add_n_broadcast(tensors[, name]) |
Add zero or many tensors with broadcasting. |
assert_rank(x, ndims[, message, name]) |
Assert the rank of x is ndims. |
assert_rank_at_least(x, ndims[, message, name]) |
Assert the rank of x is at least ndims. |
assert_scalar_equal(a, b[, message, name]) |
Assert 0-d scalar a == b. |
assert_shape_equal(x, y[, message, name]) |
Assert the shape of x equals to y. |
bits_per_dimension(log_p, value_size[, …]) |
Compute “bits per dimension” of x. |
classification_accuracy(y_pred, y_true[, name]) |
Compute the classification accuracy for y_pred and y_true. |
depth_to_space(input, block_size[, …]) |
Wraps tf.depth_to_space(), to support tensors higher than 4-d. |
log_mean_exp(x[, axis, keepdims, name]) |
Compute \(\log \frac{1}{K} \sum_{k=1}^K \exp(x_k)\). |
log_sum_exp(x[, axis, keepdims, name]) |
Compute \(\log \sum_{k=1}^K \exp(x_k)\). |
smart_cond(cond, true_fn, false_fn[, name]) |
Execute true_fn or false_fn according to cond. |
softmax_classification_output(logits[, name]) |
Get the most possible softmax classification output for each logit. |
space_to_depth(input, block_size[, …]) |
Wraps tf.space_to_depth(), to support tensors higher than 4-d. |