LostTech.TensorFlow : API Documentation

Type DNNClassifier

Namespace tensorflow.contrib.learn

Parent Estimator

Interfaces IDNNClassifier

A classifier for TensorFlow DNN models.

THIS CLASS IS DEPRECATED. See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) for general migration instructions.

Example: If the user specifies `label_keys` in constructor, labels must be strings from the `label_keys` vocabulary. Example: Input of `fit` and `evaluate` should have following features, otherwise there will be a `KeyError`:

* if `weight_column_name` is not `None`, a feature with `key=weight_column_name` whose value is a `Tensor`. * for each `column` in `feature_columns`: - if `column` is a `SparseColumn`, a feature with `key=column.name` whose `value` is a `SparseTensor`. - if `column` is a `WeightedSparseColumn`, two features: the first with `key` the id column name, the second with `key` the weight column name. Both features' `value` must be a `SparseTensor`. - if `column` is a `RealValuedColumn`, a feature with `key=column.name` whose `value` is a `Tensor`.
Show Example 
sparse_feature_a = sparse_column_with_hash_bucket(...)
            sparse_feature_b = sparse_column_with_hash_bucket(...) 
 sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a,
                                       ...)
sparse_feature_b_emb = embedding_column(sparse_id_column=sparse_feature_b,
                                       ...) 
 estimator = DNNClassifier(
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256]) 
 # Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = DNNClassifier(
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256],
    optimizer=tf.compat.v1.train.ProximalAdagradOptimizer(
      learning_rate=0.1,
      l1_regularization_strength=0.001
    )) 
 # Input builders
def input_fn_train: # returns x, y (where y represents label's class index).
  pass
estimator.fit(input_fn=input_fn_train) 
 def input_fn_eval: # returns x, y (where y represents label's class index).
  pass
estimator.evaluate(input_fn=input_fn_eval) 
 def input_fn_predict: # returns x, None
  pass
# predict_classes returns class indices.
estimator.predict_classes(input_fn=input_fn_predict)

Methods

Properties

Public instance methods

object predict(IDictionary<string, object> x, PythonFunctionContainer input_fn, int batch_size, IEnumerable<string> outputs, bool as_iterable)

Returns predictions for given features. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(as_iterable=False)`. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below.

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(outputs=None)`. They will be removed after 2017-03-01. Instructions for updating: Please switch to predict_classes, or set `outputs` argument.

By default, returns predicted classes. But this default will be dropped soon. Users should either pass `outputs`, or call `predict_classes` method.
Parameters
IDictionary<string, object> x
features.
PythonFunctionContainer input_fn
Input function. If set, x must be None.
int batch_size
Override default batch size.
IEnumerable<string> outputs
list of `str`, name of the output to predict. If `None`, returns classes.
bool as_iterable
If True, return an iterable which keeps yielding predictions for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure to pass num_epochs=1 if you are using something like read_batch_features).
Returns
object
Numpy array of predicted classes with shape [batch_size] (or an iterable of predicted classes if as_iterable is True). Each predicted class is represented by its class index (i.e. integer from 0 to n_classes-1). If `outputs` is set, returns a dict of predictions.

object predict(IDictionary<string, object> x, PythonFunctionContainer input_fn, IEnumerable<object> batch_size, IEnumerable<string> outputs, bool as_iterable)

Returns predictions for given features. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(as_iterable=False)`. They will be removed after 2016-09-15. Instructions for updating: The default behavior of predict() is changing. The default value for as_iterable will change to True, and then the flag will be removed altogether. The behavior of this flag is described below.

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: `(outputs=None)`. They will be removed after 2017-03-01. Instructions for updating: Please switch to predict_scores, or set `outputs` argument.

By default, returns predicted scores. But this default will be dropped soon. Users should either pass `outputs`, or call `predict_scores` method.
Parameters
IDictionary<string, object> x
features.
PythonFunctionContainer input_fn
Input function. If set, x must be None.
IEnumerable<object> batch_size
Override default batch size.
IEnumerable<string> outputs
list of `str`, name of the output to predict. If `None`, returns scores.
bool as_iterable
If True, return an iterable which keeps yielding predictions for each example until inputs are exhausted. Note: The inputs must terminate if you want the iterable to terminate (e.g. be sure to pass num_epochs=1 if you are using something like read_batch_features).
Returns
object
Numpy array of predicted scores (or an iterable of predicted scores if as_iterable is True). If `label_dimension == 1`, the shape of the output is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`. If `outputs` is set, returns a dict of predictions.

Public static methods

DNNClassifier NewDyn(object hidden_units, object feature_columns, object model_dir, ImplicitContainer<T> n_classes, object weight_column_name, object optimizer, ImplicitContainer<T> activation_fn, object dropout, object gradient_clip_norm, ImplicitContainer<T> enable_centered_bias, object config, object feature_engineering_fn, object embedding_lr_multipliers, object input_layer_min_slice_size, object label_keys)

Initializes a DNNClassifier instance.
Parameters
object hidden_units
List of hidden units per layer. All layers are fully connected. Ex. `[64, 32]` means first layer has 64 nodes and second one has 32.
object feature_columns
An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from `FeatureColumn`.
object model_dir
Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
ImplicitContainer<T> n_classes
number of label classes. Default is binary classification. It must be greater than 1. Note: Class labels are integers representing the class index (i.e. values from 0 to n_classes-1). For arbitrary label values (e.g. string labels), convert to class indices first.
object weight_column_name
A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
object optimizer
An instance of `tf.Optimizer` used to train the model. If `None`, will use an Adagrad optimizer.
ImplicitContainer<T> activation_fn
Activation function applied to each layer. If `None`, will use tf.nn.relu. Note that a string containing the unqualified name of the op may also be provided, e.g., "relu", "tanh", or "sigmoid".
object dropout
When not `None`, the probability we will drop out a given coordinate.
object gradient_clip_norm
A float > 0. If provided, gradients are clipped to their global norm with this clipping ratio. See tf.clip_by_global_norm for more details.
ImplicitContainer<T> enable_centered_bias
A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
object config
`RunConfig` object to configure the runtime settings.
object feature_engineering_fn
Feature engineering function. Takes features and labels which are the output of `input_fn` and returns features and labels which will be fed into the model.
object embedding_lr_multipliers
Optional. A dictionary from `EmbeddingColumn` to a `float` multiplier. Multiplier will be used to multiply with learning rate for the embedding variables.
object input_layer_min_slice_size
Optional. The min slice size of input layer partitions. If not provided, will use the default of 64M.
object label_keys
Optional list of strings with size `[n_classes]` defining the label vocabulary. Only supported for `n_classes` > 2.
Returns
DNNClassifier
A `DNNClassifier` estimator.

Public properties

object config get;

object config_dyn get;

string model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object PythonObject get;