def one_hot_encoding(labels,
num_classes,
on_value=1.0,
off_value=0.0,
outputs_collections=None,
scope=None):
"""Transform numeric labels into onehot_labels using tf.one_hot.
Args:
labels: [batch_size] target labels.
num_classes: total number of classes.
on_value: A scalar defining the on-value.
off_value: A scalar defining the off-value.
outputs_collections: collection to add the outputs.
scope: Optional scope for op_scope.
Returns:
one hot encoding of the labels.
"""
with ops.op_scope([labels, num_classes], scope, 'OneHotEncoding') as sc:
if labels.dtype == dtypes.int32:
labels = standard_ops.to_int64(labels)
outputs = standard_ops.one_hot(labels,
num_classes,
on_value=on_value,
off_value=off_value)
return utils.collect_named_outputs(outputs_collections, sc, outputs)
def one_hot_encoding(target, n_classes, on_value=1.0, off_value=0.0,
name="OneHotEncoding"):
""" One Hot Encoding.
Transform numeric labels into a binary vector.
Input:
The Labels Placeholder.
Output:
2-D Tensor, The encoded labels.
Arguments:
target: `Placeholder`. The labels placeholder.
n_classes: `int`. Total number of classes.
on_value: `scalar`. A scalar defining the on-value.
off_value: `scalar`. A scalar defining the off-value.
name: A name for this layer (optional). Default: 'OneHotEncoding'.
"""
with tf.name_scope(name):
if target.dtype != dtypes.int64:
target = standard_ops.to_int64(target)
target = standard_ops.one_hot(target, n_classes,
on_value=on_value,
off_value=off_value)
# Track output tensor.
tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, target)
return target
def one_hot_encoding(target, n_classes, on_value=1.0, off_value=1.0,
name="OneHotEncoding"):
""" One Hot Encoding.
Transform numeric labels into a binary vector.
Input:
The Labels Placeholder.
Output:
2-D Tensor, The encoded labels.
Arguments:
target: `Placeholder`. The labels placeholder.
n_classes: `int`. Total number of classes.
on_value: `scalar`. A scalar defining the on-value.
off_value: `scalar`. A scalar defining the off-value.
name: A name for this layer (optional). Default: 'OneHotEncoding'.
"""
with tf.name_scope(name):
if target.dtype == tf.dtypes.int32:
target = standard_ops.to_int64(target)
target = standard_ops.one_hot(target, n_classes,
on_value=on_value,
off_value=off_value)
# Track output tensor.
tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, target)
return target
def _build(self, incoming, *args, **kwargs):
"""
Args:
incoming: The Labels Placeholder.
Returns:
2-D Tensor, The encoded labels.
"""
if incoming.dtype != dtypes.int64:
incoming = standard_ops.to_int64(incoming)
incoming = standard_ops.one_hot(indices=incoming, depth=self.n_classes,
on_value=self.on_value, off_value=self.off_value)
track(incoming, tf.GraphKeys.LAYER_TENSOR, self.module_name)
return incoming
