To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Heritage persistent IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.
import copy from typing import List, Union, Optional import tensorflow as tf def eye(num: int, value: tf.Tensor, dtype: Optional[tf.DType] = None) -> tf.Tensor: if dtype is not None: value = tf.cast(value, dtype) return tf.linalg.diag(tf.fill([num], value)) def leading_transpose(tensor: tf.Tensor, perm: List[Union[int, type(...)]], leading_dim: int = 0) -> tf.Tensor: """ Transposes tensors with leading dimensions. Leading dimensions in permutation list represented via ellipsis `...`. When leading dimensions are found, `transpose` method considers them as a single grouped element indexed by 0 in `perm` list. So, passing `perm=[-2, ..., -1]`, you assume that your input tensor has [..., A, B] shape, and you want to move leading dims between A and B dimensions. Dimension indices in permutation list can be negative or positive. Valid positive indices start from 1 up to the tensor rank, viewing leading dimensions `...` as zero index. Example: a = tf.random.normal((1, 2, 3, 4, 5, 6)) # [..., A, B, C], # where A is 1st element, # B is 2nd element and # C is 3rd element in # permutation list, # leading dimentions are [1, 2, 3] # which are 0th element in permutation # list b = leading_transpose(a, [3, -3, ..., -2]) # [C, A, ..., B] sess.run(b).shape output> (6, 4, 1, 2, 3, 5) :param tensor: TensorFlow tensor. :param perm: List of permutation indices. :returns: TensorFlow tensor. :raises: ValueError when `...` cannot be found. """ perm = copy.copy(perm) idx = perm.index(...) perm[idx] = leading_dim rank = tf.rank(tensor) perm_tf = perm % rank leading_dims = tf.range(rank - len(perm) + 1) perm = tf.concat([perm_tf[:idx], leading_dims, perm_tf[idx + 1:]], 0) return tf.transpose(tensor, perm) def broadcasting_elementwise(op, a, b): """ Apply binary operation `op` to every pair in tensors `a` and `b`. :param op: binary operator on tensors, e.g. tf.add, tf.substract :param a: tf.Tensor, shape [n_1, ..., n_a] :param b: tf.Tensor, shape [m_1, ..., m_b] :return: tf.Tensor, shape [n_1, ..., n_a, m_1, ..., m_b] """ flatres = op(tf.reshape(a, [-1, 1]), tf.reshape(b, [1, -1])) return tf.reshape(flatres, tf.concat([tf.shape(a), tf.shape(b)], 0)) def square_distance(X, X2): """ Returns (X - X2ᵀ)² Due to the implementation and floating-point imprecision, the result may actually be very slightly negative for entries very close to each other. """ if X2 is None: Xs = tf.reduce_sum(tf.square(X), axis=-1, keepdims=True) dist = -2 * tf.matmul(X, X, transpose_b=True) dist += Xs + tf.linalg.adjoint(Xs) return dist Xs = tf.reduce_sum(tf.square(X), axis=-1) X2s = tf.reduce_sum(tf.square(X2), axis=-1) dist = -2 * tf.tensordot(X, X2, [[-1], [-1]]) dist += broadcasting_elementwise(tf.add, Xs, X2s) return dist