IUM_06
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@ -1,5 +1,6 @@
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import pandas as pd
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import tensorflow as tf
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import sys
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train_data = pd.read_csv('./beer_reviews_train.csv')
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X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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@ -22,6 +23,6 @@ model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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model.fit(X_train_pad, y_train, epochs=40, batch_size=32, validation_split=0.1)
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model.fit(X_train_pad, y_train, epochs=sys.argv[1], batch_size=sys.argv[2], validation_split=0.1)
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model.save('beer_review_sentiment_model.h5')
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11
Jenkinsfile
vendored
11
Jenkinsfile
vendored
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pipeline {
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agent any
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triggers {
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upstream(upstreamProjects: 'z-s464979-create-datase', threshold: hudson.model.Result.SUCCESS)
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}
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parameters {
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string(name: 'CUTOFF', defaultValue: '10000', description: 'Liczba wierszy do obcięcia ze zbioru danych')
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string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
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password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
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string(name: 'EPOCHS', defaultValue: '40', description: 'Number of epochs')
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string(name: 'BATCH_SIZE', defaultValue: '32', description: 'Batch size')
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buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
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}
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@ -28,7 +31,7 @@ pipeline {
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}
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steps {
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sh "chmod +x ./IUM_05-model.py"
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sh "python3 ./IUM_05-model.py"
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sh "python3 ./IUM_05-model.py ${params.EPOCHS} ${params.BATCH_SIZE}"
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archiveArtifacts artifacts: 'beer_review_sentiment_model.h5', onlyIfSuccessful: true
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}
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}
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