Removes dimensions of size 1 from the shape of a tensor.

This operation preserves the structure of the input tensors, removing the first dimension of each tensor and using it as the dataset dimension. All input ...

as a tensor shape, where size[i] is the number of elements of the 'i'th dimension of input_ that you want to slice.

To instead reorder the data to rearrange the dimensions of a tensor, see tf.transpose . t = [[1, 2, 3], [4, 5, 6]] tf.reshape(t, [3, ...

Please use one of the following methods to construct a RaggedTensor :.

Takes a list indicating the number of elements in each dimension. For example, dense_shape=[3,6] specifies a two-dimensional 3x6 tensor, dense_shape=[2,3,4] ...

In some cases, the inferred shape may have unknown dimensions.

In general, 0 < dim(mask) = K <= dim(tensor) , and mask 's shape must match the first K dimensions of tensor 's shape.

SparseTensor would use to represent the condition tensor: sparse = tf.sparse.from_dense(float_tensor) sparse.indices.numpy() array([[0, 0, 0], [0, 1, 1],

Computes tf.math.maximum of elements across dimensions of a tensor.