|TensorFlow 1 version||View source on GitHub|
Computes the sum of elements across dimensions of a tensor.
See Migration guide for more details.
tf.math.reduce_sum( input_tensor, axis=None, keepdims=False, name=None )
Used in the guide:
- Train and evaluate with Keras
- tf.data: Build TensorFlow input pipelines
- Writing custom layers and models with Keras
- Distributed training with TensorFlow
- Eager execution
Used in the tutorials:
- Automatic differentiation and gradient tape
- Better performance with tf.function
- Convolutional Variational Autoencoder
- Neural style transfer
- Customization basics: tensors and operations
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).
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.