28 lines
968 B
Python
28 lines
968 B
Python
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import pandas as pd
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import tensorflow as tf
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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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y_train = train_data['review_overall']
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tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
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tokenizer.fit_on_texts(X_train)
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X_train_seq = tokenizer.texts_to_sequences(X_train)
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X_train_pad = tf.keras.preprocessing.sequence.pad_sequences(X_train_seq, maxlen=100)
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model = tf.keras.Sequential([
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tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=100),
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tf.keras.layers.GlobalAveragePooling1D(),
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tf.keras.layers.Dense(16, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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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.save('beer_review_sentiment_model.h5')
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