tf.compat / v1 / v1.trainable_variables

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Transposes a.

tf.compat.v1.transpose(
    a, perm=None, name='transpose', conjugate=False
)

Permutes the dimensions according to perm.

The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors. If conjugate is True and a.dtype is either complex64 or complex128 then the values of a are conjugated and transposed.

For example:

x = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.transpose(x)  # [[1, 4]
                 #  [2, 5]
                 #  [3, 6]]

# Equivalently
tf.transpose(x, perm=[1, 0])  # [[1, 4]
                              #  [2, 5]
                              #  [3, 6]]

# If x is complex, setting conjugate=True gives the conjugate transpose
x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
                 [4 + 4j, 5 + 5j, 6 + 6j]])
tf.transpose(x, conjugate=True)  # [[1 - 1j, 4 - 4j],
                                 #  [2 - 2j, 5 - 5j],
                                 #  [3 - 3j, 6 - 6j]]

# 'perm' is more useful for n-dimensional tensors, for n > 2
x = tf.constant([[[ 1,  2,  3],
                  [ 4,  5,  6]],
                 [[ 7,  8,  9],
                  [10, 11, 12]]])

# Take the transpose of the matrices in dimension-0
# (this common operation has a shorthand `linalg.matrix_transpose`)
tf.transpose(x, perm=[0, 2, 1])  # [[[1,  4],
                                 #   [2,  5],
                                 #   [3,  6]],
                                 #  [[7, 10],
                                 #   [8, 11],
                                 #   [9, 12]]]

Numpy Compatibility

In numpy transposes are memory-efficient constant time operations as they simply return a new view of the same data with adjusted strides.

TensorFlow does not support strides, so transpose returns a new tensor with the items permuted.