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- import tensorflow as tf
- from tensorflow.keras.models import load_model
- savedModel = load_model('TaxiCsvModel.keras')
- # print(f'savedModel.summary() = {savedModel.summary()}')
- sample = { # cash
- "passengers": 1,
- "color": "yellow",
- "pickup_zone": "Upper West Side South",
- "dropoff_zone": "Upper West Side South",
- "pickup_borough": "Manhattan",
- "dropoff_borough": "Manhattan",
- "distance": 0.8,
- "fare": 5.0,
- "tip": 0.0,
- "tolls": 0.0,
- "total": 9.31,
- } # This passengers had a 93.37% probability payment with a credit card.
- # sample = { # credit card
- # "passengers": 1,
- # "color": "yellow",
- # "pickup_zone": "Lenox Hill West",
- # "dropoff_zone": "UN/Turtle Bay South",
- # "pickup_borough": "Manhattan",
- # "dropoff_borough": "Manhattan",
- # "distance": 1.60,
- # "fare": 7.0,
- # "tip": 2.15,
- # "tolls": 0.0,
- # "total": 12.95,
- # } # This passengers had a 97.98% probability payment with a credit card.
- input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}
- predictions = savedModel.predict(input_dict)
- print(
- f"This passengers had a {100 * predictions[0][0]:.2f}% probability "
- "payment with a credit card."
- )
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