Python v1.clip_by_value方法代码示例

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def _discriminator_alpha(block_id, progress):
  """Returns the block input parameter for discriminator network.

  The discriminator has N blocks with `block_id` = 1,2,...,N. Each block
  block_id accepts an
    - input(block_id) transformed from the real data and
    - the output of block block_id + 1, i.e. output(block_id + 1)
  The final input is a linear combination of them,
  i.e. alpha * input(block_id) + (1 - alpha) * output(block_id + 1)
  where alpha = _discriminator_alpha(block_id, progress).

  With a fixed block_id, alpha(block_id, progress) stays to be 1
  when progress <= block_id - 1, then linear decays to 0 when
  block_id - 1 < progress <= block_id, and finally stays at 0
  when progress > block_id.

  Args:
    block_id: An integer of generator block id.
    progress: A scalar float `Tensor` of training progress.

  Returns:
    A scalar float `Tensor` of block input parameter.
  """
  return tf.clip_by_value(block_id - progress, 0.0, 1.0) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def _get_projection(p):
  """Returns a projection function."""
  if p == np.inf:
    def _projection(perturbation, epsilon, input_image, image_bounds):
      clipped_perturbation = tf.clip_by_value(perturbation, -epsilon, epsilon)
      new_image = tf.clip_by_value(input_image + clipped_perturbation,
                                   image_bounds[0], image_bounds[1])
      return new_image - input_image
    return _projection

  elif p == 2:
    def _projection(perturbation, epsilon, input_image, image_bounds):
      axes = list(range(1, len(perturbation.get_shape())))
      clipped_perturbation = tf.clip_by_norm(perturbation, epsilon, axes=axes)
      new_image = tf.clip_by_value(input_image + clipped_perturbation,
                                   image_bounds[0], image_bounds[1])
      return new_image - input_image
    return _projection

  else:
    raise ValueError('p must be np.inf or 2.') 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def _rand_noise(noise_mean, noise_dev, scale, shape):
  """Generate random noise given a particular scale and shape."""
  noise_shape = [x // scale for x in shape]
  noise_shape = [1 if x == 0 else x for x in noise_shape]
  noise = tf.random.normal(
      shape=noise_shape, mean=noise_mean, stddev=noise_dev)
  noise = tf.clip_by_value(
      noise, noise_mean - 2.0 * noise_dev, noise_mean + 2.0 * noise_dev)

  if scale != 1:
    noise = tf.image.resize_images(
        noise, [shape[0], shape[1]])
    noise = tf.transpose(noise, [0, 2, 1])
    noise = tf.image.resize_images(
        noise, [shape[0], shape[2]])
    noise = tf.transpose(noise, [0, 2, 1])
  return noise 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def ensure_dataset_eos(dataset, feature_keys=None):
  """Replaces the final token of features with EOS=1 if it is not PAD=0.

  Args:
    dataset: a tf.data.Dataset
    feature_keys: (optional) list of strings, the feature names to ensure end
      with EOS or padding. Defaults to all features.
  Returns:
    a tf.data.Dataset where all specified features end with PAD=0 or EOS=1.
  """
  feature_keys = feature_keys or tf.data.get_output_shapes(dataset).keys()
  def _ensure_eos(k, v):
    if k not in feature_keys:
      return v
    return tf.concat([v[0:-1], tf.clip_by_value(v[-1:], 0, 1)], axis=0)
  return dataset.map(
      lambda ex: {k: _ensure_eos(k, v) for k, v in ex.items()},
      num_parallel_calls=tf.data.experimental.AUTOTUNE) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def safe_cumprod(x, *args, **kwargs):
  """Computes cumprod of x in logspace using cumsum to avoid underflow.

  The cumprod function and its gradient can result in numerical instabilities
  when its argument has very small and/or zero values.  As long as the argument
  is all positive, we can instead compute the cumulative product as
  exp(cumsum(log(x))).  This function can be called identically to tf.cumprod.

  Args:
    x: Tensor to take the cumulative product of.
    *args: Passed on to cumsum; these are identical to those in cumprod.
    **kwargs: Passed on to cumsum; these are identical to those in cumprod.
  Returns:
    Cumulative product of x.
  """
  with tf.name_scope(None, "SafeCumprod", [x]):
    x = tf.convert_to_tensor(x, name="x")
    tiny = np.finfo(x.dtype.as_numpy_dtype).tiny
    return tf.exp(
        tf.cumsum(tf.log(tf.clip_by_value(x, tiny, 1)), *args, **kwargs)) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def ramp(x=None, v_min=0, v_max=1, name=None):
    """The ramp activation function.

    Parameters
    ----------
    x : a tensor input
        input(s)
    v_min : float
        if input(s) smaller than v_min, change inputs to v_min
    v_max : float
        if input(s) greater than v_max, change inputs to v_max
    name : a string or None
        An optional name to attach to this activation function.


    Returns
    --------
    A `Tensor` with the same type as `x`.
    """
    return tf.clip_by_value(x, clip_value_min=v_min, clip_value_max=v_max, name=name) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def loss_fn(self):
        adv = tf.placeholder(tf.float32, [None], name="advantages")
        returns = tf.placeholder(tf.float32, [None], name="returns")
        logli_old = tf.placeholder(tf.float32, [None], name="logli_old")
        value_old = tf.placeholder(tf.float32, [None], name="value_old")

        ratio = tf.exp(self.policy.logli - logli_old)
        clipped_ratio = tf.clip_by_value(ratio, 1-self.clip_ratio, 1+self.clip_ratio)

        value_err = (self.value - returns)**2
        if self.clip_value > 0.0:
            clipped_value = tf.clip_by_value(self.value, value_old-self.clip_value, value_old+self.clip_value)
            clipped_value_err = (clipped_value - returns)**2
            value_err = tf.maximum(value_err, clipped_value_err)

        policy_loss = -tf.reduce_mean(tf.minimum(adv * ratio, adv * clipped_ratio))
        value_loss = tf.reduce_mean(value_err) * self.value_coef
        entropy_loss = tf.reduce_mean(self.policy.entropy) * self.entropy_coef
        # we want to reduce policy and value errors, and maximize entropy
        # but since optimizer is minimizing the signs are opposite
        full_loss = policy_loss + value_loss - entropy_loss

        return full_loss, [policy_loss, value_loss, entropy_loss], [adv, returns, logli_old, value_old] 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 35x35 resnet block."""
  with tf.variable_scope(scope, 'Block35', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
    with tf.variable_scope('Branch_2'):
      tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1')
      tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3')
      tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3')
    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2])
    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                     activation_fn=None, scope='Conv2d_1x1')
    scaled_up = up * scale
    if activation_fn == tf.nn.relu6:
      # Use clip_by_value to simulate bandpass activation.
      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)

    net += scaled_up
    if activation_fn:
      net = activation_fn(net)
  return net 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 17x17 resnet block."""
  with tf.variable_scope(scope, 'Block17', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7],
                                  scope='Conv2d_0b_1x7')
      tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1],
                                  scope='Conv2d_0c_7x1')
    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                     activation_fn=None, scope='Conv2d_1x1')

    scaled_up = up * scale
    if activation_fn == tf.nn.relu6:
      # Use clip_by_value to simulate bandpass activation.
      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)

    net += scaled_up
    if activation_fn:
      net = activation_fn(net)
  return net 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
  """Builds the 8x8 resnet block."""
  with tf.variable_scope(scope, 'Block8', [net], reuse=reuse):
    with tf.variable_scope('Branch_0'):
      tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1')
    with tf.variable_scope('Branch_1'):
      tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1')
      tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3],
                                  scope='Conv2d_0b_1x3')
      tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1],
                                  scope='Conv2d_0c_3x1')
    mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2])
    up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None,
                     activation_fn=None, scope='Conv2d_1x1')

    scaled_up = up * scale
    if activation_fn == tf.nn.relu6:
      # Use clip_by_value to simulate bandpass activation.
      scaled_up = tf.clip_by_value(scaled_up, -6.0, 6.0)

    net += scaled_up
    if activation_fn:
      net = activation_fn(net)
  return net 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def _clip_bbox(min_y, min_x, max_y, max_x):
  """Clip bounding box coordinates between 0 and 1.

  Args:
    min_y: Normalized bbox coordinate of type float between 0 and 1.
    min_x: Normalized bbox coordinate of type float between 0 and 1.
    max_y: Normalized bbox coordinate of type float between 0 and 1.
    max_x: Normalized bbox coordinate of type float between 0 and 1.

  Returns:
    Clipped coordinate values between 0 and 1.
  """
  min_y = tf.clip_by_value(min_y, 0.0, 1.0)
  min_x = tf.clip_by_value(min_x, 0.0, 1.0)
  max_y = tf.clip_by_value(max_y, 0.0, 1.0)
  max_x = tf.clip_by_value(max_x, 0.0, 1.0)
  return min_y, min_x, max_y, max_x 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def feed_forward_gaussian_fun(action_space, config, observations):
  """Feed-forward Gaussian."""
  if not isinstance(action_space, gym.spaces.box.Box):
    raise ValueError("Expecting continuous action space.")

  mean_weights_initializer = tf.initializers.variance_scaling(
      scale=config.init_mean_factor)
  logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)

  flat_observations = tf.reshape(observations, [
      tf.shape(observations)[0], tf.shape(observations)[1],
      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])

  with tf.variable_scope("network_parameters"):
    with tf.variable_scope("policy"):
      x = flat_observations
      for size in config.policy_layers:
        x = tf.layers.dense(x, size, activation=tf.nn.relu)
      mean = tf.layers.dense(
          x, action_space.shape[0], activation=tf.tanh,
          kernel_initializer=mean_weights_initializer)
      logstd = tf.get_variable(
          "logstd", mean.shape[2:], tf.float32, logstd_initializer)
      logstd = tf.tile(
          logstd[None, None],
          [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
    with tf.variable_scope("value"):
      x = flat_observations
      for size in config.value_layers:
        x = tf.layers.dense(x, size, activation=tf.nn.relu)
      value = tf.layers.dense(x, 1)[..., 0]
  mean = tf.check_numerics(mean, "mean")
  logstd = tf.check_numerics(logstd, "logstd")
  value = tf.check_numerics(value, "value")

  policy = tfp.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))

  return NetworkOutput(policy, value, lambda a: tf.clip_by_value(a, -2., 2)) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def postprocess(x, n_bits_x=8):
  """Converts x from [-0.5, 0.5], to [0, 255].

  Args:
    x: 3-D or 4-D Tensor normalized between [-0.5, 0.5]
    n_bits_x: Number of bits representing each pixel of the output.
              Defaults to 8, to default to 256 possible values.
  Returns:
    x: 3-D or 4-D Tensor representing images or videos.
  """
  x = tf.where(tf.is_finite(x), x, tf.ones_like(x))
  x = tf.clip_by_value(x, -0.5, 0.5)
  x += 0.5
  x = x * 2**n_bits_x
  return tf.cast(tf.clip_by_value(x, 0, 255), dtype=tf.uint8) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def visualize_predictions(self, real_frames, gen_frames, actions=None):

    def concat_on_y_axis(x):
      x = tf.unstack(x, axis=1)
      x = tf.concat(x, axis=1)
      return x
    frames_gd = common_video.swap_time_and_batch_axes(real_frames)
    frames_pd = common_video.swap_time_and_batch_axes(gen_frames)
    if actions is not None:
      actions = common_video.swap_time_and_batch_axes(actions)

    if self.is_per_pixel_softmax:
      frames_pd_shape = common_layers.shape_list(frames_pd)
      frames_pd = tf.reshape(frames_pd, [-1, 256])
      frames_pd = tf.to_float(tf.argmax(frames_pd, axis=-1))
      frames_pd = tf.reshape(frames_pd, frames_pd_shape[:-1] + [3])

    frames_gd = concat_on_y_axis(frames_gd)
    frames_pd = concat_on_y_axis(frames_pd)
    if actions is not None:
      actions = tf.clip_by_value(actions, 0, 1)
      summary("action_vid", tf.cast(actions * 255, tf.uint8))
      actions = concat_on_y_axis(actions)
      side_by_side_video = tf.concat([frames_gd, frames_pd, actions], axis=2)
    else:
      side_by_side_video = tf.concat([frames_gd, frames_pd], axis=2)
    tf.summary.image("full_video", side_by_side_video) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import clip_by_value [as 别名]
def vqa_v2_preprocess_image(
    image,
    height,
    width,
    mode,
    resize_side=512,
    distort=True,
    image_model_fn="resnet_v1_152",
):
  """vqa v2 preprocess image."""

  image = tf.image.convert_image_dtype(image, dtype=tf.float32)
  assert resize_side > 0
  if resize_side:
    image = _aspect_preserving_resize(image, resize_side)
  if mode == tf.estimator.ModeKeys.TRAIN:
    image = tf.random_crop(image, [height, width, 3])
  else:
    # Central crop, assuming resize_height > height, resize_width > width.
    image = tf.image.resize_image_with_crop_or_pad(image, height, width)

  image = tf.clip_by_value(image, 0.0, 1.0)

  if mode == tf.estimator.ModeKeys.TRAIN and distort:
    image = _flip(image)
    num_distort_cases = 4
    # pylint: disable=unnecessary-lambda
    image = _apply_with_random_selector(
        image, lambda x, ordering: _distort_color(x, ordering),
        num_cases=num_distort_cases)

  if image_model_fn.startswith("resnet_v1"):
    # resnet_v1 uses vgg preprocessing
    image = image * 255.
    image = _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
  elif image_model_fn.startswith("resnet_v2"):
    # resnet v2 uses inception preprocessing
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)

  return image