU_0_bfc
2024-12-07 08:59:07.222560: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (256): 	Total Chunks: 197, Chunks in use: 196. 49.2KiB allocated for chunks. 49.0KiB in use in bin. 3.5KiB client-requested in use in bin.
2024-12-07 08:59:07.222573: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (512): 	Total Chunks: 171, Chunks in use: 166. 90.5KiB allocated for chunks. 87.5KiB in use in bin. 83.4KiB client-requested in use in bin.
2024-12-07 08:59:07.222584: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1024): 	Total Chunks: 63, Chunks in use: 59. 75.2KiB allocated for chunks. 70.0KiB in use in bin. 66.5KiB client-requested in use in bin.
2024-12-07 08:59:07.222596: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2048): 	Total Chunks: 6, Chunks in use: 3. 12.5KiB allocated for chunks. 6.0KiB in use in bin. 5.8KiB client-requested in use in bin.
2024-12-07 08:59:07.222607: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4096): 	Total Chunks: 1, Chunks in use: 0. 4.0KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-12-07 08:59:07.222620: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8192): 	Total Chunks: 1, Chunks in use: 1. 10.0KiB allocated for chunks. 10.0KiB in use in bin. 10.0KiB client-requested in use in bin.
2024-12-07 08:59:07.222630: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16384): 	Total Chunks: 1, Chunks in use: 1. 20.5KiB allocated for chunks. 20.5KiB in use in bin. 20.5KiB client-requested in use in bin.
2024-12-07 08:59:07.222641: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (32768): 	Total Chunks: 16, Chunks in use: 16. 579.5KiB allocated for chunks. 579.5KiB in use in bin. 512.0KiB client-requested in use in bin.
2024-12-07 08:59:07.222651: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (65536): 	Total Chunks: 98, Chunks in use: 98. 6.74MiB allocated for chunks. 6.74MiB in use in bin. 6.54MiB client-requested in use in bin.
2024-12-07 08:59:07.222661: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (131072): 	Total Chunks: 66, Chunks in use: 66. 9.26MiB allocated for chunks. 9.26MiB in use in bin. 8.52MiB client-requested in use in bin.
2024-12-07 08:59:07.222671: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (262144): 	Total Chunks: 7, Chunks in use: 7. 2.51MiB allocated for chunks. 2.51MiB in use in bin. 2.47MiB client-requested in use in bin.
2024-12-07 08:59:07.222681: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (524288): 	Total Chunks: 3, Chunks in use: 3. 1.76MiB allocated for chunks. 1.76MiB in use in bin. 1.44MiB client-requested in use in bin.
2024-12-07 08:59:07.222691: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1048576): 	Total Chunks: 1, Chunks in use: 1. 1.28MiB allocated for chunks. 1.28MiB in use in bin. 1.28MiB client-requested in use in bin.
2024-12-07 08:59:07.222701: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2097152): 	Total Chunks: 6, Chunks in use: 5. 14.89MiB allocated for chunks. 12.81MiB in use in bin. 12.81MiB client-requested in use in bin.
2024-12-07 08:59:07.222711: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4194304): 	Total Chunks: 6, Chunks in use: 6. 33.15MiB allocated for chunks. 33.15MiB in use in bin. 28.19MiB client-requested in use in bin.
2024-12-07 08:59:07.222721: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8388608): 	Total Chunks: 42, Chunks in use: 42. 437.22MiB allocated for chunks. 437.22MiB in use in bin. 430.50MiB client-requested in use in bin.
2024-12-07 08:59:07.222731: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16777216): 	Total Chunks: 12, Chunks in use: 12. 263.74MiB allocated for chunks. 263.74MiB in use in bin. 252.20MiB client-requested in use in bin.
2024-12-07 08:59:07.222741: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (33554432): 	Total Chunks: 1, Chunks in use: 1. 34.48MiB allocated for chunks. 34.48MiB in use in bin. 26.27MiB client-requested in use in bin.
2024-12-07 08:59:07.222751: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (67108864): 	Total Chunks: 1, Chunks in use: 1. 97.20MiB allocated for chunks. 97.20MiB in use in bin. 97.20MiB client-requested in use in bin.
2024-12-07 08:59:07.222761: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2024-12-07 08:59:07.222770: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (268435456): 	Total Chunks: 12, Chunks in use: 12. 8.62GiB allocated for chunks. 8.62GiB in use in bin. 8.62GiB client-requested in use in bin.
2024-12-07 08:59:07.222783: I tensorflow/tsl/framework/bfc_allocator.cc:1062] Bin for 10.25MiB was 8.00MiB, Chunk State: 
2024-12-07 08:59:07.222796: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 1608187904
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2024-12-07 08:59:07.222972: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7abe1e307600 of size 2183168 next 703
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2024-12-07 08:59:07.223236: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296
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2024-12-07 08:59:07.224950: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7ad4e79e1f00 of size 1792 next 92
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2024-12-07 08:59:07.225026: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2d00 of size 768 next 568
2024-12-07 08:59:07.225034: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7ad4e79e3000 of size 768 next 639
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2024-12-07 08:59:07.225051: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3500 of size 1024 next 606
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2024-12-07 08:59:07.225078: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3b00 of size 1280 next 420
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2024-12-07 08:59:07.225099: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4100 of size 2048 next 465
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2024-12-07 08:59:07.225142: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4d00 of size 512 next 39
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2024-12-07 08:59:07.225168: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7ad4e79e5200 of size 2048 next 540
2024-12-07 08:59:07.225176: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5a00 of size 768 next 34
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2024-12-07 08:59:07.225271: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7100 of size 512 next 576
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2024-12-07 08:59:07.227772: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506e00 of size 65536 next 597
2024-12-07 08:59:07.227781: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8516e00 of size 186368 next 447
2024-12-07 08:59:07.227790: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8544600 of size 262144 next 701
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2024-12-07 08:59:07.227816: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8601e00 of size 5373952 next 570
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2024-12-07 08:59:07.227832: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9041e00 of size 2686976 next 725
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2024-12-07 08:59:07.227849: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9561e00 of size 1343488 next 429
2024-12-07 08:59:07.227857: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e96a9e00 of size 12410880 next 700
2024-12-07 08:59:07.227867: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea27fe00 of size 131072 next 602
2024-12-07 08:59:07.227877: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free  at 7ad4ea29fe00 of size 512 next 500
2024-12-07 08:59:07.227886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea2a0000 of size 720384 next 427
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2024-12-07 08:59:07.227906: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea3bfe00 of size 10747904 next 476
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2024-12-07 08:59:07.228008: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f74600 of size 83968 next 626
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2024-12-07 08:59:07.228027: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f147fe00 of size 12059136 next 18446744073709551615
2024-12-07 08:59:07.228035: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296
2024-12-07 08:59:07.228045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad61e000000 of size 2303392000 next 1
2024-12-07 08:59:07.228054: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74af900 of size 1280 next 2
2024-12-07 08:59:07.228062: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74afe00 of size 354368000 next 534
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2024-12-07 08:59:07.228414: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07c00 of size 65536 next 454
2024-12-07 08:59:07.228423: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d17c00 of size 165888 next 62
2024-12-07 08:59:07.228431: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d40400 of size 131072 next 448
2024-12-07 08:59:07.228440: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d60400 of size 131072 next 120
2024-12-07 08:59:07.228448: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80400 of size 1024 next 671
2024-12-07 08:59:07.228456: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80800 of size 256 next 687
2024-12-07 08:59:07.228465: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80900 of size 256 next 449
2024-12-07 08:59:07.228473: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80a00 of size 2048 next 513
2024-12-07 08:59:07.228482: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d81200 of size 231424 next 434
2024-12-07 08:59:07.228491: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714db9a00 of size 83968 next 733
2024-12-07 08:59:07.228500: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dce200 of size 2048 next 591
2024-12-07 08:59:07.228509: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dcea00 of size 272640 next 59
2024-12-07 08:59:07.228517: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e11300 of size 65536 next 585
2024-12-07 08:59:07.228525: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e21300 of size 131072 next 636
2024-12-07 08:59:07.228534: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e41300 of size 131072 next 638
2024-12-07 08:59:07.228542: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e61300 of size 458752 next 512
2024-12-07 08:59:07.228551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed1300 of size 10240 next 85
2024-12-07 08:59:07.228559: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed3b00 of size 83968 next 61
2024-12-07 08:59:07.228568: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ee8300 of size 131072 next 483
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2024-12-07 08:59:07.228589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f28300 of size 32768 next 622
2024-12-07 08:59:07.228598: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f30300 of size 124928 next 101
2024-12-07 08:59:07.228607: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f4eb00 of size 231424 next 520
2024-12-07 08:59:07.228615: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f87300 of size 251904 next 668
2024-12-07 08:59:07.228624: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714fc4b00 of size 524288 next 128
2024-12-07 08:59:07.228633: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad715044b00 of size 604672 next 71
2024-12-07 08:59:07.228642: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150d8500 of size 131072 next 573
2024-12-07 08:59:07.228651: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150f8500 of size 141056 next 444
2024-12-07 08:59:07.228660: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71511ac00 of size 27541504 next 608
2024-12-07 08:59:07.228668: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad716b5ec00 of size 21495808 next 640
2024-12-07 08:59:07.228677: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad717fdec00 of size 21495808 next 84
2024-12-07 08:59:07.228686: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71945ec00 of size 10747904 next 133
2024-12-07 08:59:07.228694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad719e9ec00 of size 10747904 next 744
2024-12-07 08:59:07.228703: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71a8dec00 of size 10747904 next 77
2024-12-07 08:59:07.228711: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71b31ec00 of size 10747904 next 510
2024-12-07 08:59:07.228719: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71bd5ec00 of size 10747904 next 150
2024-12-07 08:59:07.228728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71c79ec00 of size 10747904 next 673
2024-12-07 08:59:07.228736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71d1dec00 of size 14816256 next 18446744073709551615
2024-12-07 08:59:07.228744: I tensorflow/tsl/framework/bfc_allocator.cc:1100]      Summary of in-use Chunks by size: 
2024-12-07 08:59:07.228755: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 196 Chunks of size 256 totalling 49.0KiB
2024-12-07 08:59:07.228764: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 148 Chunks of size 512 totalling 74.0KiB
2024-12-07 08:59:07.228773: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 18 Chunks of size 768 totalling 13.5KiB
2024-12-07 08:59:07.228781: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 37 Chunks of size 1024 totalling 37.0KiB
2024-12-07 08:59:07.228790: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1280 totalling 12.5KiB
2024-12-07 08:59:07.228798: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 1536 totalling 3.0KiB
2024-12-07 08:59:07.228807: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1792 totalling 17.5KiB
2024-12-07 08:59:07.228815: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 2048 totalling 6.0KiB
2024-12-07 08:59:07.228824: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 10240 totalling 10.0KiB
2024-12-07 08:59:07.228833: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 20992 totalling 20.5KiB
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2024-12-07 08:59:07.229284: I tensorflow/tsl/framework/bfc_allocator.cc:1109] Total bytes in pool: 10198122496 memory_limit_: 10198122496 available bytes: 0 curr_region_allocation_bytes_: 17179869184
2024-12-07 08:59:07.229298: I tensorflow/tsl/framework/bfc_allocator.cc:1114] Stats: 
Limit:                     10198122496
InUse:                     10195919872
MaxInUse:                  10195920896
NumAllocs:                    43119401
MaxAllocSize:               2507232000
Reserved:                            0
PeakReserved:                        0
LargestFreeBlock:                    0

2024-12-07 08:59:07.229328: W tensorflow/tsl/framework/bfc_allocator.cc:497] ****************************************************************************************************
2024-12-07 08:59:07.229377: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at matmul_op_impl.h:908 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[20992,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Memoria GPU esaurita, riprovo con batch size più piccolo...
Epoch 1/150
      5/Unknown - 1s 38ms/step - loss: 0.0238 - mae: 0.1432WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0342s vs `on_train_batch_end` time: 0.0350s). Check your callbacks.
   9953/Unknown - 294s 29ms/step - loss: 0.0258 - mae: 0.1480
In [ ]:
percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)
In [ ]:
def evaluate_model_performance(model, data, targets, set_name=""):
    """
    Valuta le performance del modello su un set di dati specifico.
    """
    predictions = model.predict(data, verbose=0)

    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']
    metrics = {}

    for i, name in enumerate(target_names):
        mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))
        mse = np.mean(np.square(targets[:, i] - predictions[:, i]))
        rmse = np.sqrt(mse)
        mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100

        metrics[f"{name}_mae"] = mae
        metrics[f"{name}_rmse"] = rmse
        metrics[f"{name}_mape"] = mape

    if set_name:
        print(f"\nPerformance sul set {set_name}:")
        for metric, value in metrics.items():
            print(f"{metric}: {value:.4f}")

    return metrics


def retrain_model(base_model, train_data, train_targets,
                  val_data, val_targets,
                  test_data, test_targets,
                  epochs=50, batch_size=128):
    """
    Implementa il retraining del modello con i dati combinati.
    """
    print("Valutazione performance iniziali del modello...")
    initial_metrics = {
        'train': evaluate_model_performance(base_model, train_data, train_targets, "training"),
        'val': evaluate_model_performance(base_model, val_data, val_targets, "validazione"),
        'test': evaluate_model_performance(base_model, test_data, test_targets, "test")
    }

    # Combina i dati per il retraining
    combined_data = {
        'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),
        'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])
    }
    combined_targets = np.concatenate([train_targets, val_targets, test_targets])

    # Crea una nuova suddivisione per la validazione
    indices = np.arange(len(combined_targets))
    np.random.shuffle(indices)

    split_idx = int(len(indices) * 0.9)
    train_idx, val_idx = indices[:split_idx], indices[split_idx:]

    # Prepara i dati per il retraining
    retrain_data = {k: v[train_idx] for k, v in combined_data.items()}
    retrain_targets = combined_targets[train_idx]
    retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}
    retrain_val_targets = combined_targets[val_idx]

    # Configura callbacks
    callbacks = [
        tf.keras.callbacks.EarlyStopping(
            monitor='val_loss',
            patience=10,
            restore_best_weights=True,
            min_delta=0.0001
        ),
        tf.keras.callbacks.ReduceLROnPlateau(
            monitor='val_loss',
            factor=0.2,
            patience=5,
            min_lr=1e-6,
            verbose=1
        ),
        tf.keras.callbacks.ModelCheckpoint(
            filepath=f'{execute_name}_retrained_best_oil_model.h5',
            monitor='val_loss',
            save_best_only=True,
            mode='min',
            save_weights_only=True
        )
    ]

    # Imposta learning rate per il fine-tuning
    optimizer = tf.keras.optimizers.AdamW(
        learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(
            initial_learning_rate=1e-4,
            decay_steps=1000,
            decay_rate=0.9
        ),
        weight_decay=0.01
    )

    # Ricompila il modello con il nuovo optimizer
    base_model.compile(
        optimizer=optimizer,
        loss=tf.keras.losses.Huber(),
        metrics=['mae']
    )

    print("\nAvvio retraining...")
    history = base_model.fit(
        retrain_data,
        retrain_targets,
        validation_data=(retrain_val_data, retrain_val_targets),
        epochs=epochs,
        batch_size=batch_size,
        callbacks=callbacks,
        verbose=1
    )

    print("\nValutazione performance finali...")
    final_metrics = {
        'train': evaluate_model_performance(base_model, train_data, train_targets, "training"),
        'val': evaluate_model_performance(base_model, val_data, val_targets, "validazione"),
        'test': evaluate_model_performance(base_model, test_data, test_targets, "test")
    }

    # Salva il modello finale
    save_path = f'{execute_name}_retrained_model.keras'
    os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)
    
    base_model.save_weights(f'{execute_name}_retrained/weights')
    base_model.save(save_path, save_format='keras')
    print(f"\nModello riaddestrato salvato in: {save_path}")

    # Report miglioramenti
    print("\nMiglioramenti delle performance:")
    for dataset in ['train', 'val', 'test']:
        print(f"\nSet {dataset}:")
        for metric in initial_metrics[dataset].keys():
            initial = initial_metrics[dataset][metric]
            final = final_metrics[dataset][metric]
            improvement = ((initial - final) / initial) * 100
            print(f"{metric}: {improvement:.2f}% di miglioramento")

    return base_model, history, final_metrics


def start_retraining(model_path, train_data, train_targets,
                     val_data, val_targets,
                     test_data, test_targets,
                     epochs=50, batch_size=128):
    """
    Avvia il processo di retraining in modo sicuro.
    """
    try:
        print("Caricamento del modello...")
        base_model = tf.keras.models.load_model(model_path, compile=False)
        print("Modello caricato con successo!")

        return retrain_model(
            base_model=base_model,
            train_data=train_data,
            train_targets=train_targets,
            val_data=val_data,
            val_targets=val_targets,
            test_data=test_data,
            test_targets=test_targets,
            epochs=epochs,
            batch_size=batch_size
        )
    except Exception as e:
        print(f"Errore durante il retraining: {str(e)}")
        raise
In [ ]:
model_path = f'{execute_name}_final_model.keras'

retrained_model, retrain_history, final_metrics = start_retraining(
    model_path=model_path,
    train_data=train_data,
    train_targets=train_targets,
    val_data=val_data,
    val_targets=val_targets,
    test_data=test_data,
    test_targets=test_targets,
    epochs=50,
    batch_size=256
)
In [ ]:
percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)
In [ ]:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from typing import List, Dict, Tuple, Union

def analyze_feature_importance(model: tf.keras.Model, 
                             test_data: dict, 
                             feature_names: List[str]) -> Dict[str, float]:
    """
    Analizza l'importanza delle feature usando perturbazione.
    
    Args:
        model: Modello TensorFlow addestrato
        test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati
        feature_names: Lista dei nomi delle feature
        
    Returns:
        dict: Dizionario con l'importanza relativa di ogni feature
    """
    # Estrai i dati temporali e statici
    temporal_data = test_data['temporal']
    static_data = test_data['static']
    
    # Ottieni la predizione base
    base_prediction = model.predict(test_data)
    feature_importance = {}
    
    # Per ogni feature temporale
    for i, feature in enumerate(feature_names):
        if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:
            # Crea copia perturbata dei dati
            perturbed_data = {
                'temporal': temporal_data.copy(),
                'static': static_data.copy()
            }
            
            # Trova l'indice della feature temporale
            temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)
            
            # Crea rumore per la feature temporale
            feature_values = temporal_data[..., temp_idx]
            noise = np.random.normal(0, np.std(feature_values) * 0.1, 
                                   size=feature_values.shape)
            
            # Applica il rumore alla feature temporale
            perturbed_temporal = perturbed_data['temporal'].copy()
            perturbed_temporal[..., temp_idx] = feature_values + noise
            perturbed_data['temporal'] = perturbed_temporal
            
        else:  # Feature statiche
            # Crea copia perturbata dei dati
            perturbed_data = {
                'temporal': temporal_data.copy(),
                'static': static_data.copy()
            }
            
            # Trova l'indice della feature statica
            static_idx = ['ha'].index(feature)
            
            # Crea rumore per la feature statica
            feature_values = static_data[..., static_idx]
            noise = np.random.normal(0, np.std(feature_values) * 0.1, 
                                   size=feature_values.shape)
            
            # Applica il rumore alla feature statica
            perturbed_static = perturbed_data['static'].copy()
            perturbed_static[..., static_idx] = feature_values + noise
            perturbed_data['static'] = perturbed_static
        
        # Calcola nuova predizione
        perturbed_prediction = model.predict(perturbed_data)
        
        # Calcola impatto della perturbazione
        impact = np.mean(np.abs(perturbed_prediction - base_prediction))
        feature_importance[feature] = float(impact)
    
    # Normalizza le importanze
    total_importance = sum(feature_importance.values())
    feature_importance = {k: v/total_importance 
                         for k, v in feature_importance.items()}
    
    return feature_importance

class ProbabilityFunctions:
    @staticmethod
    def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:
        """
        Calcola statistiche di base usando TensorFlow.
        
        Args:
            data: Tensor o array dei dati
            
        Returns:
            dict: Dizionario con le statistiche
        """
        if not isinstance(data, tf.Tensor):
            data = tf.convert_to_tensor(data, dtype=tf.float32)
            
        mean = tf.reduce_mean(data)
        # Calcola varianza manualmente
        squared_deviations = tf.square(data - mean)
        variance = tf.reduce_mean(squared_deviations)
        std = tf.sqrt(variance)
        
        # Ordina il tensor per il calcolo della mediana
        sorted_data = tf.sort(data)
        size = tf.size(data)
        mid_index = size // 2
        median = sorted_data[mid_index]
        
        return {
            'mean': mean.numpy(),
            'variance': variance.numpy(),
            'std': std.numpy(),
            'min': tf.reduce_min(data).numpy(),
            'max': tf.reduce_max(data).numpy(),
            'median': median.numpy()
        }

    @staticmethod
    def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Calcola la Probability Mass Function (PMF) dei dati.
        
        Args:
            data: Array di dati
            bins: Numero di bin per l'istogramma
            
        Returns:
            tuple: (bin_centers, pmf, bin_edges)
        """
        # Calcola l'istogramma
        hist, bin_edges = np.histogram(data, bins=bins, density=True)
        
        # Calcola i centri dei bin
        bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
        
        # Normalizza per ottenere la PMF
        pmf = hist / np.sum(hist)
        
        return bin_centers, pmf, bin_edges

    @staticmethod
    def calculate_cmf(pmf: np.ndarray) -> np.ndarray:
        """
        Calcola la Cumulative Mass Function (CMF) dalla PMF.
        
        Args:
            pmf: Probability Mass Function
            
        Returns:
            array: Cumulative Mass Function
        """
        return np.cumsum(pmf)

    def plot_distributions(self, data: np.ndarray, 
                         bins: int = 50, 
                         title: str = "Distribuzione") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Calcola e visualizza PMF e CMF delle distribuzioni.
        
        Args:
            data: Array di dati da analizzare
            bins: Numero di bin per l'istogramma
            title: Titolo del grafico
            
        Returns:
            tuple: (bin_centers, pmf, cmf)
        """
        # Calcola PMF e CMF
        bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)
        cmf = self.calculate_cmf(pmf)
        
        # Crea il plot
        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
        
        # Plot PMF
        width = np.diff(bin_edges)
        ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')
        ax1.set_title('Probability Mass Function')
        ax1.set_ylabel('Probability')
        ax1.grid(True, alpha=0.3)
        ax1.legend()
        
        # Plot CMF
        ax2.plot(bin_centers, cmf, 'r-', label='CMF')
        ax2.set_title('Cumulative Mass Function')
        ax2.set_xlabel('Value')
        ax2.set_ylabel('Cumulative Probability')
        ax2.grid(True, alpha=0.3)
        ax2.legend()
        
        # Imposta il titolo generale
        fig.suptitle(title, y=1.02)
        plt.tight_layout()
        plt.show()
        
        return bin_centers, pmf, cmf

def analyze_model_predictions(model: tf.keras.Model, 
                            test_data: np.ndarray,
                            test_targets: np.ndarray,
                            scaler_y) -> None:
    """
    Analizza le distribuzioni di probabilità delle predizioni del modello.
    
    Args:
        model: Modello TensorFlow addestrato
        test_data: Dati di test
        test_targets: Target di test
        scaler_y: Scaler usato per denormalizzare i target
    """
    # Ottieni le predizioni
    predictions = model.predict(test_data)
    
    # Denormalizza predizioni e target
    predictions_real = scaler_y.inverse_transform(predictions)
    targets_real = scaler_y.inverse_transform(test_targets)
    
    # Inizializza la classe per l'analisi delle probabilità
    prob = ProbabilityFunctions()
    
    # Analizza ogni target
    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 
                   'avg_oil_prod', 'total_water_need']
    
    for i, target in enumerate(target_names):
        print(f"\nAnalisi per {target}")
        print("-" * 50)
        
        # Calcola errori
        errors = predictions_real[:, i] - targets_real[:, i]
        
        # Calcola statistiche degli errori
        error_stats = prob.calculate_statistics(errors)
        print("\nStatistiche degli Errori:")
        for key, value in error_stats.items():
            print(f"{key}: {value:.3f}")
        
        # Visualizza le distribuzioni degli errori
        bin_centers, pmf, cmf = prob.plot_distributions(
            errors, 
            bins=50,
            title=f"Distribuzione degli Errori - {target}"
        )
        
        # Calcola intervalli di confidenza
        confidence_levels = [0.68, 0.95, 0.99]  # 1σ, 2σ, 3σ
        for level in confidence_levels:
            lower_idx = np.searchsorted(cmf, (1 - level) / 2)
            upper_idx = np.searchsorted(cmf, (1 + level) / 2)
            
            print(f"\nIntervallo di Confidenza {level*100}%:")
            print(f"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]")

def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):
    """
    Esegue un'analisi completa del modello includendo errori,
    importanza delle feature e distribuzioni.
    """
    print("=== ANALISI COMPLETA DEL MODELLO ===")
    
    # 1. Analisi degli errori
    print("\n1. ANALISI DEGLI ERRORI")
    print("-" * 50)
    analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)
    
    # 2. Analisi dell'importanza delle feature
    print("\n2. IMPORTANZA DELLE FEATURE")
    print("-" * 50)
    
    # Definisci i nomi delle feature
    temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']
    static_features = ['ha']
    
    all_features = temporal_features + static_features
    importance = analyze_feature_importance(retrained_model, test_data, all_features)
    
    print("\nImportanza relativa delle feature:")
    for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):
        print(f"{feature}: {imp:.4f}")
        
    # 3. Analisi distribuzionale
    print("\n3. ANALISI DISTRIBUZIONALE")
    print("-" * 50)
    
    prob = ProbabilityFunctions()
    predictions = retrained_model.predict(test_data)
    predictions_real = scaler_y.inverse_transform(predictions)
    targets_real = scaler_y.inverse_transform(test_targets)
    
    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 
                    'avg_oil_prod', 'total_water_need']
    
    for i, target in enumerate(target_names):
        print(f"\nAnalisi distribuzionale per {target}")
        
        # Statistiche
        stats_pred = prob.calculate_statistics(predictions_real[:, i])
        stats_true = prob.calculate_statistics(targets_real[:, i])
        
        print("\nStatistiche Predizioni:")
        for key, value in stats_pred.items():
            print(f"{key}: {value:.3f}")
            
        print("\nStatistiche Target Reali:")
        for key, value in stats_true.items():
            print(f"{key}: {value:.3f}")
        
        # Visualizza distribuzioni
        prob.plot_distributions(predictions_real[:, i], bins=50,
                              title=f"Distribuzione Predizioni - {target}")
        prob.plot_distributions(targets_real[:, i], bins=50,
                              title=f"Distribuzione Target Reali - {target}")

def analyze_model_predictions(model, test_data, test_targets, scaler_y):
    """
    Analizza le distribuzioni di probabilità delle predizioni del modello.
    
    Args:
        model: Modello TensorFlow addestrato
        test_data: Dati di test
        test_targets: Target di test
        scaler_y: Scaler usato per denormalizzare i target
    """
    # Ottieni le predizioni
    predictions = model.predict(test_data)
    
    # Denormalizza predizioni e target
    predictions_real = scaler_y.inverse_transform(predictions)
    targets_real = scaler_y.inverse_transform(test_targets)
    
    # Inizializza la classe per l'analisi delle probabilità
    prob = ProbabilityFunctions()
    
    # Analizza ogni target
    target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 
                   'avg_oil_prod', 'total_water_need']
    
    for i, target in enumerate(target_names):
        print(f"\nAnalisi per {target}")
        print("-" * 50)
        
        # Calcola errori
        errors = predictions_real[:, i] - targets_real[:, i]
        
        # Calcola statistiche degli errori
        error_stats = prob.calculate_statistics(errors)
        print("\nStatistiche degli Errori:")
        for key, value in error_stats.items():
            print(f"{key}: {value:.3f}")
        
        # Visualizza le distribuzioni degli errori
        bin_centers, pmf, cmf = prob.plot_distributions(
            errors, 
            bins=50,
            title=f"Distribuzione degli Errori - {target}"
        )
        
        # Calcola intervalli di confidenza
        confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99]  # 1σ, 2σ, 3σ
        for level in confidence_levels:
            lower_idx = np.searchsorted(cmf, (1 - level) / 2)
            upper_idx = np.searchsorted(cmf, (1 + level) / 2)
            
            print(f"\nIntervallo di Confidenza {level*100}%:")
            print(f"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]")

class ProbabilityFunctions:
    @staticmethod
    def calculate_statistics(data):
        """
        Calcola statistiche di base usando TensorFlow.
        
        Args:
            data: Tensor dei dati
            
        Returns:
            dict: Dizionario con le statistiche
        """
        if not isinstance(data, tf.Tensor):
            data = tf.convert_to_tensor(data, dtype=tf.float32)
            
        mean = tf.reduce_mean(data)
        # Calculate variance manually
        squared_deviations = tf.square(data - mean)
        variance = tf.reduce_mean(squared_deviations)
        std = tf.sqrt(variance)
        
        # Sort the tensor for median calculation
        sorted_data = tf.sort(data)
        size = tf.size(data)
        mid_index = size // 2
        median = sorted_data[mid_index]
        
        return {
            'mean': mean.numpy(),
            'variance': variance.numpy(),
            'std': std.numpy(),
            'min': tf.reduce_min(data).numpy(),
            'max': tf.reduce_max(data).numpy(),
            'median': median.numpy()
        }

    @staticmethod
    def calculate_pmf(data, bins=50):
        """
        Calcola la Probability Mass Function (PMF) dei dati.
        
        Args:
            data: Array di dati
            bins: Numero di bin per l'istogramma
            
        Returns:
            tuple: (bin_centers, pmf, bin_edges)
        """
        # Calcola l'istogramma
        hist, bin_edges = np.histogram(data, bins=bins, density=True)
        
        # Calcola i centri dei bin
        bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
        
        # Normalizza per ottenere la PMF
        pmf = hist / np.sum(hist)
        
        return bin_centers, pmf, bin_edges

    @staticmethod
    def calculate_cmf(pmf):
        """
        Calcola la Cumulative Mass Function (CMF) dalla PMF.
        
        Args:
            pmf: Probability Mass Function
            
        Returns:
            array: Cumulative Mass Function
        """
        return np.cumsum(pmf)

    def plot_distributions(self, data, bins=50, title="Distribuzione"):
        """
        Calcola e visualizza PMF e CMF delle distribuzioni.
        
        Args:
            data: Array di dati da analizzare
            bins: Numero di bin per l'istogramma
            title: Titolo del grafico
            
        Returns:
            tuple: (bin_centers, pmf, cmf)
        """
        # Calcola PMF e CMF
        bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)
        cmf = self.calculate_cmf(pmf)
        
        # Crea il plot
        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
        
        # Plot PMF
        width = np.diff(bin_edges)
        ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')
        ax1.set_title('Probability Mass Function')
        ax1.set_ylabel('Probability')
        ax1.grid(True, alpha=0.3)
        ax1.legend()
        
        # Plot CMF
        ax2.plot(bin_centers, cmf, 'r-', label='CMF')
        ax2.set_title('Cumulative Mass Function')
        ax2.set_xlabel('Value')
        ax2.set_ylabel('Cumulative Probability')
        ax2.grid(True, alpha=0.3)
        ax2.legend()
        
        # Set overall title
        fig.suptitle(title, y=1.02)
        plt.tight_layout()
        plt.show()
        
        return bin_centers, pmf, cmf
In [ ]:
run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)