diff options
Diffstat (limited to 'tensorflow/python/ops/ctc_ops.py')
| -rw-r--r-- | tensorflow/python/ops/ctc_ops.py | 30 |
1 files changed, 13 insertions, 17 deletions
diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py index f037767cf4..477c0d1cb4 100644 --- a/tensorflow/python/ops/ctc_ops.py +++ b/tensorflow/python/ops/ctc_ops.py @@ -22,8 +22,8 @@ from __future__ import print_function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_ctc_ops +from tensorflow.python.ops import array_ops from tensorflow.python.ops.nn_grad import _BroadcastMul @@ -38,8 +38,7 @@ def ctc_loss(labels, inputs, sequence_length, [A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber. Connectionist Temporal Classification: Labeling Unsegmented Sequence Data - with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, - pp. 369-376.](http://www.cs.toronto.edu/~graves/icml_2006.pdf) + with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, pp. 369-376.](http://www.cs.toronto.edu/~graves/icml_2006.pdf) Input requirements: @@ -109,9 +108,9 @@ def ctc_loss(labels, inputs, sequence_length, See `core/ops/ctc_ops.cc` for more details. inputs: 3-D `float` `Tensor`. If time_major == False, this will be a `Tensor` shaped: - `[batch_size, max_time, num_classes]`. + `[batch_size x max_time x num_classes]`. If time_major == True (default), this will be a `Tensor` shaped: - `[max_time, batch_size, num_classes]`. + `[max_time x batch_size x num_classes]`. The logits. sequence_length: 1-D `int32` vector, size `[batch_size]`. The sequence lengths. @@ -121,18 +120,15 @@ def ctc_loss(labels, inputs, sequence_length, ignore_longer_outputs_than_inputs: Boolean. Default: False. If True, sequences with longer outputs than inputs will be ignored. time_major: The shape format of the `inputs` Tensors. - If True, these `Tensors` must be shaped `[max_time, batch_size, - num_classes]`. - If False, these `Tensors` must be shaped `[batch_size, max_time, - num_classes]`. - Using `time_major = True` (default) is a bit more efficient because it - avoids transposes at the beginning of the ctc_loss calculation. However, - most TensorFlow data is batch-major, so by this function also accepts - inputs in batch-major form. + If True, these `Tensors` must be shaped `[max_time, batch_size, num_classes]`. + If False, these `Tensors` must be shaped `[batch_size, max_time, num_classes]`. + Using `time_major = True` (default) is a bit more efficient because it avoids + transposes at the beginning of the ctc_loss calculation. However, most + TensorFlow data is batch-major, so by this function also accepts inputs + in batch-major form. Returns: - A 1-D `float` `Tensor`, size `[batch]`, containing the negative log - probabilities. + A 1-D `float` `Tensor`, size `[batch]`, containing the negative log probabilities. Raises: TypeError: if labels is not a `SparseTensor`. @@ -202,7 +198,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True): Args: inputs: 3-D `float` `Tensor` sized - `[max_time, batch_size, num_classes]`. The logits. + `[max_time x batch_size x num_classes]`. The logits. sequence_length: 1-D `int32` vector containing sequence lengths, having size `[batch_size]`. merge_repeated: Boolean. Default: True. @@ -211,7 +207,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True): A tuple `(decoded, neg_sum_logits)` where decoded: A single-element list. `decoded[0]` is an `SparseTensor` containing the decoded outputs s.t.: - `decoded.indices`: Indices matrix `(total_decoded_outputs, 2)`. + `decoded.indices`: Indices matrix `(total_decoded_outputs x 2)`. The rows store: `[batch, time]`. `decoded.values`: Values vector, size `(total_decoded_outputs)`. The vector stores the decoded classes. |