python类negative()的实例源码

blocks_test.py 文件源码 项目:fold 作者: tensorflow 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_one_of(self):
    block = tdb.OneOf(lambda x: x > 0,
                      {True: tdb.Scalar(),
                       False: tdb.Scalar() >> tdb.Function(tf.negative)})
    self.assertBuildsConst(3., block, 3)
    self.assertBuildsConst(3., block, -3)
blocks_test.py 文件源码 项目:fold 作者: tensorflow 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def test_one_of_mixed_input_type(self):
    block = (tdb.Identity(), tdb.Scalar('int32')) >> tdb.OneOf(
        key_fn=tdb.GetItem(0),
        case_blocks=(tdb.Function(tf.square), tdb.Function(tf.negative)),
        pre_block=tdb.GetItem(1))
    self.assertBuilds(4, block, (0, 2))
    self.assertBuilds(-2, block, (1, 2))
blocks_test.py 文件源码 项目:fold 作者: tensorflow 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_optional_default_none_type_inference(self):
    child = tdb.Scalar() >> tdb.Function(tf.negative)
    block = tdb.Optional(child)
    self.assertEqual(child.output_type, None)
    child.set_output_type([])
    self.assertEqual(block.output_type, tdt.TensorType([]))
losses.py 文件源码 项目:mlc2017-online 作者: machine-learning-challenge 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
losses.py 文件源码 项目:mlc2017-online 作者: machine-learning-challenge 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)
losses.py 文件源码 项目:youtube-8m 作者: google 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
losses.py 文件源码 项目:youtube-8m 作者: google 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)
losses.py 文件源码 项目:Video-Classification 作者: boyaolin 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
losses.py 文件源码 项目:Video-Classification 作者: boyaolin 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)
utils.py 文件源码 项目:zhusuan 作者: thu-ml 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def __neg__(self):
        return tf.negative(self)
losses.py 文件源码 项目:Youtube-8M-WILLOW 作者: antoine77340 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      alpha = FLAGS.alpha

      float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = 2*(alpha*float_labels * tf.log(predictions + epsilon) + (1-alpha)*(
          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))
fwgrad.py 文件源码 项目:tensorflow-forward-ad 作者: renmengye 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def Neg_FwGrad(op, dx, _op_table=None, _grad_table=None):
  if dx is None:
    return None
  return tf.negative(dx)
fwgrad_tests.py 文件源码 项目:tensorflow-forward-ad 作者: renmengye 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def test_basic(self):
    with tf.Graph().as_default(), self.test_session() as sess:
      rnd = np.random.RandomState(0)
      x = self.get_random_tensor([18, 12], rnd=rnd)
      y = tf.negative(x)
      self.assert_bw_fw(sess, x, y, rnd=rnd)
losses.py 文件源码 项目:Y8M 作者: mpekalski 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
losses.py 文件源码 项目:Y8M 作者: mpekalski 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)
losses.py 文件源码 项目:Y8M 作者: mpekalski 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, epsilon, wgts, **unused_params):
    with tf.name_scope("loss_xent"):
      #epsilon = 10e-8
      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 * wgts)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
losses.py 文件源码 项目:Y8M 作者: mpekalski 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_softmax"):
      epsilon = 10e-8
      float_labels = tf.cast(labels, tf.float32)
      # l1 normalization (labels are no less than 0)
      label_rowsum = tf.maximum(
          tf.reduce_sum(float_labels, 1, keep_dims=True),
          epsilon)
      norm_float_labels = tf.div(float_labels, label_rowsum)
      softmax_outputs = tf.nn.softmax(predictions)
      softmax_loss = tf.negative(tf.reduce_sum(
          tf.multiply(norm_float_labels, tf.log(softmax_outputs)), 1))
    return tf.reduce_mean(softmax_loss)
losses.py 文件源码 项目:Y8M 作者: mpekalski 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def calculate_loss(self, predictions, labels, epsilon, **unused_params):
    with tf.name_scope("loss_xent"):
      #epsilon = 10e-8
      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)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
tf_glove.py 文件源码 项目:FYP-AutoTextSum 作者: MrRexZ 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def _window(region, start_index, end_index):
    """
    Returns the list of words starting from `start_index`, going to `end_index`
    taken from region. If `start_index` is a negative number, or if `end_index`
    is greater than the index of the last word in region, this function will pad
    its return value with `NULL_WORD`.
    """
    last_index = len(region) + 1
    selected_tokens = region[max(start_index, 0):min(end_index, last_index) + 1]
    return selected_tokens
cwise_ops_cplx_test.py 文件源码 项目:complex_tf 作者: woodshop 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def testCplxNegGPU(self):
        shapes = [(5,4,3), (5,4), (5,), (1,)]
        for sh in shapes:
            x = ((np.random.randn(*sh) +
                  1j*np.random.randn(*sh)).astype(np.complex64))
            self._compareGpu(x, np.negative, tf.negative)


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