# isort: skip_file
# __use_pretrained_model_start__
import ray
import tensorflow as tf
from ray.train.batch_predictor import BatchPredictor
from ray.train.tensorflow import (
to_air_checkpoint,
TensorflowPredictor,
)
# to simulate having a pretrained model.
def build_model() -> tf.keras.Model:
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(1, )),
tf.keras.layers.Dense(1),
])
return model
model = build_model()
checkpoint = to_air_checkpoint(model)
batch_predictor = BatchPredictor(checkpoint,
TensorflowPredictor,
model_definition=build_model)
predict_dataset = ray.data.range(3)
predictions = batch_predictor.predict(predict_dataset)
# __use_pretrained_model_end__
# __batch_prediction_start__
import pandas as pd
from ray.train.batch_predictor import BatchPredictor
batch_predictor = BatchPredictor(checkpoint,
TensorflowPredictor,
model_definition=build_model)
# Create a dummy dataset.
ds = ray.data.from_pandas(
pd.DataFrame({
"feature_1": [1, 2, 3],
"label": [1, 2, 3]
}))
# Use `feature_columns` to specify the input columns to your model.
predictions = batch_predictor.predict(ds, feature_columns=["feature_1"])
print(predictions.show())
# {'predictions': array([-1.2789773], dtype=float32)}
# {'predictions': array([-2.5579545], dtype=float32)}
# {'predictions': array([-3.8369317], dtype=float32)}
# __batch_prediction_end__
# __compute_accuracy_start__
def calculate_accuracy(df):
return pd.DataFrame({"correct": int(df["predictions"][0]) == df["label"]})
predictions = batch_predictor.predict(ds,
feature_columns=["feature_1"],
