LostTech.TensorFlow : API Documentation

Type tf.estimator.inputs

Namespace tensorflow

Methods

Properties

Public static methods

object numpy_input_fn(IDictionary<string, object> x, ndarray y, int batch_size, Nullable<int> num_epochs, Nullable<bool> shuffle, int queue_capacity, int num_threads)

Returns input function that would feed dict of numpy arrays into the model.

This returns a function outputting `features` and `targets` based on the dict of numpy arrays. The dict `features` has the same keys as the `x`. The dict `targets` has the same keys as the `y` if `y` is a dict.

Example:
Parameters
IDictionary<string, object> x
numpy array object or dict of numpy array objects. If an array, the array will be treated as a single feature.
ndarray y
numpy array object or dict of numpy array object. `None` if absent.
int batch_size
Integer, size of batches to return.
Nullable<int> num_epochs
Integer, number of epochs to iterate over data. If `None` will run forever.
Nullable<bool> shuffle
Boolean, if True shuffles the queue. Avoid shuffle at prediction time.
int queue_capacity
Integer, size of queue to accumulate.
int num_threads
Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.
Returns
object
Function, that has signature of ()->(dict of `features`, `targets`)
Show Example 
age = np.arange(4) * 1.0
            height = np.arange(32, 36)
            x = {'age': age, 'height': height}
            y = np.arange(-32, -28) 
 with tf.Session() as session:
  input_fn = numpy_io.numpy_input_fn(
      x, y, batch_size=2, shuffle=False, num_epochs=1)

object numpy_input_fn_dyn(object x, object y, ImplicitContainer<T> batch_size, ImplicitContainer<T> num_epochs, object shuffle, ImplicitContainer<T> queue_capacity, ImplicitContainer<T> num_threads)

Returns input function that would feed dict of numpy arrays into the model.

This returns a function outputting `features` and `targets` based on the dict of numpy arrays. The dict `features` has the same keys as the `x`. The dict `targets` has the same keys as the `y` if `y` is a dict.

Example:
Parameters
object x
numpy array object or dict of numpy array objects. If an array, the array will be treated as a single feature.
object y
numpy array object or dict of numpy array object. `None` if absent.
ImplicitContainer<T> batch_size
Integer, size of batches to return.
ImplicitContainer<T> num_epochs
Integer, number of epochs to iterate over data. If `None` will run forever.
object shuffle
Boolean, if True shuffles the queue. Avoid shuffle at prediction time.
ImplicitContainer<T> queue_capacity
Integer, size of queue to accumulate.
ImplicitContainer<T> num_threads
Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.
Returns
object
Function, that has signature of ()->(dict of `features`, `targets`)
Show Example 
age = np.arange(4) * 1.0
            height = np.arange(32, 36)
            x = {'age': age, 'height': height}
            y = np.arange(-32, -28) 
 with tf.Session() as session:
  input_fn = numpy_io.numpy_input_fn(
      x, y, batch_size=2, shuffle=False, num_epochs=1)

object pandas_input_fn(object x, object y, int batch_size, Nullable<int> num_epochs, Nullable<bool> shuffle, int queue_capacity, int num_threads, IEnumerable<object> target_column)

Returns input function that would feed Pandas DataFrame into the model.

Note: `y`'s index must match `x`'s index.
Parameters
object x
pandas `DataFrame` object.
object y
pandas `Series` object or `DataFrame`. `None` if absent.
int batch_size
int, size of batches to return.
Nullable<int> num_epochs
int, number of epochs to iterate over data. If not `None`, read attempts that would exceed this value will raise `OutOfRangeError`.
Nullable<bool> shuffle
bool, whether to read the records in random order.
int queue_capacity
int, size of the read queue. If `None`, it will be set roughly to the size of `x`.
int num_threads
Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.
IEnumerable<object> target_column
str, name to give the target column `y`. This parameter is not used when `y` is a `DataFrame`.
Returns
object
Function, that has signature of ()->(dict of `features`, `target`)

object pandas_input_fn(object x, object y, int batch_size, Nullable<int> num_epochs, Nullable<bool> shuffle, int queue_capacity, int num_threads, string target_column)

Returns input function that would feed Pandas DataFrame into the model.

Note: `y`'s index must match `x`'s index.
Parameters
object x
pandas `DataFrame` object.
object y
pandas `Series` object or `DataFrame`. `None` if absent.
int batch_size
int, size of batches to return.
Nullable<int> num_epochs
int, number of epochs to iterate over data. If not `None`, read attempts that would exceed this value will raise `OutOfRangeError`.
Nullable<bool> shuffle
bool, whether to read the records in random order.
int queue_capacity
int, size of the read queue. If `None`, it will be set roughly to the size of `x`.
int num_threads
Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.
string target_column
str, name to give the target column `y`. This parameter is not used when `y` is a `DataFrame`.
Returns
object
Function, that has signature of ()->(dict of `features`, `target`)

object pandas_input_fn_dyn(object x, object y, ImplicitContainer<T> batch_size, ImplicitContainer<T> num_epochs, object shuffle, ImplicitContainer<T> queue_capacity, ImplicitContainer<T> num_threads, ImplicitContainer<T> target_column)

Returns input function that would feed Pandas DataFrame into the model.

Note: `y`'s index must match `x`'s index.
Parameters
object x
pandas `DataFrame` object.
object y
pandas `Series` object or `DataFrame`. `None` if absent.
ImplicitContainer<T> batch_size
int, size of batches to return.
ImplicitContainer<T> num_epochs
int, number of epochs to iterate over data. If not `None`, read attempts that would exceed this value will raise `OutOfRangeError`.
object shuffle
bool, whether to read the records in random order.
ImplicitContainer<T> queue_capacity
int, size of the read queue. If `None`, it will be set roughly to the size of `x`.
ImplicitContainer<T> num_threads
Integer, number of threads used for reading and enqueueing. In order to have predicted and repeatable order of reading and enqueueing, such as in prediction and evaluation mode, `num_threads` should be 1.
ImplicitContainer<T> target_column
str, name to give the target column `y`. This parameter is not used when `y` is a `DataFrame`.
Returns
object
Function, that has signature of ()->(dict of `features`, `target`)

Public properties

PythonFunctionContainer numpy_input_fn_fn get;

PythonFunctionContainer pandas_input_fn_fn get;