Convert pandas DataFrame into TensorFlow Dataset

convert_dataframe_to_dataset.py

import numpy as np
import pandas as pd
import tensorflow as tf

tf.enable_eager_execution()

training_df: pd.DataFrame = pd.DataFrame(
    data={
        'feature1': np.random.rand(10),
        'feature2': np.random.rand(10),
        'feature3': np.random.rand(10),
        'target': np.random.randint(0, 3, 10)
    }
)
features = ['feature1', 'feature2', 'feature3']
print(training_df)

training_dataset = (
    tf.data.Dataset.from_tensor_slices(
        (
            tf.cast(training_df[features].values, tf.float32),
            tf.cast(training_df['target'].values, tf.int32)
        )
    )
)

for features_tensor, target_tensor in training_dataset:
    print(f'features:{features_tensor} target:{target_tensor}')

Line number 18 does not return a dataset type object but a TensorSliceDataset object. Could you suggest how to convert it to Dataset?

Line number 18 does not return a dataset type object but a TensorSliceDataset object. Could you suggest how to convert it to Dataset?

I think it does return a dataset.