Python tensorflow.divide方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def get_loss(predicted_transformation, batch_size, template_pointclouds_pl, source_pointclouds_pl):
	with tf.variable_scope('loss') as LossEvaluation:
		predicted_position = tf.slice(predicted_transformation,[0,0],[batch_size,3])
		predicted_quat = tf.slice(predicted_transformation,[0,3],[batch_size,4])

		# with tf.variable_scope('quat_normalization') as norm:
		norm_predicted_quat = tf.reduce_sum(tf.square(predicted_quat),1)
		norm_predicted_quat = tf.sqrt(norm_predicted_quat)
		norm_predicted_quat = tf.reshape(norm_predicted_quat,(batch_size,1))
		const = tf.constant(0.0000001,shape=(batch_size,1),dtype=tf.float32)
		norm_predicted_quat = tf.add(norm_predicted_quat,const)
		predicted_norm_quat = tf.divide(predicted_quat,norm_predicted_quat)

		transformed_predicted_point_cloud = helper.transformation_quat_tensor(source_pointclouds_pl, predicted_norm_quat,predicted_position)

		#loss = tf_util_loss.earth_mover(template_pointclouds_pl, transformed_predicted_point_cloud)
		loss = tf_util_loss.chamfer(template_pointclouds_pl, transformed_predicted_point_cloud)
	return loss 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def get_loss_b(self,predicted_transformation,batch_size,template_pointclouds_pl,source_pointclouds_pl):	
		with tf.variable_scope('loss') as LossEvaluation:
			predicted_position = tf.slice(predicted_transformation,[0,0],[batch_size,3])
			predicted_quat = tf.slice(predicted_transformation,[0,3],[batch_size,4])

			# with tf.variable_scope('quat_normalization') as norm:
			norm_predicted_quat = tf.reduce_sum(tf.square(predicted_quat),1)
			norm_predicted_quat = tf.sqrt(norm_predicted_quat)
			norm_predicted_quat = tf.reshape(norm_predicted_quat,(batch_size,1))
			const = tf.constant(0.0000001,shape=(batch_size,1),dtype=tf.float32)
			norm_predicted_quat = tf.add(norm_predicted_quat,const)
			predicted_norm_quat = tf.divide(predicted_quat,norm_predicted_quat)
	
			transformed_predicted_point_cloud = helper.transformation_quat_tensor(source_pointclouds_pl, predicted_norm_quat, predicted_position)

			# Use 1024 Points to find loss.
			#loss = tf_util_loss.earth_mover(template_pointclouds_pl, transformed_predicted_point_cloud)
			loss = tf_util_loss.chamfer(template_pointclouds_pl, transformed_predicted_point_cloud)
			# loss = 0
		return loss 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
    """Compute loss function.

    Args:
      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing the predicted logits for each class
      target_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing one-hot encoded classification targets
      weights: a float tensor of shape, either [batch_size, num_anchors,
        num_classes] or [batch_size, num_anchors, 1]. If the shape is
        [batch_size, num_anchors, 1], all the classses are equally weighted.

    Returns:
      loss: a float tensor of shape [batch_size, num_anchors]
        representing the value of the loss function.
    """
    weights = tf.reduce_mean(weights, axis=2)
    num_classes = prediction_tensor.get_shape().as_list()[-1]
    prediction_tensor = tf.divide(
        prediction_tensor, self._logit_scale, name='scale_logit')
    per_row_cross_ent = (tf.nn.softmax_cross_entropy_with_logits(
        labels=tf.reshape(target_tensor, [-1, num_classes]),
        logits=tf.reshape(prediction_tensor, [-1, num_classes])))
    return tf.reshape(per_row_cross_ent, tf.shape(weights)) * weights 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def convert_class_logits_to_softmax(multiclass_scores, temperature=1.0):
  """Converts multiclass logits to softmax scores after applying temperature.

  Args:
    multiclass_scores: float32 tensor of shape
      [num_instances, num_classes] representing the score for each box for each
      class.
    temperature: Scale factor to use prior to applying softmax. Larger
      temperatures give more uniform distruibutions after softmax.

  Returns:
    multiclass_scores: float32 tensor of shape
      [num_instances, num_classes] with scaling and softmax applied.
  """

  # Multiclass scores must be stored as logits. Apply temp and softmax.
  multiclass_scores_scaled = tf.divide(
      multiclass_scores, temperature, name='scale_logits')
  multiclass_scores = tf.nn.softmax(multiclass_scores_scaled, name='softmax')

  return multiclass_scores 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def read_tensor_from_image_file(frames, input_height=299, input_width=299, input_mean=0, input_std=255):
    input_name = "file_reader"
    frames = [(tf.read_file(frame, input_name), frame) for frame in frames]
    decoded_frames = []
    for frame in frames:
        file_name = frame[1]
        file_reader = frame[0]
        if file_name.endswith(".png"):
            image_reader = tf.image.decode_png(file_reader, channels=3, name="png_reader")
        elif file_name.endswith(".gif"):
            image_reader = tf.squeeze(tf.image.decode_gif(file_reader, name="gif_reader"))
        elif file_name.endswith(".bmp"):
            image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
        else:
            image_reader = tf.image.decode_jpeg(file_reader, channels=3, name="jpeg_reader")
        decoded_frames.append(image_reader)
    float_caster = [tf.cast(image_reader, tf.float32) for image_reader in decoded_frames]
    float_caster = tf.stack(float_caster)
    resized = tf.image.resize_bilinear(float_caster, [input_height, input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
    return result 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def calculate_model_precision(input_tensor, label_tensor):
    """
    calculate accuracy acc = correct_nums / ground_truth_nums
    :param input_tensor: binary segmentation logits
    :param label_tensor: binary segmentation label
    :return:
    """

    logits = tf.nn.softmax(logits=input_tensor)
    final_output = tf.expand_dims(tf.argmax(logits, axis=-1), axis=-1)

    idx = tf.where(tf.equal(final_output, 1))
    pix_cls_ret = tf.gather_nd(label_tensor, idx)
    accuracy = tf.count_nonzero(pix_cls_ret)
    accuracy = tf.divide(
        accuracy,
        tf.cast(tf.shape(tf.gather_nd(label_tensor, tf.where(tf.equal(label_tensor, 1))))[0], tf.int64))

    return accuracy 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def calculate_model_fp(input_tensor, label_tensor):
    """
    calculate fp figure
    :param input_tensor:
    :param label_tensor:
    :return:
    """
    logits = tf.nn.softmax(logits=input_tensor)
    final_output = tf.expand_dims(tf.argmax(logits, axis=-1), axis=-1)

    idx = tf.where(tf.equal(final_output, 1))
    pix_cls_ret = tf.gather_nd(final_output, idx)
    false_pred = tf.cast(tf.shape(pix_cls_ret)[0], tf.int64) - tf.count_nonzero(
        tf.gather_nd(label_tensor, idx)
    )

    return tf.divide(false_pred, tf.cast(tf.shape(pix_cls_ret)[0], tf.int64)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def calculate_model_fn(input_tensor, label_tensor):
    """
    calculate fn figure
    :param input_tensor:
    :param label_tensor:
    :return:
    """
    logits = tf.nn.softmax(logits=input_tensor)
    final_output = tf.expand_dims(tf.argmax(logits, axis=-1), axis=-1)

    idx = tf.where(tf.equal(label_tensor, 1))
    pix_cls_ret = tf.gather_nd(final_output, idx)
    label_cls_ret = tf.gather_nd(label_tensor, tf.where(tf.equal(label_tensor, 1)))
    mis_pred = tf.cast(tf.shape(label_cls_ret)[0], tf.int64) - tf.count_nonzero(pix_cls_ret)

    return tf.divide(mis_pred, tf.cast(tf.shape(label_cls_ret)[0], tf.int64)) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def divide(x, y, safe_mode=True, epsilon=None, name=None):
    """ A wrapper of `tf.divide`, computes Python style division of x by y but extends safe divide support.
        If safe_mode is `True` or epsilon is given(a small float number), the absolute value of denominator
        in the division will be clip to make sure it's bigger than epsilon(default is 1e-13).

    Args:
        safe_mode: Use safe divide mode.
        epsilon: Float number. Default is `1e-13`.
    """
    if not safe_mode and epsilon is None:
        return tf.divide(x, y, name=name)
    else:
        epsilon = 1e-20 if epsilon is None else epsilon
        name = "safe_divide" if name is None else name
        with tf.name_scope(name):
            y = tf.where(tf.greater(tf.abs(y), epsilon), y, y + tf.sign(y) * epsilon)
            return tf.divide(x, y) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def result(self):
    true_mean = tf.divide(self._true_sum, self._count)
    true_mean2 = tf.math.square(true_mean)
    pred_mean = tf.divide(self._pred_sum, self._count)
    pred_mean2 = tf.math.square(pred_mean)

    term1 = self._product
    term2 = -tf.multiply(true_mean, self._pred_sum)
    term3 = -tf.multiply(pred_mean, self._true_sum)
    term4 = tf.multiply(self._count, tf.multiply(true_mean, pred_mean))
    covariance = term1 + term2 + term3 + term4

    true_var = self._true_sumsq - tf.multiply(self._count, true_mean2)
    pred_var = self._pred_sumsq - tf.multiply(self._count, pred_mean2)
    tp_var = tf.multiply(tf.math.sqrt(true_var), tf.math.sqrt(pred_var))
    correlation = tf.divide(covariance, tp_var)

    if self._summarize:
        return tf.reduce_mean(correlation)
    else:
        return correlation 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def __init__(self, distribution_spec, average_time_steps=False, scope="negative-log-likelihood-loss", **kwargs):
        """
        Args:
            average_time_steps (bool): Whether, if a time rank is given, to divide by th esequence lengths to get
                the mean or not (leave as sum).
        """
        super(NegativeLogLikelihoodLoss, self).__init__(scope=scope, **kwargs)

        self.distribution = Distribution.from_spec(distribution_spec)
        self.average_time_steps = average_time_steps

        self.add_components(self.distribution)

        #self.reduce_ranks = None

        self.time_rank = None
        self.time_major = None 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def __init__(self, sparse=True, with_kl_regularizer=True, average_time_steps=False, scope="cross-entropy-loss",
                 **kwargs):
        """
        Args:
            sparse (bool): Whether we have sparse labels. Sparse labels can only assign one category to each
                sample, so labels are ints. If False, labels are already softmaxed categorical distribution probs
                OR simple logits.

            average_time_steps (bool): Whether, if a time rank is given, to divide by th esequence lengths to get
                the mean or not (leave as sum).
        """
        super(CategoricalCrossEntropyLoss, self).__init__(scope=scope, **kwargs)

        self.sparse = sparse
        self.with_kl_regularizer = with_kl_regularizer
        self.average_time_steps = average_time_steps
        #self.reduce_ranks = None

        #self.time_rank = None
        #self.time_major = None

        #self.is_bool = None 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
    """Compute loss function.

    Args:
      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing the predicted logits for each class
      target_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing one-hot encoded classification targets
      weights: a float tensor of shape [batch_size, num_anchors]

    Returns:
      loss: a float tensor of shape [batch_size, num_anchors]
        representing the value of the loss function.
    """
    num_classes = prediction_tensor.get_shape().as_list()[-1]
    prediction_tensor = tf.divide(
        prediction_tensor, self._logit_scale, name='scale_logit')
    per_row_cross_ent = (tf.nn.softmax_cross_entropy_with_logits(
        labels=tf.reshape(target_tensor, [-1, num_classes]),
        logits=tf.reshape(prediction_tensor, [-1, num_classes])))
    return tf.reshape(per_row_cross_ent, tf.shape(weights)) * weights 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def version_10(cls, node, **kwargs):
    tensor_dict = kwargs["tensor_dict"]
    x = tensor_dict[node.inputs[0]]
    y_scale = tensor_dict[node.inputs[1]]

    x = tf.cast(x, tf.float32)
    y = tf.divide(x, y_scale)
    y = tf.round(y)
    if len(node.inputs) == 3:
      y_zero_point = tensor_dict[node.inputs[2]]
      y_dtype = y_zero_point.dtype
      y_zero_point = tf.cast(y_zero_point, tf.float32)
      y = tf.add(y, y_zero_point)
    else:  # y_zero_point default dtype = uint8
      y_dtype = tf.uint8

    y = tf.saturate_cast(y, y_dtype)

    return [y] 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import divide [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
    """Compute loss function.

    Args:
      prediction_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing the predicted logits for each class
      target_tensor: A float tensor of shape [batch_size, num_anchors,
        num_classes] representing one-hot encoded classification targets
      weights: a float tensor of shape [batch_size, num_anchors]

    Returns:
      loss: a (scalar) tensor representing the value of the loss function
    """
    num_classes = prediction_tensor.get_shape().as_list()[-1]
    prediction_tensor = tf.divide(
        prediction_tensor, self._logit_scale, name='scale_logit')
    per_row_cross_ent = (tf.nn.softmax_cross_entropy_with_logits(
        labels=tf.reshape(target_tensor, [-1, num_classes]),
        logits=tf.reshape(prediction_tensor, [-1, num_classes])))
    if self._anchorwise_output:
      return tf.reshape(per_row_cross_ent, tf.shape(weights)) * weights
    return tf.reduce_sum(per_row_cross_ent * tf.reshape(weights, [-1]))