def sparse_categorical_crossentropy(output, target, from_logits=False):
    # Note: tf.nn.softmax_cross_entropy_with_logits
    # expects logits, Keras expects probabilities.
    if not from_logits:
        output = tf.clip_by_value(output, tf.cast(_EPSILON, dtype=_FLOATX),
                                  tf.cast(1.-_EPSILON, dtype=_FLOATX))
        output = tf.log(output)

    output_shape = output.get_shape()
    res = tf.nn.sparse_softmax_cross_entropy_with_logits(
        tf.reshape(output, [-1, int(output_shape[-1])]),
        cast(flatten(target), 'int64'))
    if len(output_shape) == 3:
        # if our output includes timesteps we need to reshape
        return tf.reshape(res, [-1, int(output_shape[-2])])
    else:
        return res
Return the state and number of results of a query by job id.
dataset/ETHPy150Open fchollet/keras/keras/backend/tensorflow_backend.py/sparse_categorical_crossentropy--crossover with--dataset/ETHPy150Open tylertreat/BigQuery-Python/bigquery/client.py/BigQueryClient.check_job