Advanced Tensor slicing

Utilities for advanced tensor slicing and batching operations.

Reference

tensorbank.tf.slices.slice_within_stride(x, stride, si=0, ei=None, keepdims=True)[source]

Select x[..., (i * stride + si):(i * stride + ei)] for each i.

The tensor returned will have the last dimension shrunk by a factor of (ei-si)/stride.

As a natural special case, tb.multiple_within_stride(x, N) is equivalent to adding a dimension of N at the end, as in tf.expand_dims(x, (..., -1, N)).

Example

When predicting anchor positions in SSD, num_classes + num_offsets are predicted for each anchor. To get only the class confidence, this would be used:

logits = model(input)
class_logits = tb.slice_within_stride(
    logits,
    0,
    num_classes,
    num_classes + num_offsets)
loss = softmax_cross_entropy_with_logits(
    class_preds, class_logits)
Parameters

  • x (tf.Tensor) – value to modify

  • stride (int) – stride for the last dimension

  • si (int) – starting index within stride. Negative indices are supported. Defaults to 0.

  • ei (int) – end index (1 element after the last) within stride. Negative indices are supported. Defaults to None, which means “until the last element”.

  • keepdims (bool) – if False, adds another dimension that iterates over each stride. This dimension will be of size ei-si. Defaults to True.

Returns

modified x with the last dimension sliced.

Return type

tf.Tensor