def sequence_numeric_column(
key,
shape=(1,),
default_value=0.,
dtype=dtypes.float32,
normalizer_fn=None):
"""Returns a feature column that represents sequences of numeric data.
Example:
```python
temperature = sequence_numeric_column('temperature')
columns = [temperature]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatureLayer(columns)
input_layer, sequence_length = sequence_feature_layer(features)
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)
```
Args:
key: A unique string identifying the input features.
shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
each example must contain `2 * sequence_length` values.
default_value: A single value compatible with `dtype` that is used for
padding the sparse data into a dense `Tensor`.
dtype: The type of values.
normalizer_fn: If not `None`, a function that can be used to normalize the
value of the tensor after `default_value` is applied for parsing.
Normalizer function takes the input `Tensor` as its argument, and returns
the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
even though the most common use case of this function is normalization, it
can be used for any kind of Tensorflow transformations.
Returns:
A `SequenceNumericColumn`.
Raises:
TypeError: if any dimension in shape is not an int.
ValueError: if any dimension in shape is not a positive integer.
ValueError: if `dtype` is not convertible to `tf.float32`.
"""
shape = fc_old._check_shape(shape=shape, key=key)
if not (dtype.is_integer or dtype.is_floating):
raise ValueError('dtype must be convertible to float. '
'dtype: {}, key: {}'.format(dtype, key))
if normalizer_fn is not None and not callable(normalizer_fn):
raise TypeError(
'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn))
return SequenceNumericColumn(
key,
shape=shape,
default_value=default_value,
dtype=dtype,
normalizer_fn=normalizer_fn)
def sequence_numeric_column(key,
shape=(1, ),
default_value=0.,
dtype=dtypes.float32,
normalizer_fn=None):
"""Returns a feature column that represents sequences of numeric data.
Example:
```python
temperature = sequence_numeric_column('temperature')
columns = [temperature]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatureLayer(columns)
input_layer, sequence_length = sequence_feature_layer(features)
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)
```
Args:
key: A unique string identifying the input features.
shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
each example must contain `2 * sequence_length` values.
default_value: A single value compatible with `dtype` that is used for
padding the sparse data into a dense `Tensor`.
dtype: The type of values.
normalizer_fn: If not `None`, a function that can be used to normalize the
value of the tensor after `default_value` is applied for parsing.
Normalizer function takes the input `Tensor` as its argument, and returns
the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
even though the most common use case of this function is normalization, it
can be used for any kind of Tensorflow transformations.
Returns:
A `SequenceNumericColumn`.
Raises:
TypeError: if any dimension in shape is not an int.
ValueError: if any dimension in shape is not a positive integer.
ValueError: if `dtype` is not convertible to `tf.float32`.
"""
shape = fc_old._check_shape(shape=shape, key=key)
if not (dtype.is_integer or dtype.is_floating):
raise ValueError('dtype must be convertible to float. '
'dtype: {}, key: {}'.format(dtype, key))
if normalizer_fn is not None and not callable(normalizer_fn):
raise TypeError('normalizer_fn must be a callable. Given: {}'.format(
normalizer_fn))
return SequenceNumericColumn(key,
shape=shape,
default_value=default_value,
dtype=dtype,
normalizer_fn=normalizer_fn)
def sequence_numeric_column(
key,
shape=(1,),
default_value=0.,
dtype=dtypes.float32):
"""Returns a feature column that represents sequences of numeric data.
Example:
```python
temperature = sequence_numeric_column('temperature')
columns = [temperature]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)
```
Args:
key: A unique string identifying the input features.
shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
each example must contain `2 * sequence_length` values.
default_value: A single value compatible with `dtype` that is used for
padding the sparse data into a dense `Tensor`.
dtype: The type of values.
Returns:
A `_SequenceNumericColumn`.
Raises:
TypeError: if any dimension in shape is not an int.
ValueError: if any dimension in shape is not a positive integer.
ValueError: if `dtype` is not convertible to `tf.float32`.
"""
shape = fc._check_shape(shape=shape, key=key)
if not (dtype.is_integer or dtype.is_floating):
raise ValueError('dtype must be convertible to float. '
'dtype: {}, key: {}'.format(dtype, key))
return _SequenceNumericColumn(
key,
shape=shape,
default_value=default_value,
dtype=dtype)
def sequence_numeric_column(
key,
shape=(1,),
default_value=0.,
dtype=dtypes.float32):
"""Returns a feature column that represents sequences of numeric data.
Example:
```python
temperature = sequence_numeric_column('temperature')
columns = [temperature]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
rnn_cell, inputs=input_layer, sequence_length=sequence_length)
```
Args:
key: A unique string identifying the input features.
shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
each example must contain `2 * sequence_length` values.
default_value: A single value compatible with `dtype` that is used for
padding the sparse data into a dense `Tensor`.
dtype: The type of values.
Returns:
A `_SequenceNumericColumn`.
Raises:
TypeError: if any dimension in shape is not an int.
ValueError: if any dimension in shape is not a positive integer.
ValueError: if `dtype` is not convertible to `tf.float32`.
"""
shape = fc._check_shape(shape=shape, key=key)
if not (dtype.is_integer or dtype.is_floating):
raise ValueError('dtype must be convertible to float. '
'dtype: {}, key: {}'.format(dtype, key))
return _SequenceNumericColumn(
key,
shape=shape,
default_value=default_value,
dtype=dtype)
def fixed_len_sequence_numeric_column(key,
shape=(1, ),
default_value=0.,
dtype=dtypes.float32,
normalizer_fn=None):
shape = fc._check_shape(shape=shape, key=key)
if not (dtype.is_integer or dtype.is_floating):
raise ValueError('dtype must be convertible to float. '
'dtype: {}, key: {}'.format(dtype, key))
if normalizer_fn is not None and not callable(normalizer_fn):
raise TypeError('normalizer_fn must be a callable. Given: {}'.format(
normalizer_fn))
return _FixedLenSequenceNumericColumn(key,
shape=shape,
default_value=default_value,
dtype=dtype,
normalizer_fn=normalizer_fn)