python类assert_non_negative()的实例源码

def pad_up_to(vector, size, rank):
    length_diff = tf.reshape(size - tf.shape(vector)[1], shape=(1,))
    with tf.control_dependencies([tf.assert_non_negative(length_diff, data=(vector, size, tf.shape(vector)))]):
        padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0,0]*(rank-1)], axis=0), shape=((rank+1), 2))
        return tf.pad(vector, padding, mode='constant')

def pad_up_to(vector, size):
    rank = vector.get_shape().ndims - 1

    length_diff = tf.reshape(size - tf.shape(vector)[1], shape=(1,))
    with tf.control_dependencies([tf.assert_non_negative(length_diff, data=(vector, size, tf.shape(vector)))]):
        padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0,0]*(rank-1)], axis=0), shape=((rank+1), 2))
        return tf.pad(vector, padding, mode='constant')

def add_loss_op(self, result):
        logits = result.rnn_output
        with tf.control_dependencies([tf.assert_positive(tf.shape(logits)[1], data=[tf.shape(logits)])]):
            length_diff = tf.reshape(self.config.max_length - tf.shape(logits)[1], shape=(1,))
        padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0, 0]], axis=0), shape=(3, 2))
        preds = tf.pad(logits, padding, mode='constant')

        # add epsilon to avoid division by 0
        preds = preds + 1e-5

        mask = tf.sequence_mask(self.output_length_placeholder, self.config.max_length, dtype=tf.float32)
        loss = tf.contrib.seq2seq.sequence_loss(preds, self.output_placeholder, mask)

        with tf.control_dependencies([tf.assert_non_negative(loss, data=[preds, mask], summarize=256*60*300)]):
            return tf.identity(loss)

def mix_prediction(losses, lam=0., mean_typ='arithmetic', weight_typ='normal', sign=-1., sf=1e-3):
    # losses is shape (# of discriminators x batch_size)
    # output is scalar

    tf.assert_non_negative(lam)
    assert mean_typ in ['arithmetic','geometric','harmonic']
    assert weight_typ in ['normal','log']
    assert sign == 1. or sign == -1.
    assert sf > 0.

    if lam == 0.:
        weights = tf.ones_like(losses)
    else:
        if weight_typ == 'log':
            weights = tf.pow(losses, lam)
        else:
            weights = tf.exp(lam * losses)

    if mean_typ == 'arithmetic':
        loss = weighted_arithmetic(weights, losses)
    elif mean_typ == 'geometric':
        log_losses = tf.log(sign*losses)
        loss = sign*tf.exp(weighted_arithmetic(weights, log_losses))
    else:
        mn = tf.reduce_min(losses) - sf
        inv_losses = tf.reciprocal(losses-mn)
        loss = mn + tf.reciprocal(weighted_arithmetic(weights, inv_losses))

    return loss

def five_crops(image, crop_size):
    """ Returns the central and four corner crops of `crop_size` from `image`. """
    image_size = tf.shape(image)[:2]
    crop_margin = tf.subtract(image_size, crop_size)
    assert_size = tf.assert_non_negative(
        crop_margin, message='Crop size must be smaller or equal to the image size.')
    with tf.control_dependencies([assert_size]):
        top_left = tf.floor_div(crop_margin, 2)
        bottom_right = tf.add(top_left, crop_size)
    center       = image[top_left[0]:bottom_right[0], top_left[1]:bottom_right[1]]
    top_left     = image[:-crop_margin[0], :-crop_margin[1]]
    top_right    = image[:-crop_margin[0], crop_margin[1]:]
    bottom_left  = image[crop_margin[0]:, :-crop_margin[1]]
    bottom_right = image[crop_margin[0]:, crop_margin[1]:]
    return center, top_left, top_right, bottom_left, bottom_right

def op(name,
       images,
       max_outputs=3,
       display_name=None,
       description=None,
       collections=None):
  """Create an image summary op for use in a TensorFlow graph.

  Arguments:
    name: A unique name for the generated summary node.
    images: A `Tensor` representing pixel data with shape `[k, w, h, c]`,
      where `k` is the number of images, `w` and `h` are the width and
      height of the images, and `c` is the number of channels, which
      should be 1, 3, or 4. Any of the dimensions may be statically
      unknown (i.e., `None`).
    max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
      many images will be emitted at each step. When more than
      `max_outputs` many images are provided, the first `max_outputs` many
      images will be used and the rest silently discarded.
    display_name: Optional name for this summary in TensorBoard, as a
      constant `str`. Defaults to `name`.
    description: Optional long-form description for this summary, as a
      constant `str`. Markdown is supported. Defaults to empty.
    collections: Optional list of graph collections keys. The new
      summary op is added to these collections. Defaults to
      `[Graph Keys.SUMMARIES]`.

  Returns:
    A TensorFlow summary op.
  """
  if display_name is None:
    display_name = name
  summary_metadata = metadata.create_summary_metadata(
      display_name=display_name, description=description)
  with tf.name_scope(name), \
       tf.control_dependencies([tf.assert_rank(images, 4),
                                tf.assert_type(images, tf.uint8),
                                tf.assert_non_negative(max_outputs)]):
    limited_images = images[:max_outputs]
    encoded_images = tf.map_fn(tf.image.encode_png, limited_images,
                               dtype=tf.string,
                               name='encode_each_image')
    image_shape = tf.shape(images)
    dimensions = tf.stack([tf.as_string(image_shape[1], name='width'),
                           tf.as_string(image_shape[2], name='height')],
                          name='dimensions')
    tensor = tf.concat([dimensions, encoded_images], axis=0)
    return tf.summary.tensor_summary(name='image_summary',
                                     tensor=tensor,
                                     collections=collections,
                                     summary_metadata=summary_metadata)