tf.dynamic_stitch

Interleave the values from the data tensors into a single tensor.

tf.dynamic_stitch(
    indices, data, name=None
)

Builds a merged tensor such that

    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]

For example, if each indices[m] is scalar or vector, we have

    # Scalar indices:
    merged[indices[m], ...] = data[m][...]

    # Vector indices:
    merged[indices[m][i], ...] = data[m][i, ...]

Each data[i].shape must start with the corresponding indices[i].shape, and the rest of data[i].shape must be constant w.r.t. i. That is, we must have data[i].shape = indices[i].shape + constant. In terms of this constant, the output shape is

merged.shape = [max(indices)] + constant

Values are merged in order, so if an index appears in both indices[m][i] and indices[n][j] for (m,i) < (n,j) the slice data[n][j] will appear in the merged result. If you do not need this guarantee, ParallelDynamicStitch might perform better on some devices.

For example:

    indices[0] = 6
    indices[1] = [4, 1]
    indices[2] = [[5, 2], [0, 3]]
    data[0] = [61, 62]
    data[1] = [[41, 42], [11, 12]]
    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
              [51, 52], [61, 62]]

This method can be used to merge partitions created by dynamic_partition as illustrated on the following example:

    # Apply function (increments x_i) on elements for which a certain condition
    # apply (x_i != -1 in this example).
    x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])
    condition_mask=tf.not_equal(x,tf.constant(-1.))
    partitioned_data = tf.dynamic_partition(
        x, tf.cast(condition_mask, tf.int32) , 2)
    partitioned_data[1] = partitioned_data[1] + 1.0
    condition_indices = tf.dynamic_partition(
        tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)
    x = tf.dynamic_stitch(condition_indices, partitioned_data)
    # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
    # unchanged.