ray.train.tensorflow.TensorflowPredictor.predict

TensorflowPredictor.predict(data: Union[numpy.ndarray, pandas.DataFrame, Dict[str, numpy.ndarray]], dtype: Optional[Union[tensorflow.python.framework.dtypes.DType, Dict[str, tensorflow.python.framework.dtypes.DType]]] = None) → Union[numpy.ndarray, pandas.DataFrame, Dict[str, numpy.ndarray]]

Run inference on data batch.

If the provided data is a single array or a dataframe/table with a single column, it will be converted into a single Tensorflow tensor before being inputted to the model.

If the provided data is a multi-column table or a dict of numpy arrays, it will be converted into a dict of tensors before being inputted to the model. This is useful for multi-modal inputs (for example your model accepts both image and text).

Parameters

• data – A batch of input data. Either a pandas DataFrame or numpy array.

• dtype – The dtypes to use for the tensors. Either a single dtype for all tensors or a mapping from column name to dtype.

Examples:

import numpy as np
import tensorflow as tf
from ray.train.tensorflow import TensorflowPredictor

def build_model():
    return tf.keras.Sequential(
        [
            tf.keras.layers.InputLayer(input_shape=()),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(1),
        ]
    )

weights = [np.array([[2.0]]), np.array([0.0])]
predictor = TensorflowPredictor(model=build_model())

data = np.asarray([1, 2, 3])
predictions = predictor.predict(data)

import pandas as pd
import tensorflow as tf
from ray.train.tensorflow import TensorflowPredictor

def build_model():
    input1 = tf.keras.layers.Input(shape=(1,), name="A")
    input2 = tf.keras.layers.Input(shape=(1,), name="B")
    merged = tf.keras.layers.Concatenate(axis=1)([input1, input2])
    output = tf.keras.layers.Dense(2, input_dim=2)(merged)
    return tf.keras.models.Model(
        inputs=[input1, input2], outputs=output)

predictor = TensorflowPredictor(model=build_model())

# Pandas dataframe.
data = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])

predictions = predictor.predict(data)

Returns

Prediction result. The return type will be the same as the

input type.

Return type

DataBatchType