wide_deep_evaluate_predict.py 文件源码

def input_fn(batch_size,file_name):
    """
    Input function creates feautre and label dict for cross-validation
    :param batch_size:
    :param file_name:
    :return: feature dict
    """
    examples_op = tf.contrib.learn.read_batch_examples(
        file_name,
        batch_size=batch_size,
        reader=tf.TextLineReader,
    num_threads=5,
        num_epochs=1,
        randomize_input=False,
        parse_fn=lambda x: tf.decode_csv(x, [tf.constant([''], dtype=tf.string)] * len(COLUMNS),field_delim=","))

    examples_dict = {}

    for i, header in enumerate(COLUMNS):
        examples_dict[header] = examples_op[:,i]


    feature_cols = {k: tf.string_to_number(examples_dict[k], out_type=tf.float32)
                    for k in CONTINUOUS_COLUMNS}

    feature_cols.update({k: dense_to_sparse(examples_dict[k])
                         for k in CATEGORICAL_COLUMNS})

    label = tf.string_to_number(examples_dict[LABEL_COLUMN], out_type=tf.int32)

    return feature_cols, label