def ctc_loss(inputs,
labels,
sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
Requires:
```sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.```
If ctc_merge_repeated is set False, then *during* CTC calculation
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified version of CTC.
Args:
inputs: 3-D `float` `Tensor` sized
`[max_time x batch_size x num_classes]`. The logits.
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t). See `core/ops/ctc_ops.cc` for more details.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing logits.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, ops.SparseTensor):
raise TypeError("Expected labels to be a SparseTensor")
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated)
return loss
def ctc_loss(inputs, labels, sequence_length,
preprocess_collapse_repeated=False, ctc_merge_repeated=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
Requires:
```sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.```
If ctc_merge_repeated is set False, then *during* CTC calculation
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified version of CTC.
Args:
inputs: 3-D `float` `Tensor` sized
`[max_time x batch_size x num_classes]`. The logits.
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t). See `core/ops/ctc_ops.cc` for more details.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing logits.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, ops.SparseTensor):
raise TypeError("Expected labels to be a SparseTensor")
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated)
return loss
def ctc_loss(labels, inputs, sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True, time_major=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
This op implements the CTC loss as presented in the article:
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, pp. 369-376.
http://www.cs.toronto.edu/~graves/icml_2006.pdf
Input requirements:
```
sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.
```
Notes:
This class performs the softmax operation for you, so inputs should
be e.g. linear projections of outputs by an LSTM.
The `inputs` Tensor's innermost dimension size, `num_classes`, represents
`num_labels + 1` classes, where num_labels is the number of true labels, and
the largest value `(num_classes - 1)` is reserved for the blank label.
For example, for a vocabulary containing 3 labels `[a, b, c]`,
`num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.
Regarding the arguments `preprocess_collapse_repeated` and
`ctc_merge_repeated`:
If `preprocess_collapse_repeated` is True, then a preprocessing step runs
before loss calculation, wherein repeated labels passed to the loss
are merged into single labels. This is useful if the training labels come
from, e.g., forced alignments and therefore have unnecessary repetitions.
If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified (non-standard) version of CTC.
Here is a table of the (roughly) expected first order behavior:
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`
Classical CTC behavior: Outputs true repeated classes with blanks in
between, and can also output repeated classes with no blanks in
between that need to be collapsed by the decoder.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=False`
Never learns to output repeated classes, as they are collapsed
in the input labels before training.
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=False`
Outputs repeated classes with blanks in between, but generally does not
require the decoder to collapse/merge repeated classes.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=True`
Untested. Very likely will not learn to output repeated classes.
Args:
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t).
`labels.values[i]` must take on values in `[0, num_labels)`.
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 x max_time x num_classes]`.
If time_major == True (default), this will be a `Tensor` shaped:
`[max_time x batch_size x num_classes]`.
The logits.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
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.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing the negative log probabilities.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, sparse_tensor.SparseTensor):
raise TypeError("Expected labels (first argument) to be a SparseTensor")
# For internal calculations, we transpose to [time, batch, num_classes]
if not time_major:
inputs = array_ops.transpose(inputs, [1, 0, 2]) # (B,T,N) => (T,B,N)
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated)
return loss
def ctc_loss(inputs,
labels,
sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True,
time_major=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
This op implements the CTC loss as presented in the article:
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, pp. 369-376.
http://www.cs.toronto.edu/~graves/icml_2006.pdf
Input requirements:
```
sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.
```
Notes:
This class performs the softmax operation for you, so inputs should
be e.g. linear projections of outputs by an LSTM.
The `inputs` Tensor's innermost dimension size, `num_classes`, represents
`num_labels + 1` classes, where num_labels is the number of true labels, and
the largest value `(num_classes - 1)` is reserved for the blank label.
For example, for a vocabulary containing 3 labels `[a, b, c]`,
`num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.
Regarding the arguments `preprocess_collapse_repeated` and
`ctc_merge_repeated`:
If `preprocess_collapse_repeated` is True, then a preprocessing step runs
before loss calculation, wherein repeated labels passed to the loss
are merged into single labels. This is useful if the training labels come
from, e.g., forced alignments and therefore have unnecessary repetitions.
If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified (non-standard) version of CTC.
Here is a table of the (roughly) expected first order behavior:
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`
Classical CTC behavior: Outputs true repeated classes with blanks in
between, and can also output repeated classes with no blanks in
between that need to be collapsed by the decoder.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=False`
Never learns to output repeated classes, as they are collapsed
in the input labels before training.
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=False`
Outputs repeated classes with blanks in between, but generally does not
require the decoder to collapse/merge repeated classes.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=True`
Untested. Very likely will not learn to output repeated classes.
Args:
inputs: 3-D `float` `Tensor`.
If time_major == False, this will be a `Tensor` shaped:
`[batch_size x max_time x num_classes]`.
If time_major == True (default), this will be a `Tensor` shaped:
`[max_time x batch_size x num_classes]`.
The logits.
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t).
`labels.values[i]` must take on values in `[0, num_labels)`.
See `core/ops/ctc_ops.cc` for more details.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
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.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing the negative log probabilities.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, ops.SparseTensor):
raise TypeError("Expected labels to be a SparseTensor")
# For internal calculations, we transpose to [time, batch, num_classes]
if not time_major:
inputs = array_ops.transpose(inputs, [1, 0, 2]) # (B,T,N) => (T,B,N)
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated)
return loss
def ctc_loss(inputs,
labels,
sequence_length,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
This op implements the CTC loss as presented in the article:
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, pp. 369-376.
http://www.cs.toronto.edu/~graves/icml_2006.pdf
Input requirements:
```
sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.
```
Regarding the arguments `preprocess_collapse_repeated` and
`ctc_merge_repeated`:
If `preprocess_collapse_repeated` is True, then a preprocessing step runs
before loss calculation, wherein repeated labels passed to the loss
are merged into single labels. This is useful if the training labels come
from, e.g., forced alignments and therefore have unnecessary repetitions.
If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified (non-standard) version of CTC.
Here is a table of the (roughly) expected first order behavior:
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`
Classical CTC behavior: Outputs true repeated classes with blanks in
between, and can also output repeated classes with no blanks in
between that need to be collapsed by the decoder.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=False`
Never learns to output repeated classes, as they are collapsed
in the input labels before training.
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=False`
Outputs repeated classes with blanks in between, but generally does not
require the decoder to collapse/merge repeated classes.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=True`
Untested. Very likely will not learn to output repeated classes.
Args:
inputs: 3-D `float` `Tensor` sized
`[max_time x batch_size x num_classes]`. The logits.
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t). See `core/ops/ctc_ops.cc` for more details.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing the negative log probabilities.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, ops.SparseTensor):
raise TypeError("Expected labels to be a SparseTensor")
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated)
return loss
def ctc_loss(inputs, labels, sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=True):
"""Computes the CTC (Connectionist Temporal Classification) Loss.
This op implements the CTC loss as presented in the article:
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA, pp. 369-376.
http://www.cs.toronto.edu/~graves/icml_2006.pdf
Input requirements:
```
sequence_length(b) <= time for all b
max(labels.indices(labels.indices[:, 1] == b, 2))
<= sequence_length(b) for all b.
```
Regarding the arguments `preprocess_collapse_repeated` and
`ctc_merge_repeated`:
If `preprocess_collapse_repeated` is True, then a preprocessing step runs
before loss calculation, wherein repeated labels passed to the loss
are merged into single labels. This is useful if the training labels come
from, e.g., forced alignments and therefore have unnecessary repetitions.
If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
repeated non-blank labels will not be merged and are interpreted
as individual labels. This is a simplified (non-standard) version of CTC.
Here is a table of the (roughly) expected first order behavior:
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`
Classical CTC behavior: Outputs true repeated classes with blanks in
between, and can also output repeated classes with no blanks in
between that need to be collapsed by the decoder.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=False`
Never learns to output repeated classes, as they are collapsed
in the input labels before training.
* `preprocess_collapse_repeated=False`, `ctc_merge_repeated=False`
Outputs repeated classes with blanks in between, but generally does not
require the decoder to collapse/merge repeated classes.
* `preprocess_collapse_repeated=True`, `ctc_merge_repeated=True`
Untested. Very likely will not learn to output repeated classes.
Args:
inputs: 3-D `float` `Tensor` sized
`[max_time x batch_size x num_classes]`. The logits.
labels: An `int32` `SparseTensor`.
`labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
the id for (batch b, time t). See `core/ops/ctc_ops.cc` for more details.
sequence_length: 1-D `int32` vector, size `[batch_size]`.
The sequence lengths.
preprocess_collapse_repeated: Boolean. Default: False.
If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated: Boolean. Default: True.
Returns:
A 1-D `float` `Tensor`, size `[batch]`, containing logits.
Raises:
TypeError: if labels is not a `SparseTensor`.
"""
# The second, third, etc output tensors contain the gradients. We use it in
# _CTCLossGrad() below.
if not isinstance(labels, ops.SparseTensor):
raise TypeError("Expected labels to be a SparseTensor")
loss, _ = gen_ctc_ops._ctc_loss(
inputs,
labels.indices,
labels.values,
sequence_length,
preprocess_collapse_repeated=preprocess_collapse_repeated,
ctc_merge_repeated=ctc_merge_repeated,
)
return loss