Python tensorflow.negative方法代碼示例

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def testGetBackwardOpsSplit(self):
        # a -> b -> c
        #       \-> d
        a = tf.placeholder(tf.float32)
        b = tf.exp(a)
        c = tf.log(b)
        d = tf.negative(b)
        self.assertEqual(get_backward_ops([d]), [a.op, b.op, d.op])
        self.assertEqual(get_backward_ops([c]), [a.op, b.op, c.op])
        self.assertEqual(
            get_backward_ops([c, d]), [a.op, b.op, c.op, d.op])
        self.assertEqual(get_backward_ops([b, d]), [a.op, b.op, d.op])
        self.assertEqual(get_backward_ops([a, d]), [a.op, b.op, d.op])

        self.assertEqual(
            get_backward_ops([c, d], treat_as_inputs=[b]), [c.op, d.op])
        self.assertEqual(
            get_backward_ops([c], treat_as_inputs=[d]), [a.op, b.op, c.op]) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def knn(X_test, X_ref, Y_ref, K = 5):
	

	nearest_neighbors=tf.Variable(tf.zeros([K]))

	distance = tf.negative(tf.reduce_sum(tf.abs(tf.subtract(X_ref, X_test[0])),axis=1)) #L1
	values,indices=tf.nn.top_k(distance,k=K,sorted=False)

	nn = []
	
	for k in range(K):
		nn.append(tf.argmax(Y_ref[indices[k]], 0)) 

	nearest_neighbors=nn
	y, idx, count = tf.unique_with_counts(nearest_neighbors)

	preds = tf.slice(y, begin=[tf.argmax(count, 0)], size=tf.constant([1], dtype=tf.int64))[0]
		
	return preds 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def _train_body(self, states, actions, next_states, rewards, done, weights):
        with tf.device(self.device):
            with tf.GradientTape() as tape:
                if self._enable_categorical_dqn:
                    td_errors = self._compute_td_error_body_distributional(
                        states, actions, next_states, rewards, done)
                    q_func_loss = tf.reduce_mean(
                        huber_loss(tf.negative(td_errors),
                                   delta=self.max_grad) * weights)
                else:
                    td_errors = self._compute_td_error_body(
                        states, actions, next_states, rewards, done)
                    q_func_loss = tf.reduce_mean(
                        huber_loss(td_errors,
                                   delta=self.max_grad) * weights)

            q_func_grad = tape.gradient(
                q_func_loss, self.q_func.trainable_variables)
            self.q_func_optimizer.apply_gradients(
                zip(q_func_grad, self.q_func.trainable_variables))

            return td_errors, q_func_loss 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def node_sequence(sequence, width, stride):
    """Normalizes a given sequence to have a fixed width by striding over the
    sequence. The returned sequence is padded with -1 if its length is lower
    than the requested width.

    Args:
        sequence: A 1d tensor.
        width: The length of the returned sequence.
        stride: The distance between two selected nodes.

    Returns:
        A 1d tensor.
    """

    with tf.name_scope('node_sequence', values=[sequence, width, stride]):
        # Stride the sequence based on the given stride size.
        sequence = tf.strided_slice(sequence, [0], [width*stride], [stride])

        # Pad right with -1 if the sequence length is lower than width.
        padding = tf.ones([width - tf.shape(sequence)[0]], dtype=tf.int32)
        padding = tf.negative(padding)
        sequence = tf.concat(0, [sequence, padding])

    return sequence 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def chk_idx_out_of_bounds_along_axis(cls, data, axis, indices):
    """ Check indices out of bounds for ScatterElement
    In Tensorflow GPU version, if an out of bound index is found,
    the index is ignored for ScatterND/TensorScatterNDUpdate.
    But ONNX spec state that it is an error if any index values
    are out of bounds. Therefore the converter need to run this
    function to verify all the indices are in bounds along the
    axis before send it to Tensoflow. If out of bound is detected
    then the caller of this function need to throw
    InvalidArgumentError exception.
    """
    data_shape = tf.cast(tf_shape(data), indices.dtype)
    limit = data_shape[axis]
    cond1 = tf.greater_equal(indices, tf.negative(limit))
    cond2 = tf.less(indices, limit)
    return tf.logical_and(cond1, cond2) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def chk_pos_in_bounds(cls, input_seq, pos):
    """
    Check the position is in-bounds with respect to the sequence.
    Accepted range for 'position' is in [-n, n - 1], where n is the
    number of tensors in 'input_sequence'.

    :param input_seq: input sequence
    :param pos: position of the output tensor

    :return: True if position is in-bounds 
    """
    seq_length = tf.shape(input_seq.to_sparse(), out_type=pos.dtype)[0]

    cond1 = tf.greater_equal(pos, tf.negative(seq_length))
    cond2 = tf.less_equal(pos, seq_length - 1)

    # pos >= -n and pos < n
    return tf.reduce_all(tf.logical_and(cond1, cond2)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def chk_pos_in_bounds(cls, input_seq, pos):
    """ 
    Check the position is in-bounds with respect to the sequence.
    Accepted range for 'position' is in [-n, n], where n is the 
    number of tensors in 'input_sequence'. 

    :param input_seq: input sequence
    :param pos: position to insert the tensor

    :return: True if position is in-bounds.
    """
    seq_length = tf.shape(input_seq.to_sparse(), out_type=pos.dtype)[0]

    cond1 = tf.greater_equal(pos, tf.negative(seq_length))
    cond2 = tf.less_equal(pos, seq_length)

    # pos >= -n and pos <= n
    return tf.reduce_all(tf.logical_and(cond1, cond2)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def calculate_loss(self, predictions, labels, weights=None, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      if FLAGS.label_smoothing:
        float_labels = smoothing(labels)
      else:
        float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      if weights is not None:
        print cross_entropy_loss, weights
        weighted_loss = tf.einsum("ij,i->ij", cross_entropy_loss, weights)
        print "create weighted_loss", weighted_loss
        return tf.reduce_mean(tf.reduce_sum(weighted_loss, 1))
      else:
        return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def calculate_loss_distill_boost(self, predictions, labels_distill, labels, **unused_params):
    with tf.name_scope("loss_distill_boost"):
      print("loss_distill_boost")
      epsilon = 10e-6
      float_labels = tf.cast(labels, tf.float32)
      batch_size = tf.shape(float_labels)[0]
      float_labels_distill = tf.cast(labels_distill, tf.float32)
      error = tf.negative(float_labels * tf.log(float_labels_distill + epsilon) + (
          1 - float_labels) * tf.log(1 - float_labels_distill + epsilon))
      error = tf.reduce_sum(error,axis=1,keep_dims=True)
      alpha = error / tf.reduce_sum(error) * tf.cast(batch_size,dtype=tf.float32)
      alpha = tf.clip_by_value(alpha, 0.5, 5)
      alpha = alpha / tf.reduce_sum(alpha) * tf.cast(batch_size,dtype=tf.float32)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss * alpha)

      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def calculate_loss_distill_relabel(self, predictions, labels_distill, labels, **unused_params):
    with tf.name_scope("loss_distill_relabel"):
      print("loss_distill_relabel")
      epsilon = 10e-6
      float_labels = tf.cast(labels, tf.float32)
      sum_labels = tf.cast(tf.reduce_sum(float_labels),dtype=tf.int32)
      pos_distill, _ = tf.nn.top_k(tf.reshape(labels_distill,[-1]), k=sum_labels)
      labels_true = tf.ones(tf.shape(labels))
      labels_false = tf.zeros(tf.shape(labels))
      labels_add = tf.where(tf.greater_equal(labels_distill, pos_distill[-1]), labels_true, labels_false)
      print(labels_add.get_shape().as_list())
      float_labels = float_labels+labels_add*(1.0-float_labels)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss)

      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      vocab_size = predictions.get_shape().as_list()[1]
      float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + (
          1 - float_labels) * tf.log(1 - predictions + epsilon)
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      neg_labels = 1 - float_labels
      predictions_pos = predictions*float_labels+10*neg_labels
      predictions_minpos = tf.reduce_min(predictions_pos,axis=1,keep_dims=True)
      predictions_neg = predictions*neg_labels-10*float_labels
      predictions_maxneg = tf.reduce_max(predictions_neg,axis=1,keep_dims=True)
      mask_1 = tf.cast(tf.greater_equal(predictions_neg, predictions_minpos),dtype=tf.float32)
      mask_2 = tf.cast(tf.less_equal(predictions_pos, predictions_maxneg),dtype=tf.float32)
      cross_entropy_loss = cross_entropy_loss*(mask_1+mask_2)*10 + cross_entropy_loss
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def calculate_loss(self, predictions, labels, **unused_params):
        bound = FLAGS.softmax_bound
        vocab_size_1 = bound
        with tf.name_scope("loss_softmax"):
            epsilon = 10e-8
            float_labels = tf.cast(labels, tf.float32)
            labels_1 = float_labels[:,:vocab_size_1]
            predictions_1 = predictions[:,:vocab_size_1]
            cross_entropy_loss = CrossEntropyLoss().calculate_loss(predictions_1,labels_1)
            lables_2 = float_labels[:,vocab_size_1:]
            predictions_2 = predictions[:,vocab_size_1:]
            # l1 normalization (labels are no less than 0)
            label_rowsum = tf.maximum(
                tf.reduce_sum(lables_2, 1, keep_dims=True),
                epsilon)
            label_append = 1.0-tf.reduce_max(lables_2, 1, keep_dims=True)
            norm_float_labels = tf.concat((tf.div(lables_2, label_rowsum),label_append),axis=1)
            predictions_append = 1.0-tf.reduce_sum(predictions_2, 1, keep_dims=True)
            softmax_outputs = tf.concat((predictions_2,predictions_append),axis=1)
            softmax_loss = norm_float_labels * tf.log(softmax_outputs + epsilon) + (
                                                                                       1 - norm_float_labels) * tf.log(1 - softmax_outputs + epsilon)
            softmax_loss = tf.negative(tf.reduce_sum(softmax_loss, 1))
        return tf.reduce_mean(softmax_loss) + cross_entropy_loss 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def _inputs_check(self, scores_pos, scores_neg):
        """Creates any dependencies that need to be checked before performing loss computations

        Parameters
        ----------
        scores_pos : tf.Tensor
            A tensor of scores assigned to positive statements.
        scores_neg : tf.Tensor
            A tensor of scores assigned to negative statements.
        """
        logger.debug('Creating dependencies before loss computations.')
        self._dependencies = []
        if LOSS_REGISTRY[self.name].class_params['require_same_size_pos_neg'] and self._loss_parameters['eta'] != 1:
            logger.debug('Dependencies found: \n\tRequired same size positive and negative. \n\tEta is not 1.')
            self._dependencies.append(tf.Assert(tf.equal(tf.shape(scores_pos)[0], tf.shape(scores_neg)[0]),
                                                [tf.shape(scores_pos)[0], tf.shape(scores_neg)[0]])) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def _apply(self, scores_pos, scores_neg):
        """Apply the loss function. Every inherited class must implement this function.
        (All the TF code must go in this function.)

        Parameters
        ----------
        scores_pos : tf.Tensor
            A tensor of scores assigned to positive statements.
        scores_neg : tf.Tensor
            A tensor of scores assigned to negative statements.

        Returns
        -------
        loss : tf.Tensor
            The loss value that must be minimized.
        """
        msg = 'This function is a placeholder in an abstract class.'
        logger.error(msg)
        NotImplementedError(msg) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import negative [as 別名]
def apply(self, scores_pos, scores_neg):
        """Interface to external world.
        This function does the input checks, preprocesses input and finally applies loss function.

        Parameters
        ----------
        scores_pos : tf.Tensor
            A tensor of scores assigned to positive statements.
        scores_neg : tf.Tensor
            A tensor of scores assigned to negative statements.

        Returns
        -------
        loss : tf.Tensor
            The loss value that must be minimized.
        """
        self._inputs_check(scores_pos, scores_neg)
        with tf.control_dependencies(self._dependencies):
            loss = self._apply(scores_pos, scores_neg)
        return loss