本文整理汇总了Python中tensorflow.contrib.layers.real_valued_column方法的典型用法代码示例。如果您正苦于以下问题：Python layers.real_valued_column方法的具体用法？Python layers.real_valued_column怎么用？Python layers.real_valued_column使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.layers的用法示例。
在下文中一共展示了layers.real_valued_column方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _add_bias_column
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import real_valued_column [as 别名]
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] 示例2: _add_bias_column
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import real_valued_column [as 别名]
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] 示例3: _add_bias_column
# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import real_valued_column [as 别名]
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]