82 lines
2.2 KiB
Plaintext
82 lines
2.2 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense\n",
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"\n",
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"# Wczytywanie danych\n",
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"data = pd.read_csv('openpowerlifting.csv')\n",
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"\n",
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"# Zakładając, że kolumny to 'squat', 'bench_press', 'deadlift' i 'total'\n",
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"features = data[['squat', 'bench_press', 'deadlift']]\n",
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"target = data['total']\n",
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"\n",
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"# Podział na dane treningowe i testowe\n",
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"X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
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"\n",
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"# Normalizacja danych\n",
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"scaler = StandardScaler()\n",
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"X_train = scaler.fit_transform(X_train)\n",
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"X_test = scaler.transform(X_test) # Używamy tego samego scaler do danych testowych\n",
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"\n",
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"# Tworzenie modelu\n",
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"model = Sequential([\n",
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" Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
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" Dense(64, activation='relu'),\n",
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" Dense(1)\n",
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"])\n",
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"\n",
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"model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
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"\n",
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"# Trenowanie modelu\n",
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"model.fit(X_train, y_train, epochs=10, validation_split=0.1) # Używam validation_split zamiast oddzielnego zbioru\n",
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"\n",
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"# Save the model\n",
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"model.save('powerlifting_model.h5')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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