tf.reduce_sum( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None )
See the guide: Upgrade to TensorFlow 1.0 > Upgrading your code manually
Computes the sum of elements across dimensions of a tensor. (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead
input_tensor along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each
keepdims is true, the reduced dimensions
are retained with length 1.
axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
x = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x) # 6 tf.reduce_sum(x, 0) # [2, 2, 2] tf.reduce_sum(x, 1) # [3, 3] tf.reduce_sum(x, 1, keepdims=True) # [, ] tf.reduce_sum(x, [0, 1]) # 6
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
keepdims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.
keep_dims: Deprecated alias for
The reduced tensor, of the same dtype as the input_tensor.
Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to int64 while tensorflow returns the same dtype as the input.