Exemplo n.º 1
0
def padded_accuracy(predictions,
                    labels,
                    weights_fn=common_layers.weights_nonzero):
    """Percentage of times that predictions matches labels on non-0s."""
    with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
        padded_labels = common_layers.pad_with_zeros(predictions, labels)
        weights = weights_fn(padded_labels)
        outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
        return tf.to_float(tf.equal(outputs, padded_labels)), weights
Exemplo n.º 2
0
def padded_accuracy(predictions,
                    labels,
                    weights_fn=common_layers.weights_nonzero):
  """Percentage of times that predictions matches labels on non-0s."""
  with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
    padded_labels = common_layers.pad_with_zeros(predictions, labels)
    weights = weights_fn(padded_labels)
    outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
    return tf.to_float(tf.equal(outputs, padded_labels)), weights
Exemplo n.º 3
0
def padded_sequence_accuracy(predictions,
                             labels,
                             weights_fn=common_layers.weights_nonzero):
  """Percentage of times that predictions matches labels everywhere (non-0)."""
  with tf.variable_scope(
      "padded_sequence_accuracy", values=[predictions, labels]):
    padded_labels = common_layers.pad_with_zeros(predictions, labels)
    weights = weights_fn(padded_labels)
    outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
    not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
    axis = list(range(1, len(outputs.get_shape())))
    correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
    return correct_seq, tf.constant(1.0)
Exemplo n.º 4
0
def padded_sequence_accuracy(predictions,
                             labels,
                             weights_fn=common_layers.weights_nonzero):
  """Percentage of times that predictions matches labels everywhere (non-0)."""
  with tf.variable_scope(
      "padded_sequence_accuracy", values=[predictions, labels]):
    padded_predictions, padded_labels = common_layers.pad_with_zeros(
        predictions, labels)
    weights = weights_fn(padded_labels)
    outputs = tf.to_int32(tf.argmax(padded_predictions, axis=-1))
    not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
    axis = list(range(1, len(outputs.get_shape())))
    correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
    return correct_seq, tf.constant(1.0)
Exemplo n.º 5
0
def padded_accuracy_topk(predictions,
                         labels,
                         k,
                         weights_fn=common_layers.weights_nonzero):
  """Percentage of times that top-k predictions matches labels on non-0s."""
  with tf.variable_scope("padded_accuracy_topk", values=[predictions, labels]):
    padded_labels = common_layers.pad_with_zeros(predictions, labels)
    weights = weights_fn(padded_labels)
    effective_k = tf.minimum(k, tf.shape(predictions)[-1])
    _, outputs = tf.nn.top_k(predictions, k=effective_k)
    outputs = tf.to_int32(outputs)
    padded_labels = tf.expand_dims(padded_labels, axis=-1)
    padded_labels += tf.zeros_like(outputs)  # Pad to same shape.
    same = tf.to_float(tf.equal(outputs, padded_labels))
    same_topk = tf.reduce_sum(same, axis=-1)
    return same_topk, weights
Exemplo n.º 6
0
def padded_accuracy_topk(predictions,
                         labels,
                         k,
                         weights_fn=common_layers.weights_nonzero):
    """Percentage of times that top-k predictions matches labels on non-0s."""
    with tf.variable_scope("padded_accuracy_topk",
                           values=[predictions, labels]):
        padded_labels = common_layers.pad_with_zeros(predictions, labels)
        weights = weights_fn(padded_labels)
        effective_k = tf.minimum(k, tf.shape(predictions)[-1])
        _, outputs = tf.nn.top_k(predictions, k=effective_k)
        outputs = tf.to_int32(outputs)
        padded_labels = tf.expand_dims(padded_labels, axis=-1)
        padded_labels += tf.zeros_like(outputs)  # Pad to same shape.
        same = tf.to_float(tf.equal(outputs, padded_labels))
        same_topk = tf.reduce_sum(same, axis=-1)
        return same_topk, weights