tf.math.reduce

Computes the sum of elements across dimensions of a tensor.

View aliases

Main aliases

tf.reduce_sum

tf.math.reduce_sum(
    input_tensor, axis=None, keepdims=False, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Advanced Automatic Differentiation Training and evaluation with the built-in methods Introduction to gradients and automatic differentiation tf.data: Build TensorFlow input pipelines Making new Layers and Models via subclassing	Transformer model for language understanding Convolutional Variational Autoencoder Neural style transfer Playing CartPole with the Actor-Critic Method Customization basics: tensors and operations

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis, which must be unique. If keepdims is true, the reduced dimensions are retained with length 1.

If axis is None, all dimensions are reduced, and a tensor with a single element is returned.

For example:

# x has a shape of (2, 3) (two rows and three columns):
x = tf.constant([[1, 1, 1], [1, 1, 1]])
x.numpy()
array([[1, 1, 1],
       [1, 1, 1]], dtype=int32)
# sum all the elements
# 1 + 1 + 1 + 1 + 1+ 1 = 6
tf.reduce_sum(x).numpy()
6
# reduce along the first dimension
# the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
# reduce along the second dimension
# the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
# keep the original dimensions
tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
       [3]], dtype=int32)
# reduce along both dimensions
# the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
# or, equivalently, reduce along rows, then reduce the resultant array
# [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
# 2 + 2 + 2 = 6
tf.reduce_sum(x, [0, 1]).numpy()
6

Args
`input_tensor`	The tensor to reduce. Should have numeric type.
`axis`	The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor)]`.
`keepdims`	If true, retains reduced dimensions with length 1.
`name`	A name for the operation (optional).

Returns
The reduced tensor, of the same dtype as the input_tensor.

Numpy Compatibility

Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to int64 while tensorflow returns the same dtype as the input.

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View aliases

Used in the notebooks

For example:

Args

Returns

Numpy Compatibility