Python tensorflow.contrib.layers.real_valued_column() Examples

The following are 5 code examples of tensorflow.contrib.layers.real_valued_column(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.contrib.layers , or try the search function .
Example #1
Source File: sdca_estimator.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones(
      [batch_size, 1], dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]
Example #2
Source File: linear.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones([batch_size, 1],
                                                   dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]
Example #3
Source File: linear.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones([batch_size, 1],
                                                   dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]
Example #4
Source File: linear.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     labels, columns_to_variables):
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones_like(labels,
                                                        dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]
Example #5
Source File: linear.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
                     columns_to_variables):
  """Adds a fake bias feature column filled with all 1s."""
  # TODO(b/31008490): Move definition to a common constants place.
  bias_column_name = "tf_virtual_bias_column"
  if any(col.name is bias_column_name for col in feature_columns):
    raise ValueError("%s is a reserved column name." % bias_column_name)
  if not feature_columns:
    raise ValueError("feature_columns can't be empty.")

  # Loop through input tensors until we can figure out batch_size.
  batch_size = None
  for column in columns_to_tensors.values():
    if isinstance(column, tuple):
      column = column[0]
    if isinstance(column, sparse_tensor.SparseTensor):
      shape = tensor_util.constant_value(column.dense_shape)
      if shape is not None:
        batch_size = shape[0]
        break
    else:
      batch_size = array_ops.shape(column)[0]
      break
  if batch_size is None:
    raise ValueError("Could not infer batch size from input features.")

  bias_column = layers.real_valued_column(bias_column_name)
  columns_to_tensors[bias_column] = array_ops.ones([batch_size, 1],
                                                   dtype=dtypes.float32)
  columns_to_variables[bias_column] = [bias_variable]