mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from typing import List
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import numpy as np
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import ray
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from ray.data.preprocessors import Concatenator
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from ray.train.tensorflow import TensorflowCheckpoint, TensorflowPredictor
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from ray.train.batch_predictor import BatchPredictor
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def create_model(input_features):
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from tensorflow import keras # this is needed for tf<2.9
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from tensorflow.keras import layers
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return keras.Sequential(
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[
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keras.Input(shape=(input_features,)),
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layers.Dense(16, activation="relu"),
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layers.Dense(16, activation="relu"),
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layers.Dense(1, activation="sigmoid"),
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]
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)
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dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
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all_features: List[str] = dataset.schema().names
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all_features.remove("target")
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num_features = len(all_features)
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prep = Concatenator(dtype=np.float32)
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checkpoint = TensorflowCheckpoint.from_model(
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model=create_model(num_features), preprocessor=prep
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)
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# You can also fetch a checkpoint from a Trainer
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# checkpoint = trainer.fit().checkpoint
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batch_predictor = BatchPredictor.from_checkpoint(
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checkpoint, TensorflowPredictor, model_definition=lambda: create_model(num_features)
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)
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predicted_probabilities = batch_predictor.predict(dataset, feature_columns=all_features)
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predicted_probabilities.show()
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# {'predictions': array([1.], dtype=float32)}
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# {'predictions': array([0.], dtype=float32)}
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