Python tensorflow.segment_max方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def testGradient(self):
    shape = [4, 4]
    indices = [0, 1, 2, 2]
    for tf_op in [tf.segment_sum,
                  tf.segment_mean,
                  tf.segment_min,
                  tf.segment_max]:
      with self.test_session():
        tf_x, np_x = self._input(shape, dtype=tf.float64)
        s = tf_op(data=tf_x, segment_ids=indices)
        jacob_t, jacob_n = tf.test.compute_gradient(
            tf_x,
            shape,
            s,
            [3, 4],
            x_init_value=np_x.astype(np.double),
            delta=1)
      self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def padded_segment_reduce(vecs, segment_inds, num_segments, reduction_mode):
    """
    Reduce the vecs with segment_inds and reduction_mode
    Input:
        vecs: A Tensor of shape (batch_size, vec_dim)
        segment_inds: A Tensor containing the segment index of each
        vec row, should agree with vecs in shape[0]
    Output:
        A tensor of shape (vec_dim)
    """
    if reduction_mode == 'max':
        print('USING MAX POOLING FOR REDUCTION!')
        vecs_reduced = tf.segment_max(vecs, segment_inds)
    elif reduction_mode == 'mean':
        print('USING AVG POOLING FOR REDUCTION!')
        vecs_reduced = tf.segment_mean(vecs, segment_inds)
    vecs_reduced.set_shape([num_segments, vecs.get_shape()[1]])
    return vecs_reduced 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def test_SegmentMax(self):
        t = tf.segment_max(self.random(4, 2, 3), np.array([0, 1, 1, 2]))
        self.check(t) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def testValues(self):
    dtypes = [tf.float32,
              tf.float64,
              tf.int64,
              tf.int32,
              tf.complex64,
              tf.complex128]

    # Each item is np_op1, np_op2, tf_op
    ops_list = [(np.add, None, tf.segment_sum),
                (self._mean_cum_op, self._mean_reduce_op,
                 tf.segment_mean),
                (np.ndarray.__mul__, None, tf.segment_prod),
                (np.minimum, None, tf.segment_min),
                (np.maximum, None, tf.segment_max)]

    # A subset of ops has been enabled for complex numbers
    complex_ops_list = [(np.add, None, tf.segment_sum),
                        (np.ndarray.__mul__, None, tf.segment_prod)]

    n = 10
    shape = [n, 2]
    indices = [i // 3 for i in range(n)]
    for dtype in dtypes:
      if dtype in (tf.complex64, tf.complex128):
        curr_ops_list = complex_ops_list
      else:
        curr_ops_list = ops_list

      with self.test_session(use_gpu=False):
        tf_x, np_x = self._input(shape, dtype=dtype)
        for np_op1, np_op2, tf_op in curr_ops_list:
          np_ans = self._segmentReduce(indices, np_x, np_op1, np_op2)
          s = tf_op(data=tf_x, segment_ids=indices)
          tf_ans = s.eval()
          self._assertAllClose(indices, np_ans, tf_ans)
          # NOTE(mrry): The static shape inference that computes
          # `tf_ans.shape` can only infer that sizes from dimension 1
          # onwards, because the size of dimension 0 is data-dependent
          # and may therefore vary dynamically.
          self.assertAllEqual(np_ans.shape[1:], tf_ans.shape[1:]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def testSegmentMaxGradient(self):
    data = tf.constant([1.0, 2.0, 3.0], dtype=tf.float32)
    segment_ids = tf.constant([0, 0, 1], dtype=tf.int64)
    segment_max = tf.segment_max(data, segment_ids)
    with self.test_session():
      error = tf.test.compute_gradient_error(data, [3], segment_max, [2])
      self.assertLess(error, 1e-4) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def testSegmentMaxGradientWithTies(self):
    inputs = tf.constant([1.0], dtype=tf.float32)
    data = tf.concat(0, [inputs, inputs])
    segment_ids = tf.constant([0, 0], dtype=tf.int64)
    segment_max = tf.segment_max(data, segment_ids)
    with self.test_session():
      error = tf.test.compute_gradient_error(inputs, [1], segment_max, [1])
      self.assertLess(error, 1e-4) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import segment_max [as 别名]
def segment_logsumexp(xs, segments):
    """ Similar tf.segment_sum but compute logsumexp rather then sum """
    # Stop gradients following the implementation of tf.reduce_logsumexp
    maxs = tf.stop_gradient(tf.reduce_max(xs, axis=1))
    segment_maxes = tf.segment_max(maxs, segments)
    xs -= tf.expand_dims(tf.gather(segment_maxes, segments), 1)
    sums = tf.reduce_sum(tf.exp(xs), axis=1)
    return tf.log(tf.segment_sum(sums, segments)) + segment_maxes